phd computer science berkeley

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The School of Information is UC Berkeley’s newest professional school. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.

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The School of Information offers four degrees:

The Master of Information Management and Systems (MIMS) program educates information professionals to provide leadership for an information-driven world.

The Master of Information and Data Science (MIDS) is an online degree preparing data science professionals to solve real-world problems. The 5th Year MIDS program is a streamlined path to a MIDS degree for Cal undergraduates.

The Master of Information and Cybersecurity (MICS) is an online degree preparing cybersecurity leaders for complex cybersecurity challenges.

Our Ph.D. in Information Science is a research program for next-generation scholars of the information age.

• Spring 2024 Course Schedule
• Summer 2024 Course Schedule
• Fall 2024 Course Schedule

The School of Information's courses bridge the disciplines of information and computer science, design, social sciences, management, law, and policy. We welcome interest in our graduate-level Information classes from current UC Berkeley graduate and undergraduate students and community members.  More information about signing up for classes.

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• Computer-mediated Communication
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• Human-computer Interaction (HCI)
• Information Economics
• Information Organization
• Information Policy
• Information Retrieval & Search
• Information Visualization
• Social & Cultural Studies
• Technology for Developing Regions
• User Experience Research

Research by faculty members and doctoral students keeps the I School on the vanguard of contemporary information needs and solutions.

The I School is also home to several active centers and labs, including the Center for Long-Term Cybersecurity (CLTC) , the Center for Technology, Society & Policy , and the BioSENSE Lab .

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I School graduate students and alumni have expertise in data science, user experience design & research, product management, engineering, information policy, cybersecurity, and more — learn more about hiring I School students and alumni .

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• I School Voices

On the March 27th episode of PBS’s documentary series Nova titled “A.I. Revolution,” correspondent Miles O’Brien...

A group of scholars from the School of Information are tackling the issue of illegal sand mining with the help of a...

When the Bancroft Library received over 100,000 Japanese-American internment “individual record” forms (WRA-26) from...

The Goldman School of Public Policy, the CITRIS Policy Lab, and the School of Information hosted the inaugural UC...

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Ph.D. in Information Science

Ph.d. community.

Ph.D. students are knowledge architects and respected contributors to our information society, with a vision of expanding access to quality information, an appreciation for diverse perspectives, and the spirit of collaboration.

You Belong at Berkeley

The I School is a welcoming community of students, faculty, and staff from a wide variety of backgrounds, nations, cultures, and experiences.

The doctoral program is a research-oriented program in which the student chooses specific fields of specialization, prepares sufficiently in the literature and the research of those fields to pass a qualifying examination, and completes original research culminating in the written dissertation. The degree of Doctor of Philosophy is conferred in recognition of a candidate's grasp of a broad field of learning and distinguished accomplishment in that field through contribution of an original piece of research revealing high critical ability and powers of imagination and synthesis.

The Ideal Place for Breakthrough Thinking

School of Information offers an ideal environment for information scholars , on the campus of a preeminent, forward-thinking research institution .

Dedicated to cross-disciplinary research, breakthrough thinking, and creative collaboration, the I School actively shapes the information frontier and has a track record of scholarly ideas, solutions, and policy counsel that make information more accessible, manageable, and useful.

Rigorous academics instill the theoretical and research capabilities required to advance diverse interests — from information design, architecture, and assurance, to human-computer interaction and the social, economic, and public policy implications of information. Ph.D. students work closely with faculty recognized as information pioneers.

Interdisciplinary thinking and partnership are central to the I School approach, so doctoral research often engages exceptional UC Berkeley schools and departments beyond the I School, from journalism, business, and law to computing, engineering, humanities, and social sciences.

On average, I School students complete the Ph.D. degree in 6 years.

• Semester 1–4 : Breadth, major, & minor coursework
• Semester 4–5 : Prelim research paper & exam
• Semester 6–8 : Qualifying exam
• Semester 10–12 : Complete & present dissertation

Detailed degree requirements & timeline

Areas of Study

Major and minor areas include:

• Human-Computer Interaction
• Information Economics and Policy
• Information Law and Policy
• Information Organization and Retrieval
• Information Systems Design
• Social Aspects of Information
• Information and Communication Technologies and Development

Your Career

I School Ph.D. graduates go on to careers in academia, industry, or the public sector.

Recent Ph.D. graduates hold tenure-track faculty positions at the world’s leading universities, as well as leading research positions in industry, academia, and public-interest organizations.

More about Ph.D. career outcomes

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“I think we can do a better job of using the Internet to tap into how people are doing, how they’re feeling, and what matters to them — online democracy, in a way, but minus the hype.”

—Galen Panger Ph.D. 2017

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New Research Aims to Curb Illegal Sand Mining with Data-Driven Mapping Tools

I School Representatives Lead DEIBJ Initiatives

Human-Computer Interaction Research De-Centers Humans to Give Nature a Voice

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CS Faculty List

Pieter Abbeel

Professor 746 Sutardja Dai Hall, (510) 642-7034; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) Education: 2008, Ph.D., Computer Science, Stanford University; 2000, M.S., Electrical Engineering, KU Leuven, Belgium Office Hours: arrange via email Teaching Schedule (Spring 2024): CS 294-158. Deep Unsupervised Learning , Th 14:00-16:59, Sutardja Dai 250 Teaching Schedule (Fall 2024): CS 188. Introduction to Artificial Intelligence , TuTh 15:30-16:59, Dwinelle 155

Below The Line Assistant Professor [email protected] Education: 2020, PhD, EECS, UCLA

Cameron Allen

Lecturer [email protected] Research Interests: Artificial Intelligence (AI) Education: 2023, Ph. D., Computer Science, Brown University; 2018, M.S., Computer Science, Brown University; 2011, B.S., Electrical Engineering, Tufts University Teaching Schedule (Spring 2024): CS 188. Introduction to Artificial Intelligence , TuTh 12:30-13:59, Wheeler 150

Krste Asanović

Professor Emeritus, Professor in the Graduate School 579B Soda Hall, 510-642-6506; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Integrated Circuits (INC) ; Operating Systems & Networking (OSNT) ; Design, Modeling and Analysis (DMA) Education: 1998, PhD, Computer Science, UC Berkeley; 1987, BA, Electrical and Information Sciences, University of Cambridge, UK Office Hours: Email for appt Assistants: Tammy Johnson, 565 Soda, 643-4816, [email protected]; Ria Melendres Briggs, 563 Soda, (510) 643-1455, [email protected]

Babak Ayazifar

Teaching Professor 517 Cory Hall, 510-642-9945; [email protected] Research Interests: Education (EDUC) ; Signal Processing (SP) Education: 2003, Ph.D., Electrical Engineering and Computer Science, Massachusetts Institute of Technology; 1989, B.S., Electrical Engineering, Caltech Teaching Schedule (Spring 2024): EECS 16A. Designing Information Devices and Systems I , MoWe 18:30-19:59, Pimentel 1 Teaching Schedule (Fall 2024): EECS 16A. Designing Information Devices and Systems I , MoWe 18:30-19:59, Pimentel 1

Ruzena Bajcsy

Professor Emerita 719 Sutardja Dai Hall, 510-642-9423; [email protected] Research Interests: Artificial Intelligence (AI) ; Biosystems & Computational Biology (BIO) ; Control, Intelligent Systems, and Robotics (CIR) ; Graphics (GR) ; Human-Computer Interaction (HCI) ; Security (SEC) Education: 1972, Ph.D., Computer Science, Stanford University; 1968, Ph.D., Electrical Engineering, Slovak Technical University, Bratislava, Slovak Republic; 1957, M.S., Electrical Engineering, Slovak Technical University, Bratislava, Slovak Republic Office Hours: M W 9-10, 719 Sutardja Dai

Michael Ball

Lecturer 784 Soda Hall; [email protected] Research Interests: Education (EDUC) ; Human-Computer Interaction (HCI) Education: 2016, MS, Computer Science, UC Berkeley; 2015, BA, Computer Science, UC Berkeley Office Hours: By appointment, please email me., 784 Soda Teaching Schedule (Spring 2024): CS 169L. Software Engineering Team Project , MoFr 10:30-11:59, Soda 405 Teaching Schedule (Fall 2024): CS 169A. Introduction to Software Engineering , MoWe 17:00-18:29, Genetics & Plant Bio 100

David Bamman

Below The Line Associate Professor [email protected] Education: 2015, Ph.D., Computer Science (Language Technologies Institute), Carnegie Mellon University; 2006, M.A., Applied Linguistics, Boston University; 1998, B.A., Classics, University of Wisconsin-Madison

Brian A. Barsky

Professor Emeritus, Professor in the Graduate School 443 Soda Hall; [email protected] Research Interests: Graphics (GR) ; Biosystems & Computational Biology (BIO) ; Human-Computer Interaction (HCI) ; Signal Processing (SP) Education: 1981, Ph.D., Computer Science, University of Utah, Salt Lake City; 1978, M.S., Computer Graphics/Computer Science, Cornell University; 1976, B.Sc., Mathematics/Computer Science, McGill University; 1973, D.C.S., Engineering, McGill University Office Hours: By email appointment only Teaching Schedule (Spring 2024): CS 198-57. Directed Group Studies for Advanced Undergraduates , Mo 14:00-15:59, Soda 438 CS 298-57. Assistive Technology , Mo 14:00-15:59, Soda 438

Peter Bartlett

Professor 723 Sutardja Dai Hall, 510-642-7780; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) Education: 1992, Ph.D., Electrical Engineering, University of Queensland, Australia

Alexandre Bayen

Professor [email protected] Research Interests: Control, Intelligent Systems, and Robotics (CIR) ; Artificial Intelligence (AI) ; Cyber-Physical Systems and Design Automation (CPSDA) Education: 2003, Ph.D., Aeronautics and Astronautics, Stanford University; 1999, M.S., Aeronautics and Astronautics, Stanford University; 1998, Engineering, Applied Mathematics, Ecole Polytechnique, France

Manuel Blum

Professor Emeritus 621 Soda Hall; [email protected] Education: 1964, Ph.D., Mathematics, MIT; 1961, M.S., Electrical Engineering, MIT; 1959, B.S., Electrical Engineering, MIT

Christian Borgs

Professor 8060 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Theory (THY) Education: 1987, PhD, Mathematical Physics, Max-Planck-Institute and Universitat Munchen Teaching Schedule (Spring 2024): CS 170. Efficient Algorithms and Intractable Problems , TuTh 15:30-16:59, Li Ka Shing 245

Eric Brewer

Professor Emeritus 417 Sutardja Dai Hall, 510-642-8143; [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Power and Energy (ENE) ; Security (SEC) Education: 1994, Ph.D., EECS, MIT; 1989, B.S., EECS, UC Berkeley Office Hours: M 2:30-3:30, Th 1-2, 623 Soda

Aydin Buluç

Adjunct Assistant Professor [email protected] Research Interests: Scientific Computing (SCI) ; Programming Systems (PS) ; Biosystems & Computational Biology (BIO) ; Computer Architecture & Engineering (ARC) Education: 2010, Ph.D., Computer Science, University of California, Santa Barbara Teaching Schedule (Spring 2024): CS C267. Applications of Parallel Computers , TuTh 11:00-12:29, Soda 306

John F. Canny

Professor 637 Soda Hall, 510-642-9955; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Graphics (GR) ; Human-Computer Interaction (HCI) ; Security (SEC) Education: 1987, Ph.D., Electrical Engineering, MIT; 1983, M.S., Electrical Engineering, MIT; 1980, B.E. (Hons), Electrical Engineering, Adelaide University; 1979, B.Sc., Computer Science and Theoretical Physics, Adelaide University

Sarah Chasins

Assistant Professor Research Interests: Programming Systems (PS) ; Human-Computer Interaction (HCI) Education: 2019, Ph.D., Computer Science, University of California, Berkeley; 2012, B.A., Computer Science, Psychology, Swarthmore College Teaching Schedule (Spring 2024): CS 294-184. Building User-Centered Programming Tools , TuTh 14:00-15:29, Soda 320

Jennifer Chayes

Professor, Dean [email protected] Research Interests: Information, Data, Network, and Communication Sciences (IDNCS) ; Theory (THY) ; Biosystems & Computational Biology (BIO) Education: 1983, PhD, Mathematical Physics, Princeton University; 1979, BA, Biology and Physics, Wesleyan University

Below The Line Assistant Professor 120D Warren Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Biosystems & Computational Biology (BIO) Education: 2022, PhD, Electrical Engineering and Computer Science, MIT; 2014, AB/SM, Applied Math, Harvard

Alvin Cheung

Associate Professor 785 Soda Hall; [email protected] Research Interests: Database Management Systems (DBMS) ; Programming Systems (PS) Education: 2015, Ph.D., Computer Science, MIT Teaching Schedule (Fall 2024): CS 186. Introduction to Database Systems , MoWe 10:00-11:29, Soda 306

Alessandro Chiesa

Adjunct Associate Professor 683 Soda Hall; [email protected] Research Interests: Security (SEC) ; Theory (THY) Education: 2014, Ph.D., Computer Science, MIT; 2010, M.Eng., Computer Science, MIT; 2009, B.S., Computer Science and Mathematics, MIT

Michael J. Clancy

Teaching Professor Emeritus 784 Soda Hall, 510-642-7017; [email protected] Research Interests: Education (EDUC) Education: 1971, B.S., Mathematics, University of Illinois, Champaign/Urbana Office Hours: by appointment, 784 Soda

Michael Cohen

Lecturer [email protected] Research Interests: Artificial Intelligence (AI) Education: 2023, Ph. D., Engineering Science, Oxford University; 2019, M.S., Computing, Australian National University; 2015, B.A., Chemistry, Yale Teaching Schedule (Spring 2024): CS 188. Introduction to Artificial Intelligence , TuTh 12:30-13:59, Wheeler 150

Phillip Colella

Professor in Residence Emeritus MS50A-1148 Lawrence Berkeley National Laboratory, 486-5412; [email protected]

Natacha Crooks

Assistant Professor [email protected] Research Interests: Database Management Systems (DBMS) ; Operating Systems & Networking (OSNT) Education: 2019, Ph.D., Distributed Systems, University of Texas; 2012, BA, Computer Science and Law, University of Cambridge Teaching Schedule (Spring 2024): CS 294-234. Distributed Systems and Distributed Computing , Mo 09:00-11:59, Soda 320

David E. Culler

Professor Emeritus 783 Soda Hall; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Power and Energy (ENE) ; Operating Systems & Networking (OSNT) ; Cyber-Physical Systems and Design Automation (CPSDA) ; Programming Systems (PS) ; Security (SEC) Education: 1989, Ph.D., MIT; 1985, M.S., MIT; 1980, B.A., U.C. Berkeley

Trevor Darrell

Professor in Residence 8010 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) Education: 1996, PhD, MAS, MIT; 1988, BSE, CS, U.Penn. Teaching Schedule (Spring 2024): CS 294-194. From Research to Startup , We 17:00-18:29, Soda 310 Teaching Schedule (Fall 2024): CS 294-43. Large Scale Vision and Language Models , Mo 15:00-16:59, Berkeley Way West 1213

James Demmel

Professor 564 Soda Hall, 510-643-5386; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Scientific Computing (SCI) Education: 1983, Ph.D., Computer Science, UC Berkeley; 1975, B.S., Mathematics, Caltech Office Hours: Wed, 8:30 - 9:30 am, 564 Soda Assistants: Tammy Johnson, 565 Soda, 643-4816, [email protected] Teaching Schedule (Spring 2024): CS C267. Applications of Parallel Computers , TuTh 11:00-12:29, Soda 306

John DeNero

Associate Teaching Professor [email protected] Research Interests: Artificial Intelligence (AI) ; Education (EDUC) Education: 2010, Ph.D., Computer Science, University of California, Berkeley; 2002, M.A., Philosophy, Stanford University; 2001, B.S., Mathematical & Computational Science and Symbolic Systems, Stanford University Office Hours: See Homepage, 781 Soda Teaching Schedule (Spring 2024): CS 47A. Completion of Work in Computer Science 61A CS 61A. The Structure and Interpretation of Computer Programs , MoWeFr 14:00-14:59, Pimentel 1 Teaching Schedule (Fall 2024): CS 47A. Completion of Work in Computer Science 61A CS 61A. The Structure and Interpretation of Computer Programs , MoWeFr 13:00-13:59, Wheeler 150

Anca Dragan

Associate Professor Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Human-Computer Interaction (HCI) Education: 2015, PhD, Robotics, Carnegie Mellon University; 2009, B.S., Computer Science, Jacobs University, Bremen

Prabal Dutta

Professor 550C Cory Hall; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Cyber-Physical Systems and Design Automation (CPSDA) ; Power and Energy (ENE) ; Operating Systems & Networking (OSNT) Education: 2009, PhD, Computer Science, University of California, Berkeley; 1997, B.S., Electrical and Computer Engineering, Ohio State University Assistants: Sarah Gaugler, [email protected] Teaching Schedule (Spring 2024): CS 294-194. From Research to Startup , We 17:00-18:29, Soda 310 EE 375. Teaching Techniques for Electrical Engineering Teaching Schedule (Fall 2024): CS C249A. Introduction to Embedded Systems , TuTh 14:00-15:29, Soda 306 EECS 149. Introduction to Embedded and Cyber Physical Systems , TuTh 14:00-15:29, Soda 306 EE C249A. Introduction to Embedded Systems , TuTh 14:00-15:29, Soda 306 EE 375. Teaching Techniques for Electrical Engineering

Alexei (Alyosha) Efros

Professor 724 Sutardja Dai Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Graphics (GR) Education: 2003, PhD, Computer Science, UC Berkeley; 1997, BS, Computer Science, University of Utah Teaching Schedule (Spring 2024): CS C280. Computer Vision , MoWe 12:30-13:59, Berkeley Way West 1102 Teaching Schedule (Fall 2024): CS 180. Intro to Computer Vision and Computational Photography , MoWe 17:00-18:29, Li Ka Shing 245 CS 280A. Intro to Computer Vision and Computational Photography , MoWe 17:00-18:29, Li Ka Shing 245

Laurent El Ghaoui

Professor Emeritus 421 Sutardja Dai Hall; [email protected] Research Interests: Control, Intelligent Systems, and Robotics (CIR) Education: 1990, Ph.D., Aeronautics and Astronautics, Stanford; 1985, B.S., Mathematics, Ecole Polytechnique Office Hours: Wed., 9:00-10:00am, 421 Sutardja Dai

Professor 203A South Hall; [email protected] Research Interests: Graphics (GR) Education: 1997, PhD, Computer Science, University of Pennsylvania

Richard J. Fateman

Professor Emeritus 441 Soda Hall, 510-847-2368; [email protected] Research Interests: Artificial Intelligence (AI) ; Scientific Computing (SCI) Office Hours: BY APPT, 441 Soda

Jerome A. Feldman

Professor Emeritus 739 Soda Hall, 510-666-2900; [email protected] Research Interests: Artificial Intelligence (AI) ; Biosystems & Computational Biology (BIO) ; Security (SEC) Education: 1964, Ph.D., Computer Science and Mathematics, Carnegie Mellon University; 1961, M.S., Mathematics, University of Pittsburgh; 1960, B.S., Physics, University of Rochester

Domenico Ferrari

Professor Emeritus

Christopher Fletcher

Associate Professor [email protected] Research Interests: Computer Architecture & Engineering (ARC) Education: 2016, Ph.D., EECS, MIT; 2013, S.M., EECS, MIT; 2010, B.S., EECS, UC Berkeley Teaching Schedule (Spring 2024): CS 152. Computer Architecture and Engineering , TuTh 11:00-12:29, North Gate 105 CS 252A. Graduate Computer Architecture , TuTh 11:00-12:29, North Gate 105 Teaching Schedule (Fall 2024): EECS 151. Introduction to Digital Design and Integrated Circuits , TuTh 09:30-10:59, Mulford 159 EECS 151LA. Application Specific Integrated Circuits Laboratory , Mo 17:00-19:59, Cory 111 EECS 151LA-2. Application Specific Integrated Circuits Laboratory , Th 14:00-16:59, Cory 111 EECS 151LA-3. Application Specific Integrated Circuits Laboratory , Fr 11:00-13:59, Cory 111 EECS 151LB. Field-Programmable Gate Array Laboratory , Tu 14:00-16:59, Cory 111 EECS 151LB-2. Field-Programmable Gate Array Laboratory , We 17:00-19:59, Cory 111 EECS 151LB-3. Field-Programmable Gate Array Laboratory , Fr 08:00-10:59, Cory 111 EECS 151LB-4. Field-Programmable Gate Array Laboratory , Tu 17:00-19:59, Cory 111 EECS 251A. Introduction to Digital Design and Integrated Circuits , TuTh 09:30-10:59, Mulford 159 EECS 251LA-101. Introduction to Digital Design and Integrated Circuits Lab , Mo 17:00-19:59, Cory 111 EECS 251LA-102. Introduction to Digital Design and Integrated Circuits Lab , Th 14:00-16:59, Cory 111 EECS 251LA-103. Introduction to Digital Design and Integrated Circuits Lab , Fr 11:00-13:59, Cory 111 EECS 251LB-101. Introduction to Digital Design and Integrated Circuits Lab , Tu 14:00-16:59, Cory 111 EECS 251LB-102. Introduction to Digital Design and Integrated Circuits Lab , We 17:00-19:59, Cory 111 EECS 251LB-103. Introduction to Digital Design and Integrated Circuits Lab , Fr 08:00-10:59, Cory 111

Armando Fox

Professor 581 Soda Hall, 510-642-6820; [email protected] Research Interests: Programming Systems (PS) ; Education (EDUC) ; Human-Computer Interaction (HCI) Assistants: Tammy Johnson, 565 Soda, 643-4816, [email protected] Teaching Schedule (Spring 2024): CS 169L. Software Engineering Team Project , MoFr 10:30-11:59, Soda 405 CS 194-244. Special Topics , Mo 14:30-15:59, Soda 606 CS 194-245. Special Topics , Mo 14:30-15:59, Soda 606 CS 294-244. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 CS 294-245. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 CS 375. Teaching Techniques for Computer Science , Fr 13:00-14:59, Soda 438 Teaching Schedule (Fall 2024): CS 194-244. STAR Assessments for Proficiency-Based Learning , Mo 14:00-15:29, Soda 606 CS 294-244. STAR Assessments for Proficiency-Based Learning , Mo 14:00-15:29, Soda 606

Gerald Friedland

Adjunct Assistant Professor 424 Sutardja Dai Hall; [email protected] Research Interests: Signal Processing (SP) ; Artificial Intelligence (AI) ; Information, Data, Network, and Communication Sciences (IDNCS) Education: 2006, Ph.D., Computer Science, Freie Universitat Berlin; 2002, MSc, Computer Science, Freie Universitat Berlin Teaching Schedule (Spring 2024): CS 294-82. Experimental Design for Machine Learning on Multimedia Data , Fr 15:00-16:29, Soda 306

Jack Gallant

Below the Line, Professor [email protected] Research Interests: Biosystems & Computational Biology (BIO) Education: 1995, Post-doc, Systems & Computational Neuroscience, Caltech & Wash Univ. Med. Schl.; 1988, PhD, Experimental Psychology, Yale University

Teaching Professor 777 Soda Hall, 510-517-4041; [email protected] Research Interests: Education (EDUC) ; Graphics (GR) Education: 2000, Ph.D., Computer Science, UC Berkeley; 1995, M.S., Computer Science, UC Berkeley; 1990, B.S., Computer Science, MIT; 1990, B.S., Electrical Engineering, MIT Office Hours: CS10: W 2-3pm, 777 Soda Teaching Schedule (Spring 2024): CS 10. The Beauty and Joy of Computing , MoWe 13:00-13:59, Soda 306 CS 194-244. Special Topics , Mo 14:30-15:59, Soda 606 CS 194-245. Special Topics , Mo 14:30-15:59, Soda 606 CS 198-2. Gamescrafters , MoWeFr 11:00-11:59, Soda 606 CS 294-244. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 CS 294-245. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 Teaching Schedule (Fall 2024): CS 10. The Beauty and Joy of Computing , MoWe 13:00-13:59, Hearst Field Annex A1 CS 47C. Completion of Work in Computer Science 61C CS 61C. Great Ideas of Computer Architecture (Machine Structures) , MoWeFr 10:00-10:59, Dwinelle 155 CS 194-244. STAR Assessments for Proficiency-Based Learning , Mo 14:00-15:29, Soda 606 CS 198-2. Directed Group Studies for Advanced Undergraduates , MoWeFr 11:00-11:59, Soda 606 CS 294-244. STAR Assessments for Proficiency-Based Learning , Mo 14:00-15:29, Soda 606

Sanjam Garg

Associate Professor 685 Soda Hall; [email protected] Research Interests: Theory (THY) ; Security (SEC) Education: 2013, Ph.D., Computer Science, University of California, Los Angeles; 2008, B.Tech, Computer Science and Engineering, Indian Institute of Technology, Delhi Office Hours: See Homepage Teaching Schedule (Spring 2024): CS 171. Cryptography , MoWe 11:30-12:59, Soda 306 Teaching Schedule (Fall 2024): CS 170. Efficient Algorithms and Intractable Problems , TuTh 14:00-15:29, Valley Life Sciences 2050 CS 276. Cryptography , TuTh 11:00-12:29, Soda 405

Adjunct Assistant Professor [email protected] Research Interests: Database Management Systems (DBMS) ; Operating Systems & Networking (OSNT) Education: 2006, PhD, Computer Science, KTH/Royal Institute of Technology; 2002, MBA, Logistics and Marketing, Mid-Sweden University; 2002, MSc, Computer Engineering, Mid-Sweden University Teaching Schedule (Spring 2024): CS 294-194. From Research to Startup , We 17:00-18:29, Soda 310

Ken Goldberg

Professor 425 Sutardja Dai Hall, 510-643-9565; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Human-Computer Interaction (HCI) Education: 1990, PhD, Computer Science, Carnegie Mellon University; 1984, BSEE, Electrical Engineering, University of Pennsylvania; 1984, BSE, Economics, UPenn - Wharton Office Hours: see personal homepage

Shafi Goldwasser

Professor 689 Soda Hall; Research Interests: Theory (THY) Education: 1984, Ph.D., Computer Science, UC Berkeley; 1981, M.S., Computer Science, UC Berkeley; 1979, B.S., Mathematics and Science, Carnegie Mellon

Joseph Gonzalez

Associate Professor 773 Soda Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Database Management Systems (DBMS) ; Operating Systems & Networking (OSNT) Education: 2012, Ph.D., Machine Learning, Carnegie Mellon University Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected] Teaching Schedule (Fall 2024): CS 294-162. Machine Learning Systems , MoWe 14:00-15:29, Soda 310

Susan L. Graham

Professor Emerita 751 Soda Hall, 510-642-2059; [email protected] Research Interests: Graphics (GR) ; Human-Computer Interaction (HCI) ; Programming Systems (PS) ; Scientific Computing (SCI) Office Hours: by appointment, 751 Soda

Venkatesan Guruswami

Professor [email protected] Research Interests: Theory (THY) ; Information, Data, Network, and Communication Sciences (IDNCS) Education: 2001, PhD, Computer Science, MIT

Nika Haghtalab

Assistant Professor 8028 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Theory (THY) Education: 2018, Ph.D., Computer Science, Carnegie Mellon University

Moritz Hardt

Adjunct Associate Professor [email protected] Education: 2011, PhD, Computer Science, Princeton University

Michael A. Harrison

Professor Emeritus [email protected] Research Interests: Programming Systems (PS) ; Theory (THY) Education: 1963, Ph.D., Communication Sciences, University of Michigan; 1959, M.S., Electrical Engineering and Computing, Case Western Reserve University; 1958, B.S., Electrical Engineering, Case Western Reserve University

Björn Hartmann

Associate Professor 220A Jacobs Hall, 415 868 5720; [email protected] Research Interests: Human-Computer Interaction (HCI) ; Programming Systems (PS) ; Cyber-Physical Systems and Design Automation (CPSDA) ; Graphics (GR) Education: 2009, Ph.D., Computer Science, Stanford University; 2002, MSE, Computer and Information Science, University of Pennsylvania; 2001, BSE/B.A., Digital Media Design/Communication, University of Pennsylvania Office Hours: Fall'19: Thu 5-6pm, 220A Jacobs Teaching Schedule (Spring 2024): CS 160. User Interface Design and Development , TuTh 14:00-15:29, Jacobs Hall 310 CS 260A. User Interface Design and Development , TuTh 14:00-15:29, Jacobs Hall 310 Teaching Schedule (Fall 2024): CS 260B. Human-Computer Interaction Research , TuTh 12:30-13:59, Moffitt Library 102

Brian Harvey

Teaching Professor Emeritus 441 Soda Hall; [email protected] Research Interests: Education (EDUC) Education: 1990, MA, Clinical Psychology, New College of California; 1985, PhD, Science & Mathematics Education, UC Berkeley; 1975, MS, Computer Science, Stanford; 1969, BS, Mathematics, MIT Office Hours: by appointment, 441 Soda

Marti Hearst

Professor 307b South Hall, 510-642-8016; [email protected] Research Interests: Human-Computer Interaction (HCI) ; Artificial Intelligence (AI) Education: 1994, Ph.D., Computer Science, UC Berkeley; 1989, M.S., Computer Science, UC Berkeley; 1985, B.A., Computer Science, UC Berkeley Office Hours: See home page, 307B South

Joseph M. Hellerstein

Professor 789 Soda Hall, 510-643-4011; Research Interests: Database Management Systems (DBMS) ; Operating Systems & Networking (OSNT) Education: 1995, Ph.D., Computer Science, University of Wisconsin-Madison; 1992, MS, Computer Science, UC Berkeley; 1990, AB, Computer Science, Harvard University Teaching Schedule (Spring 2024): CS 286B. Implementation of Data Base Systems, TuTh 14:00-15:29, Soda 310 CS 298-12. Group Studies Seminars, or Group Research , We 11:00-11:59, Soda 380 Teaching Schedule (Fall 2024): CS 298-12. Database Seminar , We 11:00-11:59, Soda 438

Paul N. Hilfinger

Teaching Professor, Retired 787 Soda Hall, 510-642-8401; [email protected] Research Interests: Programming Systems (PS) ; Scientific Computing (SCI) Education: 1980, Ph.D., Computer Science, Carnegie-Mellon University; 1973, AB, Mathematics, Princeton University

Associate Teaching Professor 779 Soda Hall; [email protected] Research Interests: Education (EDUC) Education: 2011, Ph.D., Electrical Engineering And Computer Science, UC Berkeley; 2003, B.S., Electrical and Computer Engineering, University of Texas at Austin Office Hours: No office hours, on sabbatical Teaching Schedule (Fall 2024): CS 70. Discrete Mathematics and Probability Theory , TuTh 17:00-18:29, Pimentel 1

Christopher Hunn

Lecturer [email protected] Teaching Schedule (Spring 2024): CS 370. Adaptive Instruction Methods in Computer Science , Tu 17:00-18:59, Wheeler 212 CS 370-2. Adaptive Instruction Methods in Computer Science , Th 17:00-18:59, Social Sciences Building 110 Teaching Schedule (Fall 2024): CS 370. Adaptive Instruction Methods in Computer Science , Tu 17:00-18:59, Wheeler 212 CS 370-2. Adaptive Instruction Methods in Computer Science , Th 17:00-18:59, Wheeler 212

Nilah Ioannidis

Assistant Professor 513 Soda Hall; [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Artificial Intelligence (AI) Education: 2013, Ph.D., Biophysics, Harvard University

Lakshya Jain

Lecturer [email protected] Education: 2020, M.S., Computer Science, University of California, Berkeley Teaching Schedule (Spring 2024): CS 186. Introduction to Database Systems , MoWe 09:30-10:59,

Jiantao Jiao

Assistant Professor 257M Cory Hall; Research Interests: Artificial Intelligence (AI) ; Information, Data, Network, and Communication Sciences (IDNCS) ; Control, Intelligent Systems, and Robotics (CIR) ; Theory (THY) ; Signal Processing (SP) ; Operating Systems & Networking (OSNT) ; Database Management Systems (DBMS) ; Cyber-Physical Systems and Design Automation (CPSDA) ; Security (SEC) ; Power and Energy (ENE) ; Programming Systems (PS) Education: 2018, Ph.D., Electrical Engineering, Stanford University Assistants: Kim Kail, 253 Cory, 510-643-6633, [email protected] Teaching Schedule (Spring 2024): EECS 126. Probability and Random Processes , TuTh 14:00-15:29, Physics Building 4 Teaching Schedule (Fall 2024): EECS 126. Probability and Random Processes , TuTh 11:00-12:29, Valley Life Sciences 2040

Michael Jordan

Professor 387 Soda Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Biosystems & Computational Biology (BIO) ; Control, Intelligent Systems, and Robotics (CIR) ; Signal Processing (SP) ; Theory (THY) Education: 1985, Ph.D., Cognitive Science, UC San Diego; 1980, M.S., Mathematics, Arizona State University; 1978, B.S., Psychology, Louisiana State University Office Hours: by appointment

Anthony D. Joseph

Professor 465 Soda Hall, 510-643-7212; [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Security (SEC) Education: 1998, Ph.D., Computer Science, MIT; 1988, S.M./S.B., Electrical Engineering and Computer Science/Computer Science and Engineering, MIT Office Hours: By appointment only - please email for appointment Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected]

William M. Kahan

Professor Emeritus 513 Soda Hall, 510-642-5638; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Scientific Computing (SCI) Education: 1958, Ph.D., Mathematics, University of Toronto; 1956, Master's, Mathematics, University of Toronto; 1954, B.A., Mathematics, University of Toronto Office Hours: Irregular- phone for app't

Angjoo Kanazawa

Assistant Professor 8014 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Graphics (GR) Education: 2017, Ph.D., Computer Science, University of Maryland, College Park

Lecturer [email protected] Education: 2022, M.S., Electrical Engineering and Computer Science, UC Berkeley; 2021, B.A., Computer Science, UC Berkeley; 2021, B.A., Data Science, UC Berkeley Teaching Schedule (Spring 2024): CS 47B. Completion of Work in Computer Science 61B CS 61B. Data Structures , MoWeFr 13:00-13:59, Dwinelle 155 CS 161. Computer Security , MoWe 18:30-19:59, Dwinelle 155 Teaching Schedule (Fall 2024): CS 47B-2. Completion of Work in Computer Science 61B CS 47C. Completion of Work in Computer Science 61C CS 61B. Data Structures , MoWeFr 14:00-14:59, Wheeler 150 CS 61C. Great Ideas of Computer Architecture (Machine Structures) , MoWeFr 10:00-10:59, Dwinelle 155

Richard M. Karp

Professor Emeritus 621 Soda Hall, 510-642-5799; [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Operating Systems & Networking (OSNT) ; Theory (THY) Education: 1959, Ph.D., Applied Mathematics, Harvard; 1956, S.M., Applied Mathematics, Harvard; 1955, A.B., Mathematics, Harvard Office Hours: M 1:30-2:30, 621 Soda Assistants: Olivia Chen, 695 Soda, (510) 642-9467, [email protected]

Randy H. Katz

Professor Emeritus 751 Soda Hall, 510-642-8778; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Operating Systems & Networking (OSNT) Education: 1980, PhD, Computer Science, UC Berkeley; 1978, MS, Computer Science, UC Berkeley; 1976, AB, Computer Science & Math, Cornell University Office Hours: By appointment. Contact [email protected] Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected]

Kurt Keutzer

Professor Emeritus, Professor in the Graduate School [email protected] Research Interests: Artificial Intelligence (AI) ; Computer Architecture & Engineering (ARC) ; Scientific Computing (SCI) Education: 1984, PhD, Computer Science, Indiana University Office Hours: by appointment only Assistants: Roxana Infante, 563 Soda, 643-1455, [email protected] Teaching Schedule (Spring 2024): CS 294-194. From Research to Startup , We 17:00-18:29, Soda 310

Daniel Klein

Professor 8058 Berkeley Way West, 510-643-0805; [email protected] Research Interests: Artificial Intelligence (AI) Education: 2004, M.S./Ph.D., Computer Science, Stanford University; 1999, M.St, Linguistics, Oxford University; 1998, B.A., Math, CS, Linguistics, Cornell University Office Hours: Tuesday 2pm-3:30pm (may be in 778 SDH), 730 Sutardja Dai Teaching Schedule (Fall 2024): CS 288. Natural Language Processing , TuTh 12:30-13:59, Donner Lab 155

Aditi Krishnapriyan

Below The Line Assistant Professor [email protected] Research Interests: Artificial Intelligence (AI) ; Scientific Computing (SCI) Education: 2020, Ph.D., ㅤ, Stanford University Teaching Schedule (Spring 2024): CS 294-254. Physics Inspired Deep Learning , TuTh 12:30-13:59, Moffitt Library 103

John D. Kubiatowicz

Professor 673 Soda Hall, 510-643-6817; [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Security (SEC) Education: 1998, Ph.D., Electrical Engineering and Computer Science (minor in Physics), M.I.T.; 1993, M.S., Electrical Engineering and Computer Science, M.I.T.; 1987, B.S., EE and Physics, M.I.T. Office Hours: T/Th 3pm-4pm, 673 Soda Teaching Schedule (Spring 2024): CS 162. Operating Systems and System Programming , TuTh 12:30-13:59, Dwinelle 155

Edward A. Lee

Professor Emeritus, Professor in the Graduate School 545Q Cory Hall, 510-643-3728; [email protected] Research Interests: Cyber-Physical Systems and Design Automation (CPSDA) ; Programming Systems (PS) ; Signal Processing (SP) ; Computer Architecture & Engineering (ARC) ; Information, Data, Network, and Communication Sciences (IDNCS) ; Design, Modeling and Analysis (DMA) Education: 1986, PhD, EECS, UC Berkeley; 1981, SM, EECS, MIT; 1979, BS, CS and Eng. & Applied Science, Yale Office Hours: By appointment, 545Q Cory

Sergey Levine

Associate Professor 8056 Berkeley Way West; Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) Education: 2014, Ph.D., Computer Science, Stanford University; 2009, B.S/M.S., Computer Science, Stanford University

Jennifer Listgarten

Professor 8022 Berkeley Way West; Research Interests: Biosystems & Computational Biology (BIO) ; Artificial Intelligence (AI) Education: 2007, Ph.D., Computer Science, University of Toronto Teaching Schedule (Spring 2024): CS 294-150. AI meets Biology and Chemistry , Mo 14:00-16:59, Berkeley Way West 1217 Teaching Schedule (Fall 2024): CS 189. Introduction to Machine Learning , TuTh 14:00-15:29, Haas Faculty Wing F295 CS 289A. Introduction to Machine Learning , TuTh 14:00-15:29, Haas Faculty Wing F295

Michael Lustig

Professor 506 Cory Hall; [email protected] Education: 2008, PhD, EE, Stanford University; 2004, MSc, EE, Stanford University; 2001, BSc, EE, 🇮🇱💔 Technion, Israel Institute of Technology Office Hours: 🇮🇱🇮🇱 Bring those kidnapped by Hammas home! 💔💔 Teaching Schedule (Spring 2024): EECS 16B. Designing Information Devices and Systems II , TuTh 09:30-10:59, Pimentel 1

Jitendra Malik

Professor 8012 Berkeley Way West, 510-642-7597; 389 Soda Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Signal Processing (SP) Education: 1980, B.Tech, Electrical Engineering, Indian Institute of Technology, Kanpur; 1975, Ph.D., Computer Science, Stanford University Office Hours: By appointment only, 722 Sutardja Dai Assistants: Angie Abbatecola, 741 Soda, (510) 643-6413, [email protected]; Alex Sandoval, 510 642-0253, [email protected]

Igor Mordatch

Lecturer [email protected] Research Interests: Artificial Intelligence (AI) Education: 2016, Post-Doctoral Associate, EECS, UC Berkeley; 2015, PhD, Computer Science & Engineering, University of Washington Teaching Schedule (Fall 2024): CS 188. Introduction to Artificial Intelligence , TuTh 15:30-16:59, Dwinelle 155

Jelani Nelson

Professor 633 Soda Hall; [email protected] Research Interests: Theory (THY) ; Database Management Systems (DBMS) Education: 2011, Ph.D., Computer Science, MIT; 2006, M.Eng., Computer Science, MIT; 2005, S.B., Computer Science, Mathematics, MIT Teaching Schedule (Fall 2024): CS 298-3. Group Studies Seminars, or Group Research , We 16:00-16:59, Soda 306

Associate Professor 8062 Berkeley Way West; [email protected] Research Interests: Graphics (GR) ; Signal Processing (SP) ; Artificial Intelligence (AI) Education: 2006, Ph.D., Computer Science, Stanford University; 2006, M.S., Computer Science, Stanford University; 2001, B.S., Mathematical and Computational Science, Stanford University Office Hours: Thursday 1 - 2pm or by appointment Teaching Schedule (Spring 2024): CS 184. Foundations of Computer Graphics , TuTh 11:00-12:29, Dwinelle 145 CS 284A. Foundations of Computer Graphics , TuTh 11:00-12:29, Dwinelle 145 Teaching Schedule (Fall 2024): CS 194-164. Computational Human Vision , Tu 13:00-15:59, Berkeley Way West 1217 CS 294-164. Computational Human Vision , Tu 13:00-15:59, Berkeley Way West 1217

Narges Norouzi

Assistant Teaching Professor 775 Soda Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Education (EDUC) ; Biosystems & Computational Biology (BIO) Education: 2017, PhD, Computer Engineering, University of Toronto; 2014, MS, Computer Engineering, University of Toronto; 2012, BS, Computer Engineering, Sharif University of Technology Teaching Schedule (Spring 2024): CS 194-244. Special Topics , Mo 14:30-15:59, Soda 606 CS 194-245. Special Topics , Mo 14:30-15:59, Soda 606 CS 294-244. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 CS 294-245. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 Teaching Schedule (Fall 2024): CS 194-244. STAR Assessments for Proficiency-Based Learning , Mo 14:00-15:29, Soda 606 CS 194-271. Research in AI Education , Tu 14:00-15:29, Soda 606 CS 294-244. STAR Assessments for Proficiency-Based Learning , Mo 14:00-15:29, Soda 606 CS 294-271. Research in AI Education , Tu 14:00-15:29, Soda 606

James O'Brien

Professor [email protected] Research Interests: Graphics (GR) ; Artificial Intelligence (AI) ; Human-Computer Interaction (HCI) ; Scientific Computing (SCI) Education: 2000, Ph.D., Computer Science, Georgia Institute of Technology; 1997, M.S., Computer Science, Georgia Institute of Technology; 1992, B.S., Computer Science, Florida International University Teaching Schedule (Spring 2024): CS 284B. Advanced Computer Graphics Algorithms and Techniques , TuTh 12:30-13:59, Soda 405

Christos Papadimitriou

Professor Emeritus [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Theory (THY) Assistants: Olivia Chen, 695 Soda, (510) 642-9467, [email protected]

Aditya Parameswaran

Associate Professor 212 South Hall; [email protected] Research Interests: Database Management Systems (DBMS) ; Human-Computer Interaction (HCI) Education: 2013, PhD, Computer Science, Stanford; 2007, BTech, Computer Science and Engineering, IIT Bombay Office Hours: by appointment Teaching Schedule (Spring 2024): CS 298-12. Group Studies Seminars, or Group Research , We 11:00-11:59, Soda 380 Teaching Schedule (Fall 2024): CS 298-12. Database Seminar , We 11:00-11:59, Soda 438

Beresford N. Parlett

Professor Emeritus 799 Evans Hall;

David A. Patterson

Professor Emeritus 579 Soda Hall, 642-6587; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Operating Systems & Networking (OSNT) Education: 1976, PhD, Computer Science, UCLA; 1970, MS, Computer Science, UCLA; 1969, AB, Mathematics, UCLA Office Hours: Mondays, by appointment, 579 Soda Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected]

Eric Paulos

Professor [email protected] Research Interests: Human-Computer Interaction (HCI) Office Hours: See Homepage www.paulos.net

Vern Paxson

Professor Emeritus, Professor in the Graduate School 737 Soda Hall, 3-4209; 630 International Computer Science Institute, 666-2882; [email protected] Research Interests: Security (SEC) ; Operating Systems & Networking (OSNT) Office Hours: By appointment via Zoom

Kristofer Pister

Professor 512 Cory Hall; [email protected] Research Interests: Micro/Nano Electro Mechanical Systems (MEMS) ; Control, Intelligent Systems, and Robotics (CIR) ; Integrated Circuits (INC) Education: 1992, Ph.D., EECS, UC Berkeley; 1989, M.S., EECS, UC Berkeley; 1986, B.A., Applied Physics, UC San Diego Office Hours: W 11-12, Th 4:30-5:30, 512 Cory

Raluca Ada Popa

Associate Professor 729 Soda Hall; [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Security (SEC) Education: 2014, Doctor of Philosophy, Computer Science, Massachusetts Institute of Technology; 2010, Masters of Engineering, Computer Science, Massachusetts Institute of Technology; 2009, Bachelor's degree, Computer Science and Mathematics, Massachusetts Institute of Technology Office Hours: Tuesday 2-3pm, 729 Soda Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected] Teaching Schedule (Spring 2024): CS 161. Computer Security , MoWe 18:30-19:59, Dwinelle 155

Prasad Raghavendra

Professor 623 Soda Hall; [email protected] Research Interests: Theory (THY) Education: 2009, PhD, Computer Science and Engineering, University of Washington, Seattle; 2007, M.S., Computer Science and Engineering, University of Washington, Seattle; 2005, B.S., Computer Science, Indian Institute of Technology , Madras, India Office Hours: Wed 11-noon, 623 Soda Teaching Schedule (Spring 2024): CS 170. Efficient Algorithms and Intractable Problems , TuTh 15:30-16:59, Li Ka Shing 245 CS 298-2. Group Studies Seminars, or Group Research , We 12:00-13:29, Soda 438 Teaching Schedule (Fall 2024): CS 170. Efficient Algorithms and Intractable Problems , TuTh 14:00-15:29, Valley Life Sciences 2050 CS 298-2. Group Studies Seminars, or Group Research , We 12:00-13:29, Soda 438

Kannan Ramchandran

Professor 269 Cory Hall, 510-642-2353; [email protected] Research Interests: Information, Data, Network, and Communication Sciences (IDNCS) ; Signal Processing (SP) ; Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) Education: 1993, Ph.D., Computer Science, Columbia University; 1984, M.S., Computer Science, Columbia University; 1982, B.E., Computer Science, City College of New York Office Hours: Tu. 3:00-4:00, or by appt., 258 Cory Assistants: Kim Kail, 253 Cory, 510-643-6633, [email protected] Teaching Schedule (Fall 2024): EE 120. Signals and Systems , MoWe 15:00-16:59, Valley Life Sciences 2060

Professor 687 Soda Hall, 510-642-4328; [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Theory (THY) Assistants: Olivia Chen, 695 Soda, (510) 642-9467, [email protected] Teaching Schedule (Fall 2024): CS 70. Discrete Mathematics and Probability Theory , TuTh 17:00-18:29, Pimentel 1 CS 197-70. Field Study CS 270. Combinatorial Algorithms and Data Structures , TuTh 11:00-12:29, Soda 306

Sylvia Ratnasamy

Professor 413 Soda Hall, 2-8905; [email protected] Research Interests: Operating Systems & Networking (OSNT) Assistants: Carlyn Chinen, 510-990-5109, [email protected]; Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected] Teaching Schedule (Spring 2024): CS 168. Introduction to the Internet: Architecture and Protocols , TuTh 15:30-16:59, Dwinelle 145 Teaching Schedule (Fall 2024): CS 168. Introduction to the Internet: Architecture and Protocols , TuTh 11:00-12:29, Haas Faculty Wing F295

Benjamin Recht

Professor 8008 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Signal Processing (SP) Teaching Schedule (Spring 2024): CS 294-269. The Philosophy and History of Automated Decision Making , Th 14:00-16:59, Teaching Schedule (Fall 2024): EE 227BT. Convex Optimization , TuTh 14:00-15:29, Anthro/Art Practice Bldg 155

Lawrence A. Rowe

Professor Emeritus [email protected] Education: 1976, Ph.D., Information and Computer Science, University of California, Irvine; 1970, B.A., Mathematics, University of California, Irvine

Jaijeet Roychowdhury

Professor 545E Cory Hall, 643-5664; [email protected] Research Interests: Cyber-Physical Systems and Design Automation (CPSDA) ; Integrated Circuits (INC) ; Information, Data, Network, and Communication Sciences (IDNCS) ; Computer Architecture & Engineering (ARC) ; Control, Intelligent Systems, and Robotics (CIR) ; Artificial Intelligence (AI) Education: 1993, PhD, EECS, Berkeley; 1989, MS, EECS, Berkeley; 1987, B.Tech., EE, IIT Kanpur Teaching Schedule (Spring 2024): EECS 219A. Numerical Simulation and Modeling , MoWe 14:00-15:59, Cory 531 EE 290-9. Oscillator Ising Machines: Special Topics , MoWeFr 11:00-11:59, Cory 531 Teaching Schedule (Fall 2024): EE 290-7. Oscillator Ising Machines: Special Topics , MoWeFr 10:00-10:59, Cory 531

Stuart J. Russell

Professor, CS Division Chair 8040 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Biosystems & Computational Biology (BIO) Education: 1986, PhD, Computer Science, Stanford University; 1982, BA Hons (1st class), Physics, Oxford University Teaching Schedule (Spring 2024): CS 298-3. EECS Colloquium , We 16:00-17:29, Soda 306

Anant Sahai

Professor 267 Cory Hall; [email protected] Research Interests: Information, Data, Network, and Communication Sciences (IDNCS) ; Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Theory (THY) ; Signal Processing (SP) Education: 2001, PhD, EECS, Massachusetts Institute of Technology; 1996, SM, EECS, Massachusetts Institute of Technology; 1994, BS, EECS, University of California, Berkeley Office Hours: TuTh 11-12, 258 Cory Assistants: Kim Kail, 253 Cory, 510-643-6633, [email protected] Teaching Schedule (Spring 2024): EE 226A. Random Processes in Systems , TuTh 09:30-10:59, Cory 540AB EE 290-12. InContext: Understanding in-context learning in language models via simple function classes , We 14:00-15:59, Cory 540AB

Niloufar Salehi

Below The Line Assistant Professor 313 South Hall; [email protected] Research Interests: Human-Computer Interaction (HCI) Education: 2019, Ph.D., Computer Science, Stanford University

Koushik Sen

Professor 735 Soda Hall, 510-642-2420; [email protected] Research Interests: Programming Systems (PS) ; Security (SEC) Office Hours: Fridays 2pm-3pm, 735 Soda Assistants: Tammy Johnson, 565 Soda, 643-4816, [email protected] Teaching Schedule (Spring 2024): CS 164. Programming Languages and Compilers , MoWe 10:00-11:29, Soda 306 Teaching Schedule (Fall 2024): CS 164. Programming Languages and Compilers , MoWe 14:00-15:29, Soda 306

Carlo H. Séquin

Professor Emeritus 639 Soda Hall, 510-642-5103; [email protected] Research Interests: Graphics (GR) ; Human-Computer Interaction (HCI) Education: 1969, Ph.D., Experimental Physics, University of Basel, Switzerland Office Hours: see homepage for currently valid time slots, 639 Soda

Sanjit A. Seshia

Professor 566 Cory Hall, 510-643-6968; [email protected] Research Interests: Cyber-Physical Systems and Design Automation (CPSDA) ; Programming Systems (PS) ; Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Security (SEC) ; Theory (THY) Education: 2005, Ph.D., Computer Science, Carnegie Mellon University; 2000, M.S., Computer Science, Carnegie Mellon University; 1998, B.Tech., Computer Science, Institute of Technology, Bombay Office Hours: MW 2:30-3 PM and by appointment, 566 Cory Assistants: Charlotte Jones, 550 Sutardja Dai, 510-664-4203, [email protected] Teaching Schedule (Spring 2024): CS 70. Discrete Mathematics and Probability Theory , TuTh 15:30-16:59, Dwinelle 155 EECS 219C. Formal Methods: Specification, Verification, and Synthesis , MoWe 13:00-14:29, Cory 299 Teaching Schedule (Fall 2024): CS C249A. Introduction to Embedded Systems , TuTh 14:00-15:29, Soda 306 EECS 149. Introduction to Embedded and Cyber Physical Systems , TuTh 14:00-15:29, Soda 306 EE C249A. Introduction to Embedded Systems , TuTh 14:00-15:29, Soda 306

Sophia Shao

Assistant Professor 570 Cory Hall; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Integrated Circuits (INC) ; Cyber-Physical Systems and Design Automation (CPSDA) Education: 2016, Ph.D., Computer Science, Harvard University Teaching Schedule (Spring 2024): EE 290-2. Hardware for Machine Learning , MoWe 09:30-10:59, Cory 293 Teaching Schedule (Fall 2024): EECS 151. Introduction to Digital Design and Integrated Circuits , TuTh 09:30-10:59, Mulford 159 EECS 151LA. Application Specific Integrated Circuits Laboratory , Mo 17:00-19:59, Cory 111 EECS 151LA-2. Application Specific Integrated Circuits Laboratory , Th 14:00-16:59, Cory 111 EECS 151LA-3. Application Specific Integrated Circuits Laboratory , Fr 11:00-13:59, Cory 111 EECS 151LB. Field-Programmable Gate Array Laboratory , Tu 14:00-16:59, Cory 111 EECS 151LB-2. Field-Programmable Gate Array Laboratory , We 17:00-19:59, Cory 111 EECS 151LB-3. Field-Programmable Gate Array Laboratory , Fr 08:00-10:59, Cory 111 EECS 151LB-4. Field-Programmable Gate Array Laboratory , Tu 17:00-19:59, Cory 111 EECS 251A. Introduction to Digital Design and Integrated Circuits , TuTh 09:30-10:59, Mulford 159 EECS 251LA-101. Introduction to Digital Design and Integrated Circuits Lab , Mo 17:00-19:59, Cory 111 EECS 251LA-102. Introduction to Digital Design and Integrated Circuits Lab , Th 14:00-16:59, Cory 111 EECS 251LA-103. Introduction to Digital Design and Integrated Circuits Lab , Fr 11:00-13:59, Cory 111 EECS 251LB-101. Introduction to Digital Design and Integrated Circuits Lab , Tu 14:00-16:59, Cory 111 EECS 251LB-102. Introduction to Digital Design and Integrated Circuits Lab , We 17:00-19:59, Cory 111 EECS 251LB-103. Introduction to Digital Design and Integrated Circuits Lab , Fr 08:00-10:59, Cory 111

Scott Shenker

Professor Emeritus, Professor in the Graduate School 415 Soda Hall, 510-643-3043; [email protected] Research Interests: Operating Systems & Networking (OSNT) Education: 1983, Ph.D., Physics, University of Chicago; 1978, Sc.B., Physics, Brown University Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected]

Jonathan Shewchuk

Professor 529 Soda Hall, 510-642-3936; [email protected] Research Interests: Scientific Computing (SCI) ; Theory (THY) ; Graphics (GR) Teaching Schedule (Spring 2024): CS 189. Introduction to Machine Learning , MoWe 18:30-19:59, Wheeler 150 CS 289A. Introduction to Machine Learning , MoWe 18:30-19:59, Wheeler 150

Alistair Sinclair

Professor 677 Soda Hall, 510-643-8144; [email protected] Research Interests: Theory (THY) Office Hours: M 1:30-2:30, Tu 12:45-1:45, 677 Soda Assistants: Olivia Chen, 695 Soda, (510) 642-9467, [email protected] Teaching Schedule (Spring 2024): CS 70. Discrete Mathematics and Probability Theory , TuTh 15:30-16:59, Dwinelle 155 Teaching Schedule (Fall 2024): CS 271. Randomness and Computation , TuTh 09:30-10:59, Wheeler 200

Alan J. Smith

Professor Emeritus 192 Soda Hall, 510-642-5290; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Operating Systems & Networking (OSNT) Office Hours: by appointment only - send email or call, 192 Soda

Professor 675 Soda Hall, 510-642-8282; [email protected] Research Interests: Artificial Intelligence (AI) ; Operating Systems & Networking (OSNT) ; Security (SEC) ; Programming Systems (PS) Education: 2002, Ph.D., Computer Science, UC Berkeley; 1999, M.S., Computer Science, Carnegie Mellon University Teaching Schedule (Spring 2024): CS 194-267. Special Topics , Tu 15:30-16:59, Soda 306 CS 294-267. Understanding Large Language Models (LLMs): Foundations and Safety , Tu 15:30-16:59, Soda 306 Teaching Schedule (Fall 2024): CS 194-177. Special Topics on Decentralized Finance , Mo 10:00-11:59, Joan and Sanford I. Weill 101D CS 194-196. Special Topics on Decentralized Intelligence: Large Language Model Agents , Mo 15:00-16:59, Latimer 120 CS 294-177. Special Topics on Decentralized Finance , Mo 10:00-11:59, Joan and Sanford I. Weill 101D CS 294-196. Special Topics on Decentralized Intelligence: Large Language Model Agents , Mo 15:00-16:59, Latimer 120

Yun S. Song

Professor 304B Stanley Hall, 510-642-2351; [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Artificial Intelligence (AI) ; Theory (THY) Education: 2001, PhD, Physics, Stanford University; 1997, B.S., Mathematics, MIT; 1996, B.S., Physics, MIT Office Hours: On sabbatical leave for Spring 2024

Costas J. Spanos

Professor Emeritus 510 Cory Hall, 510-643-6776; Research Interests: Power and Energy (ENE) ; Integrated Circuits (INC) ; Physical Electronics (PHY)

Jacob Steinhardt

Assistant Professor 8026 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Information, Data, Network, and Communication Sciences (IDNCS) Education: 2018, PhD, Computer Science, Stanford; 2012, BSc, Mathematics, MIT

Professor 481-2 Soda Hall, 510-643-4007; [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Security (SEC) Education: 2000, Ph.D., Electrical and Computer Engineering, Carnegie Mellon University; 1989, M.S., Computer Science and Control Engineering, Polytechnic University Bucharest Office Hours: Monday 11-12 PM, Location TBD Assistants: Kattt Atchley, 465 Soda, 510-643-3499, [email protected]; Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected] Teaching Schedule (Spring 2024): CS 294-194. From Research to Startup , We 17:00-18:29, Soda 310 Teaching Schedule (Fall 2024): CS 162. Operating Systems and System Programming , TuTh 18:30-19:59, Dwinelle 155

Michael Stonebraker

Professor Emeritus Education: 1971, Ph.D., Computer, Information and Control Engineering, University of Michigan; 1966, M.S.E., Electrical Engineering, University of Michigan; 1965, B.S.E., Electrical Engineering, Princeton

Bernd Sturmfels

Professor Emeritus 925 Evans Hall, 510-642 6550; [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Theory (THY) Education: 1987, Dr.rer.nat., Mathematics, Technical University Darmstadt, Germany; 1987, Ph.D., Mathematics, University of Washington, Seattle; 1985, Diplom, Mathematics and Computer Science, Technical University Darmstadt, Germany Office Hours: T 9:45-11am, F 10:30-11:30am, 925 Evans

Assistant Professor 8052 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) Education: 2022, PhD, Computer Science, Cornell University; 2016, BS, Computer Science and Engineering, Ohio State University Teaching Schedule (Spring 2024): CS 294-258. Language Agents in Interaction , TuTh 15:30-16:59, Soda 310 Teaching Schedule (Fall 2024): CS 288. Natural Language Processing , TuTh 12:30-13:59, Donner Lab 155

Avishay Tal

Assistant Professor 635 Soda Hall; [email protected] Research Interests: Theory (THY) Education: 2015, PhD, Computer Science, Weizmann Institute of Science; 2012, MS, Computer Science, The Technion, Haifa, Israel; 2007, BA, Mathematics, The Technion, Haifa, Israel; 2005, BS, Software Engineering, The Technion, Haifa, Israel Teaching Schedule (Spring 2024): CS 278. Machine-Based Complexity Theory , TuTh 14:00-15:29, Soda 405 CS 298-2. Group Studies Seminars, or Group Research , We 12:00-13:29, Soda 438 Teaching Schedule (Fall 2024): CS 172. Computability and Complexity , TuTh 17:00-18:29, Lewis 9 CS 298-2. Group Studies Seminars, or Group Research , We 12:00-13:29, Soda 438

Claire Tomlin

Professor, Chair 721 Sutardja Dai Hall, 510-643-6610; [email protected] Research Interests: Control, Intelligent Systems, and Robotics (CIR) ; Biosystems & Computational Biology (BIO) Education: 1998, Ph.D., EECS, UC Berkeley; 1993, M.Sc., Electrical Engineering, Imperial College, London; 1992, B.A.Sc., Electrical Engineering, University of Waterloo Office Hours: By Appointment, 721 Sutardja Dai Assistants: Jessica Gamble, 337 Cory, 510-643-5105, [email protected]; Alex Sandoval, 510 642-0253, [email protected]

Umesh Vazirani

Professor 671 Soda Hall, 510-642-0572; [email protected] Research Interests: Theory (THY) ; Security (SEC) Education: 1986, Ph.D., Computer Science, UC Berkeley; 1981, B.S., MIT Assistants: Olivia Chen, 695 Soda, (510) 642-9467, [email protected]

Allon Wagner

Assistant Professor 304A Stanley Hall; [email protected] Research Interests: Biosystems & Computational Biology (BIO) Education: 2021, PhD, Computer Science, UC Berkeley

David A. Wagner

Professor 733 Soda Hall, 510-642-2758; [email protected] Research Interests: Security (SEC) Education: 2000, Ph.D., Computer Science, UC Berkeley; 1999, M.S., Computer Science, UC Berkeley; 1995, A.B., Mathematics, Princeton University Teaching Schedule (Fall 2024): CS 161. Computer Security , TuTh 09:30-10:59, Hearst Field Annex A1

Martin Wainwright

Professor 263 Cory Hall, 510-643-1978; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Information, Data, Network, and Communication Sciences (IDNCS) ; Signal Processing (SP) ; Theory (THY) Office Hours: by appointment Assistants: Kim Kail, 253 Cory, 510-643-6633, [email protected]

Laura Waller

Professor 514 Cory Hall, (510) 642-2753; [email protected] Research Interests: Physical Electronics (PHY) ; Signal Processing (SP) ; Biosystems & Computational Biology (BIO) ; Graphics (GR) Education: 2010, Ph.D., Electrical Engineering and Computer Science, MIT; 2005, M.Eng., Electrical Engineering and Computer Science, MIT; 2004, B.S., Electrical Engineering and Computer Science, MIT Office Hours: Tuesday and Thursdays 11:00-11:30am, 514 Cory

Jean Walrand

Professor Emeritus 257 Cory Hall, 510-219-5821; [email protected] Research Interests: Information, Data, Network, and Communication Sciences (IDNCS)

John Wawrzynek

Professor 631 Soda Hall, 510-643-9434; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Design, Modeling and Analysis (DMA) Office Hours: Tues., 1:00-2:00pm and by appointment, 631 Soda Teaching Schedule (Spring 2024): EECS 151. Introduction to Digital Design and Integrated Circuits , MoWe 14:00-15:29, Soda 306 EECS 151LA-101. Application Specific Integrated Circuits Laboratory , Tu 11:00-13:59, Cory 111 EECS 151LA-102. Application Specific Integrated Circuits Laboratory , Tu 14:00-16:59, Cory 111 EECS 151LB. Field-Programmable Gate Array Laboratory , Mo 11:00-13:59, Cory 111 EECS 151LB-2. Field-Programmable Gate Array Laboratory , Tu 08:00-10:59, Cory 111 EECS 151LB-3. Field-Programmable Gate Array Laboratory , Mo 17:00-19:59, Cory 111 EECS 151LB-4. Field-Programmable Gate Array Laboratory , Mo 08:00-10:59, Cory 111 EECS 251A. Introduction to Digital Design and Integrated Circuits , MoWe 14:00-15:29, Soda 306 EECS 251LA-101. Introduction to Digital Design and Integrated Circuits Lab , Tu 11:00-13:59, Cory 111 EECS 251LA-102. Introduction to Digital Design and Integrated Circuits Lab , Tu 14:00-16:59, Cory 111 EECS 251LB-101. Introduction to Digital Design and Integrated Circuits Lab , Mo 11:00-13:59, Cory 111 EECS 251LB-103. Introduction to Digital Design and Integrated Circuits Lab , Mo 17:00-19:59, Cory 111 EECS 251LB-104. Introduction to Digital Design and Integrated Circuits Lab , Mo 08:00-10:59, Cory 111

Max Willsey

Assistant Professor 725 Soda Hall; [email protected] Research Interests: Programming Systems (PS) Education: 2021, PhD, Computer Science, University of Washington Teaching Schedule (Spring 2024): CS 294-260. Declarative Program Analysis and Optimization , MoWe 14:30-15:59, Soda 405 Teaching Schedule (Fall 2024): CS 265. Compiler Optimization and Code Generation , TuTh 14:00-15:29, Soda 405

John Wright

Assistant Professor [email protected] Research Interests: Theory (THY) Education: 2016, Ph.D., Computer Science, Carnegie Mellon University Teaching Schedule (Spring 2024): CS 294-242. Quantum Coding Theory , MoFr 10:00-11:29, Hearst Mining 410 Teaching Schedule (Fall 2024): CS 294-261. Learning Problems in Quantum Computing , MoWe 10:30-11:59, Soda 405

Below The Line Assistant Professor [email protected] Research Interests: Artificial Intelligence (AI) Education: 2022, PhD, Computer Science, MIT

Assistant Teaching Professor 783 Soda Hall; [email protected] Research Interests: Education (EDUC) Education: 2019, PhD, Electrical Engineering, Stanford University; 2015, MS, Electrical Engineering, Stanford University; 2013, BS, Electrical Engineering and Computer Science, University of California, Berkeley Office Hours: (CS61C) M 2-3pm, 783 Soda; (Tea Hours, Data 375) Th 1-2:30pm, 783 Soda Teaching Schedule (Spring 2024): CS 47C. Completion of Work in Computer Science 61C CS 61C. Great Ideas of Computer Architecture (Machine Structures) , MoWeFr 10:00-10:59, Dwinelle 155 Teaching Schedule (Fall 2024): CS 195. Social Implications of Computer Technology , Tu 15:30-16:59, Physics Building 1 CS H195. Honors Social Implications of Computer Technology , Tu 15:30-16:59, Physics Building 1

Katherine A. Yelick

Professor 50A Lawrence Berkeley National Laboratory, 510-495-2431; [email protected] Research Interests: Programming Systems (PS) ; Scientific Computing (SCI) ; Biosystems & Computational Biology (BIO) Education: 1991, Ph.D., EECS, MIT; 1985, S.M., EECS, MIT; 1982, B.S., EECS, MIT Assistants: Tammy Johnson, 565 Soda, 643-4816, [email protected]

Justin Yokota

Lecturer [email protected] Education: 2022, M.S., Computer Science, UC Berkeley; 2021, B.A., Computer Science, Mathematic, UC Berkeley Teaching Schedule (Spring 2024): CS 47B. Completion of Work in Computer Science 61B CS 47C. Completion of Work in Computer Science 61C CS 61B. Data Structures , MoWeFr 13:00-13:59, Dwinelle 155 CS 61C. Great Ideas of Computer Architecture (Machine Structures) , MoWeFr 10:00-10:59, Dwinelle 155 Teaching Schedule (Fall 2024): CS 47B-2. Completion of Work in Computer Science 61B CS 61B. Data Structures , MoWeFr 14:00-14:59, Wheeler 150

Adjunct Associate Professor 629 Soda Hall; [email protected] Research Interests: Biosystems & Computational Biology (BIO)

Professor 367 Evans Hall, 510-642-2021; [email protected] Research Interests: Signal Processing (SP) Education: 1990, Ph.D., Statistics, University of California, Berkeley; 1987, M.A., Statistics, University of California, Berkeley; 1984, B.S., Mathematics, Peking University

Adjunct Professor [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Graphics (GR) ; Signal Processing (SP) Education: 2003, Ph.D., Robotics, Carnegie Mellon University

Matei Zaharia

Associate Professor [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Artificial Intelligence (AI) ; Database Management Systems (DBMS) Education: 2013, PhD, Computer Science, UC Berkeley Teaching Schedule (Fall 2024): CS 294-162. Machine Learning Systems , MoWe 14:00-15:29, Soda 310

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Theory at Berkeley

This is the homepage of the Theory Group in the EECS Department at the University of California, Berkeley .

Berkeley is one of the cradles of modern theoretical computer science. Over the last thirty years, our graduate students and, sometimes, their advisors have done foundational work on NP-completeness, cryptography, derandomization, probabilistically checkable proofs, quantum computing, and algorithmic game theory. The mild weather, celebrated eateries (see here and here ), and collaborative atmosphere are known to be conducive to great theory-building and problem-solving.

In addition, Berkeley's Simons Institute for the Theory of Computing regularly brings together theory-oriented researchers from all over the world to collaboratively work on hard problems. The institute organizes a sequence of programs based on topics (see current & future programs and past ones ), supported by workshops (see current & future workshops and past ones ) and other events.

On Wednesdays, our group comes together for Theory Lunch , an event featuring an informal lunch followed by a whiteboard presentation; this allows for much mingling, including with our friends from Statistics and Math (and, occasionally, Physics and Chemistry). On Fridays, TGIF, the informal student seminar that is off-limits to faculty, provides a comfortable space for students to learn about each other's work.

Some of our current focus is on using computation as a lens to the sciences . Like probabilistic thinking in the last century, computational thinking will give mathematics and, more generally, science a new language to use and the ability to formulate new fundamental questions. We are studying the applications of theoretical computer science in many sciences, including economics (with our work on computational game theory and mechanism design), physics (with our work on random structures and quantum computing), biology, and pure mathematics (especially geometry, functional analysis, and additive number theory). The core problems in algorithms, compexity theory, and cryptography remain, of course, dear to our hearts.

If you would like to join Berkeley's EECS Department as a graduate student, apply to our Ph.D. program . If you are interested in postdoc opportunities at UC Berkeley to work with the theory group, click here .

• Theory Lunch on Wednesdays, 12:00-13:00, Wozniak Lounge
• Theory Seminar on (most) Mondays, 16:00-17:00, Wozniak Lounge
• TGIF on Fridays, 15:30-17:00, Theory Lounge

Undergraduate Courses

• CS 170: Efficient Algorithms and Intractable Problems
• CS 172: Computability and Complexity
• CS 174: Combinatorics and Discrete Probability
• CS 191: Qubits, Quantum Mechanics, and Computers
• CS 191: Quantum Information Science And Technology
• CS 194: Undergraduate Cryptography

Graduate Courses

• in Spring of 2023 (Jelani Nelson)
• in Spring of 2021 (Prasad Raghavendra)
• in Spring of 2019 (Satish Rao)
• in Spring of 2017 (Satish Rao)
• in Spring of 2016 (Christos Papadimitriou)
• in Spring of 2012 (Satish Rao, Umesh Vazirani)
• in Spring of 2011 (Satish Rao, Umesh Vazirani)
• in Fall of 2022 (Alistair Sinclair)
• in Spring of 2020 (Alistair Sinclair)
• in Spring of 2018 (Alistair Sinclair)
• in Fall of 2011 (Alistair Sinclair)
• in Fall of 2008 (Alistair Sinclair)
• in Fall of 2010 (Satish Rao)
• in Spring of 2009 (Satish Rao)
• in Fall of 2006 (Satish Rao)
• in Spring of 2005 (Satish Rao)
• in Spring of 2003 (Satish Rao)
• in Spring of 2001 (Satish Rao)
• in Spring of 2019 (Jonathan Shewchuk)
• in Spring of 2015 (Jonathan Shewchuk)
• in Spring of 2013 (Jonathan Shewchuk)
• in Fall of 2009 (Jonathan Shewchuk)
• in Fall of 2006 (Jonathan Shewchuk)
• in Spring of 2005 (Jonathan Shewchuk)
• in Spring of 2003 (Jonathan Shewchuk)
• in Fall of 2020 (Raluca Ada Popa, Shafi Goldwasser)
• in Fall of 2018 (Sanjam Garg)
• in Fall of 2017 (Alessandro Chiesa)
• in Fall of 2016 (Sanjam Garg)
• in Fall of 2015 (Alessandro Chiesa)
• in Fall of 2014 (Sanjam Garg)
• in Spring of 2009 (Luca Trevisan)
• in Spring of 2006 (David Wagner)
• in Spring of 2004 (David Wagner)
• in Spring of 2002 (Luca Trevisan, David Wagner)
• in Spring of 2021 (Avishay Tal)
• in Fall of 2016 (Prasad Raghavendra)
• in Spring of 2008 (Luca Trevisan)
• in Fall of 2004 (Luca Trevisan)
• in Fall of 2002 (Luca Trevisan)
• in Spring of 2001 (Luca Trevisan)
• in Spring of 2013 (Elchanan Mossel)
• in Spring of 2020 (Vinod Vaikuntanathan)
• in Spring of 2016 (Luca Trevisan)
• in Fall of 2012 (Prasad Raghavendra)
• in Spring of 2014 (Christos Papadimitriou)
• in Spring of 2010 (Christos Papadimitriou)
• in Fall of 2009 (Alistair Sinclair)
• in Spring of 2016 (Prasad Raghavendra)
• in Spring of 2012 (Jonathan Shewchuk)
• in Spring of 2008 (Jonathan Shewchuk)
• in Fall of 1999 (Jonathan Shewchuk)
• in Fall of 2020 (Jelani Nelson)
• in Fall of 2019 (Frank Partnoy, Shafi Goldwasser)
• in Fall of 2018 (Shafi Goldwasser)
• in Fall of 2017 (Luca Trevisan)
• in Spring of 2006 (Luca Trevisan)
• in Fall of 2005 (Luca Trevisan)
• in Fall of 2017 (Tom Gur, Igor Shinkar)
• in Fall of 2003 (Luca Trevisan)
• in Spring of 1999 (Umesh Vazirani)
• in Fall of 2012 (Yun Song)
• in Fall of 2018 (Prasad Raghavendra)
• in Fall of 2020 (Alistair Sinclair)
• in Spring of 2016 (Sanjam Garg)
• in Spring of 2020 (Shafi Goldwasser and Vinod Vaikuntanathan)
• in Spring of 2018 (Sanjam Garg)
• in Fall of 2020 (Alessandro Chiesa)
• in Spring of 2019 (Alessandro Chiesa)
• in Spring of 2017 (Alessandro Chiesa and Igor Shinkar)
• in Fall of 2016 (Alessandro Chiesa and Igor Shinkar)
• in Spring of 2006 (Richard Karp)
• in Fall of 2021 (Avishay Tal)
• in Spring of 2022 (Umesh Vazirani)
• in Fall of 2010 (Elchanan Mossel)
• in Fall of 2020 (Christian Borgs)
• in Spring of 2023 (Shafi Goldwasser and Dawn Song)
• in Fall of 2019 (Umesh Vazirani)
• in Fall of 2016 (Umesh Vazirani)
• in Fall of 2011 (Umesh Vazirani)
• in Spring of 2009 (Umesh Vazirani)
• in Spring of 2007 (Umesh Vazirani)
• in Fall of 2004 (Umesh Vazirani)
• in Spring of 2021 (Christian Borgs)
• in Fall of 2022 (Venkatesan Guruswami)
• in Fall of 2022 (Prasad Raghavendra)
• in Fall of 2021 (Umesh Vazirani)
• in Spring of 2023 (Fermi Ma and Umesh Vazirani )
• in Spring of 2023 (Avishay Tal)
• in Spring of 2020 (Avishay Tal)
• in Fall of 2013 (Gil Kalai)
• in Fall of 2011 (Christos Papadimitriou)
• in Fall of 2009 (Christos Papadimitriou)
• in Fall of 2007 (Christos Papadimitriou)
• in Fall of 2005 (Elchanan Mossel)
• in Spring of 2019 (Yun Song)
• in Spring of 2015 (Yun Song)
• in Fall of 2011 (Yun Song)
• Christian Borgs
• Jennifer Chayes
• Alessandro Chiesa
• Sanjam Garg
• Shafi Goldwasser
• Venkatesan Guruswami
• Nika Haghtalab
• Moritz Hardt
• Richard Karp
• Jelani Nelson
• Prasad Raghavendra
• Jonathan Shewchuk
• Alistair Sinclair
• Nikhil Srivastava
• Jacob Steinhardt
• Bernd Sturmfels
• Avishay Tal
• Umesh Vazirani
• John Wright

Affiliated Faculty

• Venkat Anantharam
• Thomas Courtade
• Jiantao Jiao
• Martin Wainwright
• Abhishek Jain
• Meryem Essaidi
• William Hoza
• Michael Kim
• Andrea Lincoln
• Sai Sandeep
• Hsin-Po Wang
• Ishaq Aden-Ali
• Omar Alrabiah
• James Bartusek
• Thiago Bergamaschi
• Jaiden Fairoze
• Louis Golowich
• Lucas Gretta
• Meghal Gupta
• Christian Ikeokwu
• Meena Jagadeesan
• Malvika Joshi
• Tarun Kathuria
• Seri Khoury
• Rachel Lawrence
• Yunchao Liu
• Jarrod Millman
• Sidhanth Mohanty
• Orr Paradise
• Angelos Pelecanos
• Guru-Vamsi Policharla
• Bhaskar Roberts
• Jonathan Shafer
• Abhishek Shetty
• Sriram Sridhar
• Francisca Vasconcelos
• Elizabeth Yang
• Yinuo Zhang

Recent alumni

• Jonah Brown-Cohen
• Arun Ganesh
• Fotis Iliopoulos
• Marc Khoury
• Jingcheng Liu
• Pasin Manurangsi
• Peihan Miao
• Chinmay Nirkhe
• Aaron Schild
• Nick Spooner
• Akshayaram Srinivasan
• Qiuyi Zhang

Computer Science 152/252: CS152 Computer Architecture and Engineering CS252 Graduate Computer Architecture

Spring 2020, prof. krste asanović, tas: albert ou and yue dai.

Welcome to the Spring 2020 CS152 and CS252 web page. This semester the undergraduate and graduate computer architecture classes will be sharing lectures, and so the course web page has been combined.

CS152 is intended to provide a foundation for students interested in performance programming, compilers, and operating systems, as well as computer architecture and engineering. Our goal is for you to better understand how software interacts with hardware, and to understand how trends in technology, applications, and economics drive continuing changes in the field. The course will cover the different forms of parallelism found in applications (instruction-level, data-level, thread-level, gate-level) and how these can be exploited with various architectural features. We will cover pipelining, superscalar, speculative and out-of-order execution, vector machines, VLIW machines, multithreading, graphics processing units, and parallel microprocessors. We will also explore the design of memory systems including caches, virtual memory, and DRAM. An important part of CS152 is series of lab assignments using real microprocessor designs implemented in the Chisel hardware description language, and running as simulators and FPGA emulators hosted in the Amazon cloud (FireSim) . These simulators will give you an in-depth look at a variety of processor architectural techniques. Our objective is that you will understand all the major concepts used in modern microprocessors by the end of the semester.

CS252 is intended to provide essential background for students intending to pursue research in computer architecture or related fields, and also provides preparation for the Berkeley EECS computer architecture oral prelim examination. An important part of CS252 is reading and discussion of classic architecture papers, as well as a substantial course project.

Course Calendar with Handouts

This page uses the Holy Grail Liquid-Layout: No quirks mode by Matthew James Taylor .

Email: sanjayss AT berkeley DOT edu

• Equity & Inclusion

Graduate Education

Berkeley offers a variety of opportunities for graduate students, including master's programs and PhD programs with interdisciplinary designated emphases in data science.

Computational Biology

Ph.D. in Computational Biology

The main objective of the Computational Biology Ph.D. is to train the next generation of scientists who are passionate about exploring the interface of computation and biology and committed to functioning at a high level in both computational and biological fields.

Designated Emphasis in Computational and Genomic Biology

The Designated Emphasis is a specialization offered adjunct to affiliated doctoral degrees for students with research interests in computational biology and genomics. DE students receive a solid foundation in the different facets of computational/genomic research and the ensuing competitive edge for the most desirable jobs in academia and industry, which increasingly require interdisciplinary training.

Computational Precision Health

Ph.D. in Computational Precision Health

Students in the Ph.D. in Computational Precision Health develop skills and expertise in both the computational sciences (machine learning and AI, natural language processing, statistical inference and modeling, data standards, parallel computing and data at scale, etc.) and health sciences (clinical decision sciences and cognitive informatics, clinical delivery, clinical research, implementation science, health information policy, etc.).

Designated Emphasis in Computational Precision Health

Current UCSF and UC Berkeley PhD students from affiliated programs can incorporate CPH courses and advising into their Ph.D. Designated Emphasis students will receive a solid grounding in the fundamentals of computational precision health, with training in the application of computation to the practice of medicine and public health. 

Electrical Engineering and Computer Sciences

Master of Engineering (M.Eng.)

The Master of Engineering program offers innovative graduate courses on scientific and technical topics, organized by technical concentrations that match student interest.

Master of Science (M.S.) and PhD in Electrical Engineering or Computer Science  

The Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) programs emphasize research preparation and experience. 

Joint Bachelors/Masters (5th Year M.S.)

This program is available only to Berkeley EECS and CS L&S Undergraduates. It is a five year combined Bachelor/Master's program geared toward outstanding and highly motivated students who desire a program of study that offers greater breadth than is practical in the B.S. or B.A. programs alone.

Master of Arts (M.A.) in Statistics

Professional master's program candidates are engaged in a full-time program for one year (with a possibility of a third semester depending on circumstances). The program is designed to prepare students for careers in industries that require statistical skills.

Ph.D. in Statistics

The Statistics Ph.D. program welcomes students from a broad range of theoretical, applied, and interdisciplinary backgrounds, and provides rigorous preparation for a future career in statistics, probability, or data science. Students in the Ph.D. program take core courses on the theory and application of probability and statistics during their first year.

Designated Emphasis in Computational and Data Science and Engineering

The College of Computing, Data Science, and Society (CDSS) sponsors a Designated Emphasis in Computational and Data Science and Engineering, a program committed to the development of new curricula and expanded programs aimed at the development and use of numerical and computational tools to further research across multiple disciplines.

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Berkeley Berkeley Academic Guide: Academic Guide 2023-24

Electrical engineering and computer sciences.

About the Program

The Department of Electrical Engineering and Computer Sciences offers three graduate programs in Electrical Engineering: the Master of Engineering (MEng) in Electrical Engineering and Computer Sciences, the Master of Science (MS), and the Doctor of Philosophy (PhD).

Master of Engineering (MEng)

The Master of Engineering (MEng) in Electrical Engineering & Computer Sciences, first offered by the EECS Department in the 2011-2012 academic year, is a professional master’s with a larger tuition than our other programs and is for students who plan to join the engineering profession immediately following graduation. This accelerated program is designed to train professional engineering leaders who understand the technical, economic, and social issues around technology. The interdisciplinary experience spans one academic year and includes three major components: (1) an area of technical concentration, (2) courses in leadership skills, and (3) a rigorous capstone project experience. 

Master of Science (MS)

The Master of Science (MS) emphasizes research preparation and experience and, for most students, provides an opportunity to lay the groundwork for pursuing a PhD.

Doctor of Philosophy (PhD)

The Berkeley PhD in EECS combines coursework and original research with some of the finest EECS faculty in the US, allowing students to prepare for careers in academia or industry. Our alumni  have gone on to hold amazing positions around the world.

Visit Department Website

Admission to the University

Applying for graduate admission.

Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. A complete list of graduate academic departments, degrees offered, and application deadlines can be found on the Graduate Division website .

Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application can be found on the Graduate Division website .

Admission Requirements

The minimum graduate admission requirements are:

A bachelor’s degree or recognized equivalent from an accredited institution;

A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and

Enough undergraduate training to do graduate work in your chosen field.

For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page . It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here .

Where to apply?

Visit the Berkeley Graduate Division application page .

Admission to the MS/PhD Program

The following items are required for admission to the Berkeley EECS MS/PhD program in addition to the University’s general graduate admission requirements:

• Statement of Purpose: Why are you applying to this program? What do you hope to accomplish during this degree program? What do you want to do afterwards, and how will this degree help you to achieve your goals?
• Personal History Statement: What past experiences made you decide to go into this field? How will your personal history help you succeed in this program and reach your future goals?
• GPA: If you attended a university outside of the USA, please leave the GPA section blank.
• Resume: Please also include a full resume/CV listing your experience and education.

Complete the online UC Berkeley graduate application:

• Start your application through this link and fill in each relevant page.
• Upload the materials above, and send the recommender links several weeks prior to the application deadline to give your recommenders time to submit their letters.

Doctoral Degree Requirements

Normative time requirements, total normative time.

Normative time in the EECS department is between 5.5-6 years for the doctoral program.

Time to Advancement

The faculty of the College of Engineering recommends a minimum number of courses taken while in graduate standing. The total minimum is 24 units of coursework, taken for a letter grade and not including courses numbered 297, 298, 299, 301, 302, 375 and 602. 

Preliminary Exams

The EECS preliminary requirement consists of two components:

Oral Examination

The oral exam serves an advisory role in a student's graduate studies, providing official feedback from an exam committee of faculty members. Students must be able to demonstrate an integrated grasp of the exam area's body of knowledge in an unstructured framework. Students must pass the oral portion of the preliminary exam within their first two attempts. A third attempt is possible with a petition of support from the student's faculty adviser and final approval by the prelim committee chair. Failure to pass the oral portion of the preliminary exam will result in the student being ineligible to complete the PhD program. The examining committee awards a score in the range of 0-10. The minimum passing score is 6.0.

Breadth Courses

The prelim breadth courses ensure that students have exposure to areas outside their concentration.

EE students are expected to complete two courses of at least three units each in two areas of EECS outside their prelim oral exam area. These courses must be graduate or advanced undergraduate courses, and students must receive a grade of A- or better.

Qualifying Examination

The qualifying examination is an important checkpoint, meant to show that a student is on a promising research track toward the PhD. It is a University examination, administered by the Graduate Council, with the specific purpose of demonstrating that "the student is clearly an expert in those areas of the discipline that have been specified for the examination, and that he or she can, in all likelihood, design and produce an acceptable dissertation." Despite such rigid criteria, faculty examiners recognize that the level of expertise expected is that appropriate for a third year graduate student who may be only in the early stages of a research project.

The EECS department offers the qualifying exam in two formats, A or B. Students may choose the exam type of their choice after consultation with their advisor.

• Students prepare a write-up and presentation, summarizing a specific research area, preferably the one in which they intend to do their dissertation work. Their summary surveys that area and describes open and interesting research problems.
• They describe why they chose these problems and indicate what direction their research may take in the future.
• They prepare to display expertise on both the topic presented and on any related material that the committee thinks is relevant.
• The student should talk (at least briefly) about any research progress to date (e.g., MS project, PhD research, or class project). Some evidence of their ability to do research is expected.
• The committee shall evaluate students on the basis of their comprehension of the fundamental facts and principles that apply within their research area and the student’s ability to think incisively and critically about the theoretical and practical aspects of this field.
•  Students must demonstrate command of the content and the ability to design and produce an acceptable dissertation.

This option includes the presentation and defense of a thesis proposal in addition to the requirements of option A. It will include a summary of research to date and plans for future work (or at least the next stage thereof). The committee shall not only evaluate the student's thesis proposal and his/her progress to date, but shall also evaluate according to option A. As in option A, the student should prepare a single document and presentation, but in this case, additional emphasis must be placed on research completed to date and plans for the remainder of the dissertation research.

Thesis Proposal Defense

Students not presenting a satisfactory thesis proposal defense, either because they took option A for the QE, or because the material presented in an option B exam was not deemed a satisfactory proposal defense (although it may have sufficed to pass the QE), must write up and present a thesis proposal, which should include a summary of the research to date and plans for the remainder of the dissertation research. They should be prepared to discuss background and related areas, but the focus of the proposal should be on the progress made so far, and detailed plans for completing the thesis. The standard for continuing with PhD research is that the proposal has sufficient merit to lead to a satisfactory dissertation. Another purpose of this presentation is for faculty to provide feedback on the quality of work to date. For this step, the committee should consist of at least three members from EECS familiar with the research area, preferably including those on the dissertation committee.

Normative Time in Candidacy

Advancement to candidacy.

Students must file the advancement form online through CalCentral no later than the end of the semester following the one in which the qualifying exam was passed. In approving this application, Graduate Division approves the dissertation committee and will send a certificate of candidacy.

Students in the EECS department are required to be advanced to candidacy at least two semesters before they are eligible to graduate.  Once a student is advanced to candidacy, candidacy is valid for five years.  For the first three years, non-resident tuition may be waived, if applicable.

Dissertation Talk

As part of the requirements for the doctoral degree, students must give a public talk on the research covered by their dissertation. The dissertation talk should be given a few months before the signing of the final submission of the dissertation.  It must be given before the final submission of the dissertation.  The talk should cover all the major components of the dissertation in a substantial manner; in particular, the dissertation talk should not omit topics that will appear in the dissertation but are incomplete at the time of the talk.

The dissertation talk is to be attended by the whole dissertation committee, or, if this is not possible, by at least a majority of the members. Attendance at this talk is part of the committee's responsibility. It is, however, the responsibility of the student to schedule a time for the talk that is convenient for members of the committee.  

Required Professional Development

Graduate student instructor teaching requirement.

The department requires all PhD candidates to serve as graduate student instructors (GSIs) within the EECS department. The GSI teaching requirement not only helps to develop a student's communication skills, but it also makes a great contribution to the department's academic community. Students must fulfill this requirement by working as a GSI (excluding EL ENG 375 , or COMPSCI 375 ) for a total of 30 hours minimum prior to graduation. At least 20 of those hours must be for an EE or CS undergraduate course. In addition, students must earn a Satisfactory grade in the mandatory pedagogy course to complete the GSI teaching requirement.

Master's Degree Requirements (MS)

Unit requirements.

A minimum of 24 units is required.

All courses must be taken for a letter grade, except courses numbered  299, which are only offered for S/U  credit.

Students must maintain a minimum cumulative GPA of 3.0. No credit will be given for courses in which the student earns a grade of D+ or below.

Transfer credit may be awarded for a maximum of 4 semester or 6 quarter units of graduate coursework from another institution.

For both Plan I and Plan II, MS students need to complete the departmental Advance to Candidacy form, have their research advisor sign the form, and submit the form to the Department's Master's Degree Advisor. Students who choose Plan I will also need to complete the Graduate Division's online Advancement to Candidacy form through  Calcentral  no later than the end of the second week of classes in their final semester. 

Once a student has advanced to candidacy, candidacy is valid for three years.

Capstone/Thesis (Plan I)

Students planning to use Plan I for their MS Degree will need to follow the  Graduate Division's “Thesis Filing Guidelines."  A copy of the signature page and abstract should be submitted to the Department's Master's Degree Advisor.  In addition, a copy should be uploaded to  the EECS website .

Capstone/Master's Project (Plan II)

Students planning to use Plan II for their MS Degree will need to produce an MS Plan II Title/Signature Page. A copy of the signature page and abstract should be submitted to the the Department's Master's Degree Advisor. In addition, a copy should be uploaded to  the EECS website .

There is no special formatting required for the body of the Plan II MS report, unlike the Plan I MS thesis, which must follow Graduate Division guidelines.

Master's Degree Requirements (MEng)

Unit requirements.

The minimum number of units to complete the degree is 25 semester units.

Students will join a team of three to five students, working on a specific problem or opportunity that can be addressed by technology and gaining direct experience in applying the skills learned in leadership courses.

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Computer science.

• Electrical Engineering

EECS C206A Introduction to Robotics 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course is an introduction to the field of robotics. It covers the fundamentals of kinematics, dynamics, control of robot manipulators, robotic vision, sensing, forward & inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics, & control. We will present techniques for geometric motion planning & obstacle avoidance. Open problems in trajectory generation with dynamic constraints will also be discussed. The course also presents the use of the same analytical techniques as manipulation for the analysis of images & computer vision. Low level vision, structure from motion, & an introduction to vision & learning will be covered. The course concludes with current applications of robotics. Introduction to Robotics: Read More [+]

Rules & Requirements

Prerequisites: Familiarity with linear algebra at level of EECS 16A / EECS 16B or MATH 54 . Experience doing coding in python at the level of COMPSCI 61A . Preferred: experience developing software at level of COMPSCI 61B and experience using Linux. EECS 120 is not required, but some knowledge of linear systems may be helpful for the control of robots

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and three hours of laboratory per week.

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructors: Sastry, Sreenath

Formerly known as: Electrical Engin and Computer Sci 206A

Also listed as: MEC ENG C206A

Introduction to Robotics: Read Less [-]

EECS C206B Robotic Manipulation and Interaction 4 Units

Terms offered: Spring 2024, Spring 2023 This course is a sequel to EECS C106A /206A, which covers kinematics, dynamics and control of a single robot. This course will cover dynamics and control of groups of robotic manipulators coordinating with each other and interacting with the environment. Concepts will include an introduction to grasping and the constrained manipulation, contacts and force control for interaction with the environment. We will also cover active perception guided manipulation, as well as the manipulation of non-rigid objects. Throughout, we will emphasize design and human-robot interactions, and applications to applications in manufacturing, service robotics, tele-surgery, and locomotion. Robotic Manipulation and Interaction: Read More [+]

Prerequisites: Students are expected to have taken EECS C106A / BioE C106A / ME C106A / ME C206A/ EECS C206A or an equivalent course. A strong programming background, knowledge of Python and Matlab, and some coursework in feedback controls (such as EE C128 / ME C134) are also useful. Students who have not taken EECS C106A / BioE C106A / ME C106A / ME C206A/ EECS C206A should have a strong programming background, knowledge of Python and Matlab, and exposure to linear algebra, and Lagrangian dynamics

Instructors: Bajcsy, Sastry

Formerly known as: Electrical Engin and Computer Sci 206B

Also listed as: MEC ENG C206B

Robotic Manipulation and Interaction: Read Less [-]

EECS 208 Computational Principles for High-dimensional Data Analysis 4 Units

Terms offered: Fall 2023, Fall 2022, Fall 2021 Introduction to fundamental geometric and statistical concepts and principles of low-dimensional models for high-dimensional signal and data analysis, spanning basic theory, efficient algorithms, and diverse real-world applications. Systematic study of both sampling complexity and computational complexity for sparse, low-rank, and low-dimensional models – including important cases such as matrix completion, robust principal component analysis, dictionary learning, and deep networks. Computational Principles for High-dimensional Data Analysis: Read More [+]

Prerequisites: The following courses are recommended undergraduate linear algebra (Math 110), statistics (Stat 134), and probability (EE126). Back-ground in signal processing (ELENG 123), optimization (ELENG C227T), machine learning (CS189/289), and computer vision ( COMPSCI C280 ) may allow you to appreciate better certain aspects of the course material, but not necessary all at once. The course is open to senior undergraduates, with consent from the instructor

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week.

Instructor: Ma

Computational Principles for High-dimensional Data Analysis: Read Less [-]

EECS 219A Numerical Simulation and Modeling 4 Units

Terms offered: Spring 2024 Numerical simulation and modeling are enabling technologies that pervade science and engineering. This course provides a detailed introduction to the fundamental principles of these technologies and their translation to engineering practice. The course emphasizes hands-on programming in MATLAB and application to several domains, including circuits, nanotechnology, and biology. Numerical Simulation and Modeling: Read More [+]

Prerequisites: Consent of instructor; a course in linear algebra and on circuits is very useful

Credit Restrictions: Students will receive no credit for EL ENG 219A after completing EL ENG 219.

Fall and/or spring: 15 weeks - 4 hours of lecture per week

Additional Format: Four hours of lecture per week.

Instructor: Roychowdhury

Formerly known as: Electrical Engineering 219A

Numerical Simulation and Modeling: Read Less [-]

EECS 219C Formal Methods: Specification, Verification, and Synthesis 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Introduction to the theory and practice of formal methods for the design and analysis of systems, with a focus on algorithmic techniques. Covers selected topics in computational logic and automata theory including modeling and specification formalisms, temporal logics, satisfiability solving, model checking, synthesis, learning, and theorem proving. Applications to software and hardware design, cyber-physical systems, robotics, computer security , and other areas will be explored as time permits. Formal Methods: Specification, Verification, and Synthesis: Read More [+]

Prerequisites: Graduate standing or consent of instructor; COMPSCI 170 is recommended

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week.

Instructor: Seshia

Formerly known as: Electrical Engineering 219C

Formal Methods: Specification, Verification, and Synthesis: Read Less [-]

EECS 225A Statistical Signal Processing 3 Units

Terms offered: Spring 2023, Fall 2021, Fall 2020 This course connects classical statistical signal processing (Hilbert space filtering theory by Wiener and Kolmogorov, state space model, signal representation, detection and estimation, adaptive filtering) with modern statistical and machine learning theory and applications. It focuses on concrete algorithms and combines principled theoretical thinking with real applications. Statistical Signal Processing: Read More [+]

Prerequisites: EL ENG 120 and EECS 126

Additional Format: Three hours of Lecture per week for 15 weeks.

Instructors: Jiao, Waller

Formerly known as: Electrical Engineering 225A

Statistical Signal Processing: Read Less [-]

EECS 225B Digital Image Processing 3 Units

Terms offered: Fall 2023, Fall 2022, Fall 2020 This course deals with computational methods as applied to digital imagery. It focuses on image sensing and acquisition, image sampling and quantization; spatial transformation, linear and nonlinear filtering; introduction to convolutional neural networks, and GANs; applications of deep learning methods to image processing problems; image enhancement, histogram equalization, image restoration, Weiner filtering, tomography, image reconstruction from projections and partial Fourier information, Radon transform, multiresolution analysis, continuous and discrete wavelet transform and computation, subband coding, image and video compression, sparse signal approximation, dictionary techniques, image and video compression standards, and more. Digital Image Processing: Read More [+]

Prerequisites: Basic knowledge of signals and systems, convolution, and Fourier Transform

Instructor: Zakhor

Formerly known as: Electrical Engineering 225B

Digital Image Processing: Read Less [-]

EECS 227AT Optimization Models in Engineering 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems, such as linear or constrained least-squares optimization. Optimization Models in Engineering: Read More [+]

Prerequisites: MATH 54 or consent of instructor

Credit Restrictions: Students will receive no credit for EECS 227AT after taking EECS 127 or Electrical Engineering 127/227AT.

Instructor: El Ghaoui

Formerly known as: Electrical Engineering 227AT

Optimization Models in Engineering: Read Less [-]

EECS C249B Cyber Physical System Design Prinicples and Applications 4 Units

Terms offered: Spring 2020, Spring 2019, Spring 2016 Principles of embedded system design. Focus on design methodologies and foundations. Platform-based design and communication-based design and their relationship with design time, re-use, and performance. Models of computation and their use in design capture, manipulation, verification, and synthesis. Mapping into architecture and systems platforms. Performance estimation. Scheduling and real-time requirements. Synchronous languages and time-triggered protocols to simplify the design process. Cyber Physical System Design Prinicples and Applications: Read More [+]

Prerequisites: Suggested but not required: CS170, EECS149/249A

Credit Restrictions: Students will receive no credit for EECS C249B after completing EL ENG 249, or EECS 249B. A deficient grade in EECS C249B may be removed by taking EECS 249B.

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 2 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and two hours of laboratory per week.

Instructor: Sangiovanni-Vincentelli

Formerly known as: Electrical Engineering C249B/Civil and Environmental Engineering C289

Also listed as: CIV ENG C289

Cyber Physical System Design Prinicples and Applications: Read Less [-]

EECS 251A Introduction to Digital Design and Integrated Circuits 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 An introduction to digital circuit and system design. The material provides a top-down view of the principles, components, and methodologies for large scale digital system design. The underlying CMOS devices and manufacturing technologies are introduced, but quickly abstracted to higher levels to focus the class on design of larger digital modules for both FPGAs (field programmable gate arrays) and ASICs (application specific integrated circuits). The class includes extensive use of industrial grade design automation and verification tools for assignments, labs, and projects. Introduction to Digital Design and Integrated Circuits: Read More [+]

Objectives & Outcomes

Course Objectives: The Verilog hardware description language is introduced and used. Basic digital system design concepts, Boolean operations/combinational logic, sequential elements and finite-state-machines, are described. Design of larger building blocks such as arithmetic units, interconnection networks, input/output units, as well as memory design (SRAM, Caches, FIFOs) and integration are also covered. Parallelism, pipelining and other micro-architectural optimizations are introduced. A number of physical design issues visible at the architecture level are covered as well, such as interconnects, power, and reliability.

Student Learning Outcomes: Although the syllabus is the same as EECS151, the assignments and exams for EECS251A will have harder problems that test deeper understanding expected from a graduate level course.

Prerequisites: EECS 16A and EECS 16B ; COMPSCI 61C ; and recommended: EL ENG 105 . Students must enroll concurrently in at least one the laboratory flavors EECS 251LA or EECS 251LB . Students wishing to take a second laboratory flavor next term can sign-up only for that laboratory section and receive a letter grade. The prerequisite for “Lab-only” enrollment that term will be EECS 251A from previous terms

Credit Restrictions: Students must enroll concurrently in at least one the laboratory flavors Electrical Engineering and Computer Science 251LA or Electrical Engineering and Computer Science 251LB. Students wishing to take a second laboratory flavor next term can sign-up only for that laboratory section and receive a letter grade. The pre-requisite for “Lab-only” enrollment that term will be Electrical Engineering and Computer Science 251A from previous terms.

Instructors: Stojanovic, Wawrzynek

Formerly known as: Electrical Engineering 241A

Introduction to Digital Design and Integrated Circuits: Read Less [-]

EECS 251B Advanced Digital Integrated Circuits and Systems 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course aims to convey a knowledge of advanced concepts of digital circuit and system-on-a-chip design in state-of-the-art technologies. Emphasis is on the circuit and system design and optimization for both energy efficiency and high performance for use in a broad range of applications, from edge computing to datacenters. Special attention will be devoted to the most important challenges facing digital circuit designers in the coming decade. The course is accompanied with practical laboratory exercises that introduce students to modern tool flows. Advanced Digital Integrated Circuits and Systems: Read More [+]

Prerequisites: Introduction to Digital Design and Integrated Circuits, EECS151 (taken with either EECS151LA or EECS151LB lab) or EECS251A (taken with either EECS251LA or EECS251LB lab)

Credit Restrictions: Students will receive no credit for EECS 251B after completing COMPSCI 250 , or EL ENG 241B .

Fall and/or spring: 15 weeks - 4 hours of lecture and 1 hour of discussion per week

Additional Format: Four hours of lecture and one hour of discussion per week.

Instructors: Nikolić, Shao, Wawrzynek, Asanović, Stojanović, Seshia

Advanced Digital Integrated Circuits and Systems: Read Less [-]

EECS 251LA Introduction to Digital Design and Integrated Circuits Lab 2 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This lab lays the foundation of modern digital design by first presenting the scripting and hardware description language base for specification of digital systems and interactions with tool flows. The labs are centered on a large design with the focus on rapid design space exploration. The lab exercises culminate with a project design, e.g. implementation of a 3-stage RISC-V processor with a register file and caches. The design is mapped to simulation and layout specification. Introduction to Digital Design and Integrated Circuits Lab: Read More [+]

Course Objectives: Software testing of digital designs is covered leading to a set of exercises that cover the design flow. Digital synthesis, floor-planning, placement and routing are covered, as well as tools to evaluate timing and power consumption. Chip-level assembly is covered, including instantiation of custom blocks: I/O pads, memories, PLLs, etc.

Student Learning Outcomes: Although the syllabus is the same as EECS151LA, the assignments and exams for EECS251LA will have harder problems in labs and in the project that test deeper understanding expected from a graduate level course.

Prerequisites: EECS 16A , EECS 16B , and COMPSCI 61C ; and EL ENG 105 is recommended

Fall and/or spring: 15 weeks - 3 hours of laboratory per week

Additional Format: Three hours of laboratory per week.

Introduction to Digital Design and Integrated Circuits Lab: Read Less [-]

EECS 251LB Introduction to Digital Design and Integrated Circuits Lab 2 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This lab covers the design of modern digital systems with Field-Programmable Gate Array (FPGA) platforms. A series of lab exercises provide the background and practice of digital design using a modern FPGA design tool flow. Digital synthesis, partitioning, placement, routing, and simulation tools for FPGAs are covered in detail. The labs exercises culminate with a large design project, e.g., an implementation of a full 3-stage RISC-V processor system, with caches, graphics acceleration, and external peripheral components. The design is mapped and demonstrated on an FPGA hardware platform. Introduction to Digital Design and Integrated Circuits Lab: Read More [+]

Student Learning Outcomes: Although the syllabus is the same as EECS151LB, the assignments and exams for EECS251LB will have harder problems in labs and in the project that test deeper understanding expected from a graduate level course.

COMPSCI C200A Principles and Techniques of Data Science 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023, Spring 2023, Spring 2022, Spring 2021, Spring 2020 Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project. Principles and Techniques of Data Science: Read More [+]

Prerequisites: COMPSCI C8 / INFO C8 / STAT C8 or ENGIN 7 ; and either COMPSCI 61A or COMPSCI 88. Corequisites: MATH 54 or EECS 16A

Credit Restrictions: Students will receive no credit for DATA C200 \ COMPSCI C200A \ STAT C200C after completing DATA C100 .

Fall and/or spring: 8 weeks - 6-6 hours of lecture, 2-2 hours of discussion, and 0-2 hours of laboratory per week 15 weeks - 3-3 hours of lecture, 1-1 hours of discussion, and 0-1 hours of laboratory per week

Summer: 8 weeks - 6-6 hours of lecture, 2-2 hours of discussion, and 0-2 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and zero to one hours of laboratory per week. Six hours of lecture and two hours of discussion and zero to two hours of laboratory per week for 8 weeks. Six hours of lecture and two hours of discussion and zero to two hours of laboratory per week for 8 weeks.

Subject/Course Level: Computer Science/Graduate

Formerly known as: Statistics C200C/Computer Science C200A

Also listed as: DATA C200/STAT C200C

Principles and Techniques of Data Science: Read Less [-]

COMPSCI C249A Introduction to Embedded Systems 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course introduces students to the basics of models, analysis tools, and control for embedded systems operating in real time. Students learn how to combine physical processes with computation. Topics include models of computation, control, analysis and verification, interfacing with the physical world, mapping to platforms, and distributed embedded systems. The course has a strong laboratory component, with emphasis on a semester-long sequence of projects. Introduction to Embedded Systems: Read More [+]

Credit Restrictions: Students will receive no credit for Electrical Engineering/Computer Science C249A after completing Electrical Engineering/Computer Science C149.

Fall and/or spring: 15 weeks - 3 hours of lecture and 3 hours of laboratory per week

Additional Format: Three hours of lecture and three hours of laboratory per week.

Instructors: Lee, Seshia

Formerly known as: Electrical Engineering C249M/Computer Science C249M

Also listed as: EL ENG C249A

Introduction to Embedded Systems: Read Less [-]

COMPSCI 250 VLSI Systems Design 4 Units

Terms offered: Fall 2020, Spring 2017, Spring 2016 Unified top-down and bottom-up design of integrated circuits and systems concentrating on architectural and topological issues. VLSI architectures, systolic arrays, self-timed systems. Trends in VLSI development. Physical limits. Tradeoffs in custom-design, standard cells, gate arrays. VLSI design tools. VLSI Systems Design: Read More [+]

Prerequisites: COMPSCI 150

Fall and/or spring: 15 weeks - 3 hours of lecture and 4 hours of laboratory per week

Additional Format: Three hours of lecture and four hours of laboratory per week.

Instructor: Wawrzynek

VLSI Systems Design: Read Less [-]

COMPSCI 252A Graduate Computer Architecture 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Graduate survey of contemporary computer organizations covering: early systems, CPU design, instruction sets, control, processors, busses, ALU, memory, I/O interfaces, connection networks, virtual memory, pipelined computers, multiprocessors, and case studies. Term paper or project is required. Graduate Computer Architecture: Read More [+]

Prerequisites: COMPSCI 61C

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week

Additional Format: Three hours of lecture and two hours of discussion per week.

Instructors: Asanović, Kubiatowicz

Formerly known as: Computer Science 252

Graduate Computer Architecture: Read Less [-]

COMPSCI 260A User Interface Design and Development 4 Units

Terms offered: Spring 2024, Spring 2023, Fall 2020 The design, implementation, and evaluation of user interfaces. User-centered design and task analysis. Conceptual models and interface metaphors. Usability inspection and evaluation methods. Analysis of user study data. Input methods (keyboard, pointing, touch, tangible) and input models. Visual design principles. Interface prototyping and implementation methodologies and tools. Students will develop a user interface for a specific task and target user group in teams. User Interface Design and Development: Read More [+]

Prerequisites: COMPSCI 61B , COMPSCI 61BL , or consent of instructor

Credit Restrictions: Students will receive no credit for Computer Science 260A after taking Computer Science 160.

Instructors: Agrawala, Canny, Hartmann

User Interface Design and Development: Read Less [-]

COMPSCI 260B Human-Computer Interaction Research 3 Units

Terms offered: Fall 2024, Fall 2017 This course is a broad introduction to conducting research in Human-Computer Interaction. Students will become familiar with seminal and recent literature; learn to review and critique research papers; re-implement and evaluate important existing systems; and gain experience in conducting research. Topics include input devices, computer-supported cooperative work, crowdsourcing, design tools, evaluation methods, search and mobile interfaces, usable security , help and tutorial systems. Human-Computer Interaction Research: Read More [+]

Prerequisites: COMPSCI 160 recommended, or consent of instructor

Instructor: Hartmann

Human-Computer Interaction Research: Read Less [-]

COMPSCI 261 Security in Computer Systems 3 Units

Terms offered: Fall 2023, Spring 2021, Fall 2018 Graduate survey of modern topics in computer security, including protection, access control, distributed access security, firewalls, secure coding practices, safe languages, mobile code, and case studies from real-world systems. May also cover cryptographic protocols, privacy and anonymity, and/or other topics as time permits. Security in Computer Systems: Read More [+]

Prerequisites: COMPSCI 162

Instructors: D. Song, Wagner

Security in Computer Systems: Read Less [-]

COMPSCI 261N Internet and Network Security 4 Units

Terms offered: Spring 2020, Fall 2016, Spring 2015 Develops a thorough grounding in Internet and network security suitable for those interested in conducting research in the area or those more broadly interested in security or networking. Potential topics include denial-of-service; capabilities; network intrusion detection/prevention; worms; forensics; scanning; traffic analysis; legal issues; web attacks; anonymity; wireless and networked devices; honeypots; botnets; scams; underground economy; attacker infrastructure; research pitfalls. Internet and Network Security: Read More [+]

Prerequisites: EL ENG 122 or equivalent; and COMPSCI 161 or familiarity with basic security concepts

Instructor: Paxson

Internet and Network Security: Read Less [-]

COMPSCI 262A Advanced Topics in Computer Systems 4 Units

Terms offered: Fall 2023, Fall 2022, Fall 2021 Graduate survey of systems for managing computation and information, covering a breadth of topics: early systems; volatile memory management, including virtual memory and buffer management; persistent memory systems, including both file systems and transactional storage managers; storage metadata, physical vs. logical naming, schemas, process scheduling, threading and concurrency control; system support for networking, including remote procedure calls, transactional RPC, TCP, and active messages; security infrastructure; extensible systems and APIs; performance analysis and engineering of large software systems. Homework assignments, exam, and term paper or project required. Advanced Topics in Computer Systems: Read More [+]

Prerequisites: COMPSCI 162 and entrance exam

Instructors: Brewer, Hellerstein

Formerly known as: 262

Advanced Topics in Computer Systems: Read Less [-]

COMPSCI 262B Advanced Topics in Computer Systems 3 Units

Terms offered: Spring 2020, Spring 2009, Fall 2008 Continued graduate survey of large-scale systems for managing information and computation. Topics include basic performance measurement; extensibility, with attention to protection, security, and management of abstract data types; index structures, including support for concurrency and recovery; parallelism, including parallel architectures, query processing and scheduling; distributed data management, including distributed and mobile file systems and databases; distributed caching; large-scale data analysis and search. Homework assignments, exam, and term paper or project required. Advanced Topics in Computer Systems: Read More [+]

Prerequisites: COMPSCI 262A

Instructors: Brewer, Culler, Hellerstein, Joseph

COMPSCI 263 Design of Programming Languages 3 Units

Terms offered: Fall 2021, Fall 2019, Spring 2019 Selected topics from: analysis, comparison, and design of programming languages, formal description of syntax and semantics, advanced programming techniques, structured programming, debugging, verification of programs and compilers, and proofs of correctness. Design of Programming Languages: Read More [+]

Prerequisites: COMPSCI 164

Instructor: Necula

Design of Programming Languages: Read Less [-]

COMPSCI 264 Implementation of Programming Languages 4 Units

Terms offered: Fall 2023, Fall 2021, Spring 2011 Compiler construction. Lexical analysis, syntax analysis. Semantic analysis code generation and optimization. Storage management. Run-time organization. Implementation of Programming Languages: Read More [+]

Prerequisites: COMPSCI 164 ; COMPSCI 263 recommended

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 6 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and six hours of laboratory per week.

Instructor: Bodik

Implementation of Programming Languages: Read Less [-]

COMPSCI 265 Compiler Optimization and Code Generation 3 Units

Terms offered: Fall 2024, Fall 2009, Spring 2003 Table-driven and retargetable code generators. Register management. Flow analysis and global optimization methods. Code optimization for advanced languages and architectures. Local code improvement. Optimization by program transformation. Selected additional topics. A term paper or project is required. Compiler Optimization and Code Generation: Read More [+]

Instructor: Sen

Compiler Optimization and Code Generation: Read Less [-]

COMPSCI C267 Applications of Parallel Computers 3 - 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022, Spring 2021 Models for parallel programming. Overview of parallelism in scientific applications and study of parallel algorithms for linear algebra, particles, meshes, sorting, FFT, graphs, machine learning, etc. Survey of parallel machines and machine structures. Programming shared- and distributed-memory parallel computers, GPUs, and cloud platforms. Parallel programming languages, compilers, libraries and toolboxes. Data partitioning techniques. Techniques for synchronization and load balancing. Detailed study and algorithm/program development of medium sized applications. Applications of Parallel Computers: Read More [+]

Prerequisites: No formal pre-requisites. Prior programming experience with a low-level language such as C, C++, or Fortran is recommended but not required. CS C267 is intended to be useful for students from many departments and with different backgrounds, although we will assume reasonable programming skills in a conventional (non-parallel) language, as well as enough mathematical skills to understand the problems and algorithmic solutions presented

Repeat rules: Course may be repeated for credit without restriction.

Fall and/or spring: 15 weeks - 3-3 hours of lecture and 1-1 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of laboratory per week.

Instructors: Demmel, Yelick

Also listed as: ENGIN C233

Applications of Parallel Computers: Read Less [-]

COMPSCI W267 Applications of Parallel Computers 3 Units

Terms offered: Prior to 2007 Parallel programming, from laptops to supercomputers to the cloud. Goals include writing programs that run fast while minimizing programming effort. Parallel architectures and programming languages and models, including shared memory (eg OpenMP on your multicore laptop), distributed memory (MPI and UPC on a supercomputer), GPUs (CUDA and OpenCL), and cloud (MapReduce, Hadoop and Spark). Parallel algorithms and software tools for common computations (eg dense and sparse linear algebra, graphs, structured grids). Tools for load balancing, performance analysis, debugging. How high level applications are built (eg climate modeling). On-line lectures and office hours. Applications of Parallel Computers: Read More [+]

Student Learning Outcomes: An understanding of computer architectures at a high level, in order to understand what can and cannot be done in parallel, and the relative costs of operations like arithmetic, moving data, etc. To master parallel programming languages and models for different computer architectures To recognize programming "patterns" to use the best available algorithms and software to implement them. To understand sources of parallelism and locality in simulation in designing fast algorithms

Prerequisites: Computer Science W266 or the consent of the instructor

Credit Restrictions: Students will receive no credit for Computer Science W267 after completing Computer Science C267.

Fall and/or spring: 15 weeks - 3 hours of web-based lecture per week

Additional Format: Three hours of web-based lecture per week.

Online: This is an online course.

COMPSCI 268 Computer Networks 3 Units

Terms offered: Spring 2023, Spring 2021, Spring 2019 Distributed systems, their notivations, applications, and organization. The network component. Network architectures. Local and long-haul networks, technologies, and topologies. Data link, network, and transport protocols. Point-to-point and broadcast networks. Routing and congestion control. Higher-level protocols. Naming. Internetworking. Examples and case studies. Computer Networks: Read More [+]

Instructors: Joseph, Katz, Stoica

Formerly known as: 292V

Computer Networks: Read Less [-]

COMPSCI 270 Combinatorial Algorithms and Data Structures 3 Units

Terms offered: Fall 2024, Spring 2023, Spring 2021 Design and analysis of efficient algorithms for combinatorial problems. Network flow theory, matching theory, matroid theory; augmenting-path algorithms; branch-and-bound algorithms; data structure techniques for efficient implementation of combinatorial algorithms; analysis of data structures; applications of data structure techniques to sorting, searching, and geometric problems. Combinatorial Algorithms and Data Structures: Read More [+]

Prerequisites: COMPSCI 170

Instructors: Papadimitriou, Rao, Sinclair, Vazirani

Combinatorial Algorithms and Data Structures: Read Less [-]

COMPSCI 271 Randomness and Computation 3 Units

Terms offered: Fall 2024, Fall 2022, Spring 2020 Computational applications of randomness and computational theories of randomness. Approximate counting and uniform generation of combinatorial objects, rapid convergence of random walks on expander graphs, explicit construction of expander graphs, randomized reductions, Kolmogorov complexity, pseudo-random number generation, semi-random sources. Randomness and Computation: Read More [+]

Prerequisites: COMPSCI 170 and at least one course from the following: COMPSCI 270 - COMPSCI 279

Instructor: Sinclair

Randomness and Computation: Read Less [-]

COMPSCI 272 Foundations of Decisions, Learning, and Games 4 Units

Terms offered: Not yet offered This course introduces students to the mathematical foundation of learning in the presence of strategic and societal agency. This is a theory-oriented course that will draw from the statistical and computational foundations of machine learning, computer science, and economics. As a research-oriented course, a range of advanced topics will be explored to paint a comprehensive picture of classical and modern approaches to learning for the purpose of decision making.These topics include foundations of learning, foundations of algorithmic game theory, cooperative and non-cooperative games, equilibria and dynamics, learning in games, information asymmetries, mechanism design, and learning with incentives. Foundations of Decisions, Learning, and Games: Read More [+]

Prerequisites: Graduate-level mathematical maturity, including proof-based graduate-level courses in at least two, but recommended three, of the following categories: Statistics and Probability, e.g., STAT205A, STAT210B Economics, e.g., ECON207A Algorithms, e.g., CS270 Optimization, e.g., EE 227B Control theory, e.g., EE 221A

Credit Restrictions: Students will receive no credit for COMPSCI 272 after completing COMPSCI 272 . A deficient grade in COMPSCI 272 may be removed by taking COMPSCI 272 .

Instructors: Jordan, Haghtalab

Foundations of Decisions, Learning, and Games: Read Less [-]

COMPSCI 273 Foundations of Parallel Computation 3 Units

Terms offered: Spring 2012, Fall 2010, Spring 2009 . Fundamental theoretical issues in designing parallel algorithms and architectures. Shared memory models of parallel computation. Parallel algorithms for linear algegra, sorting, Fourier Transform, recurrence evaluation, and graph problems. Interconnection network based models. Algorithm design techniques for networks like hypercubes, shuffle-exchanges, threes, meshes and butterfly networks. Systolic arrays and techniques for generating them. Message routing. Foundations of Parallel Computation: Read More [+]

Prerequisites: COMPSCI 170 , or consent of instructor

Instructor: Rao

Foundations of Parallel Computation: Read Less [-]

COMPSCI 274 Computational Geometry 3 Units

Terms offered: Spring 2019, Spring 2017, Spring 2015 . Constructive problems in computational geometry: convex hulls, triangulations, Voronoi diagrams, arrangements of hyperplanes; relationships among these problems. Search problems: advanced data structures; subdivision search; various kinds of range searches. Models of computation; lower bounds. Computational Geometry: Read More [+]

Instructor: Shewchuk

Computational Geometry: Read Less [-]

COMPSCI 276 Cryptography 3 Units

Terms offered: Fall 2024, Fall 2020, Fall 2018 Graduate survey of modern topics on theory, foundations, and applications of modern cryptography. One-way functions; pseudorandomness; encryption; authentication; public-key cryptosystems; notions of security. May also cover zero-knowledge proofs, multi-party cryptographic protocols, practical applications, and/or other topics, as time permits. Cryptography: Read More [+]

Instructors: Trevisan, Wagner

Cryptography: Read Less [-]

COMPSCI 278 Machine-Based Complexity Theory 3 Units

Terms offered: Spring 2024, Spring 2021, Fall 2016 Properties of abstract complexity measures; Determinism vs. nondeterminism; time vs. space; complexity hierarchies; aspects of the P-NP question; relative power of various abstract machines. Machine-Based Complexity Theory: Read More [+]

Prerequisites: 170

Instructor: Trevisan

Machine-Based Complexity Theory: Read Less [-]

COMPSCI 280A Intro to Computer Vision and Computational Photography 4 Units

Terms offered: Fall 2024, Fall 2023 This course introduces students to computing with visual data (images and video). We will cover acquisition, representation, and manipulation of visual information from digital photographs (image processing), image analysis and visual understanding (computer vision), and image synthesis (computational photography). Key algorithms will be presented, ranging from classical to contemporary, with an emphasis on using these techniques to build practical systems. The hands-on emphasis will be reflected in the programming assignments, where students will acquire their own images and develop, largely from scratch, image analysis and synthesis tools for real-world applications. Intro to Computer Vision and Computational Photography: Read More [+]

Course Objectives: Students will learn classic algorithms in image manipulation with Gaussian and Laplacian Pyramids, understand the hierarchy of image transformations including homographies, and how to warp an image with these transformations., Students will learn how to apply Convolutional Neural Networks for computer vision problems and how they can be used for image manipulation. Students will learn the fundamentals of 3D vision: stereo, multi-view geometry, camera calibration, structure-frommotion, multi-view stereo, and the plenoptic function mechanics of a pin-hole camera, representation of images as pixels, physics of light and the process of image formation, to manipulating the visual information using signal processing techniques in the spatial and frequency domains.

Student Learning Outcomes: After this class, students will be comfortable implementing, from scratch, these algorithms in modern programming languages and deep learning libraries.

Prerequisites: COMPSCI 61B and MATH 53 . MATH 54 , MATH 56 , MATH 110 , or EECS 16A . COMPSCI 182 or COMPSCI 189

Instructors: Efros, Kanazawa

Intro to Computer Vision and Computational Photography: Read Less [-]

COMPSCI C280 Computer Vision 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Paradigms for computational vision. Relation to human visual perception. Mathematical techniques for representing and reasoning, with curves, surfaces and volumes. Illumination and reflectance models. Color perception. Image segmentation and aggregation. Methods for bottom-up three dimensional shape recovery: Line drawing analysis, stereo, shading, motion, texture. Use of object models for prediction and recognition. Computer Vision: Read More [+]

Prerequisites: MATH 1A ; MATH 1B ; MATH 53 ; and MATH 54 (Knowledge of linear algebra and calculus)

Instructor: Malik

Also listed as: VIS SCI C280

Computer Vision: Read Less [-]

COMPSCI C281A Statistical Learning Theory 3 Units

Terms offered: Fall 2023, Fall 2021, Fall 2020 Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods. Statistical Learning Theory: Read More [+]

Instructors: Bartlett, Jordan, Wainwright

Also listed as: STAT C241A

Statistical Learning Theory: Read Less [-]

COMPSCI C281B Advanced Topics in Learning and Decision Making 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning. Advanced Topics in Learning and Decision Making: Read More [+]

Also listed as: STAT C241B

Advanced Topics in Learning and Decision Making: Read Less [-]

COMPSCI 282A Designing, Visualizing and Understanding Deep Neural Networks 4 Units

Terms offered: Fall 2023, Spring 2023, Fall 2022 Deep Networks have revolutionized computer vision, language technology, robotics and control. They have growing impact in many other areas of science and engineering. They do not however, follow a closed or compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground. Designing, Visualizing and Understanding Deep Neural Networks: Read More [+]

Student Learning Outcomes: Students will come to understand visualizing deep networks. Exploring the training and use of deep networks with visualization tools. Students will learn design principles and best practices: design motifs that work well in particular domains, structure optimization and parameter optimization. Understanding deep networks. Methods with formal guarantees: generative and adversarial models, tensor factorization.

Prerequisites: MATH 53 and MATH 54 or equivalent; COMPSCI 70 or STAT 134 ; COMPSCI 61B or equivalent; COMPSCI 189 or COMPSCI 289A (recommended)

Instructor: Canny

Designing, Visualizing and Understanding Deep Neural Networks: Read Less [-]

COMPSCI 284A Foundations of Computer Graphics 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Techniques of modeling objects for the purpose of computer rendering: boundary representations, constructive solids geometry, hierarchical scene descriptions. Mathematical techniques for curve and surface representation. Basic elements of a computer graphics rendering pipeline; architecture of modern graphics display devices. Geometrical transformations such as rotation, scaling, translation, and their matrix representations. Homogeneous coordinates, projective and perspective transformations. Foundations of Computer Graphics: Read More [+]

Prerequisites: COMPSCI 61B or COMPSCI 61BL ; programming skills in C, C++, or Java; linear algebra and calculus; or consent of instructor

Credit Restrictions: Students will receive no credit for Computer Science 284A after taking 184.

Instructors: Agrawala, Barsky, O'Brien, Ramamoorthi, Sequin

Foundations of Computer Graphics: Read Less [-]

COMPSCI 284B Advanced Computer Graphics Algorithms and Techniques 4 Units

Terms offered: Spring 2024, Spring 2022, Spring 2019 This course provides a graduate-level introduction to advanced computer graphics algorithms and techniques. Students should already be familiar with basic concepts such as transformations, scan-conversion, scene graphs, shading, and light transport. Topics covered in this course include global illumination, mesh processing, subdivision surfaces, basic differential geometry, physically based animation, inverse kinematics, imaging and computational photography, and precomputed light transport. Advanced Computer Graphics Algorithms and Techniques: Read More [+]

Prerequisites: COMPSCI 184

Instructors: O'Brien, Ramamoorthi

Formerly known as: Computer Science 283

Advanced Computer Graphics Algorithms and Techniques: Read Less [-]

COMPSCI 285 Deep Reinforcement Learning, Decision Making, and Control 3 Units

Terms offered: Fall 2023, Fall 2022, Fall 2021 Intersection of control, reinforcement learning, and deep learning. Deep learning methods, which train large parametric function approximators, achieve excellent results on problems that require reasoning about unstructured real-world situations (e.g., computer vision, speech recognition, NLP). Advanced treatment of the reinforcement learning formalism, the most critical model-free reinforcement learning algorithms (policy gradients, value function and Q-function learning, and actor-critic), a discussion of model-based reinforcement learning algorithms, an overview of imitation learning, and a range of advanced topics (e.g., exploration, model-based learning with video prediction, transfer learning, multi-task learning, and meta-learning). Deep Reinforcement Learning, Decision Making, and Control: Read More [+]

Student Learning Outcomes: Provide an opportunity to embark on a research-level final project with support from course staff. Provide hands-on experience with several commonly used RL algorithms; Provide students with an overview of advanced deep reinforcement learning topics, including current research trends; Provide students with foundational knowledge to understand deep reinforcement learning algorithms;

Prerequisites: CS189/289A or equivalent is a prerequisite for the course. This course will assume some familiarity with reinforcement learning, numerical optimization and machine learning, as well as a basic working knowledge of how to train deep neural networks (which is taught in CS182 and briefly covered in CS189)

Instructors: Levine, Abbeel

Deep Reinforcement Learning, Decision Making, and Control: Read Less [-]

COMPSCI 286 Implementation of Data Base Systems 3 Units

Terms offered: Fall 2009, Spring 2009, Spring 2008 Implementation of data base systems on modern hardware systems. Considerations concerning operating system design, including buffering, page size, prefetching, etc. Query processing algorithms, design of crash recovery and concurrency control systems. Implementation of distributed data bases and data base machines. Implementation of Data Base Systems: Read More [+]

Prerequisites: COMPSCI 162 and COMPSCI 186 ; or COMPSCI 286A

Instructors: Franklin, Hellerstein

Formerly known as: Computer Science 286B

Implementation of Data Base Systems: Read Less [-]

COMPSCI 286A Introduction to Database Systems 4 Units

Terms offered: Spring 2018, Fall 2017, Spring 2017 Access methods and file systems to facilitate data access. Hierarchical, network, relational, and object-oriented data models. Query languages for models. Embedding query languages in programming languages. Database services including protection, integrity control, and alternative views of data. High-level interfaces including application generators, browsers, and report writers. Introduction to transaction processing. Database system implementation to be done as term project. Introduction to Database Systems: Read More [+]

Prerequisites: COMPSCI 61B and COMPSCI 61C

Credit Restrictions: Students will receive no credit for CS 286A after taking CS 186.

Introduction to Database Systems: Read Less [-]

COMPSCI 287 Advanced Robotics 3 Units

Terms offered: Fall 2019, Fall 2015, Spring 2015 Advanced topics related to current research in algorithms and artificial intelligence for robotics. Planning, control, and estimation for realistic robot systems, taking into account: dynamic constraints, control and sensing uncertainty, and non-holonomic motion constraints. Advanced Robotics: Read More [+]

Prerequisites: Instructor consent for undergraduate and masters students

Instructor: Abbeel

Advanced Robotics: Read Less [-]

COMPSCI 287H Algorithmic Human-Robot Interaction 4 Units

Terms offered: Spring 2023, Spring 2021, Spring 2020 As robot autonomy advances, it becomes more and more important to develop algorithms that are not solely functional, but also mindful of the end-user. How should the robot move differently when it's moving in the presence of a human? How should it learn from user feedback? How should it assist the user in accomplishing day to day tasks? These are the questions we will investigate in this course. We will contrast existing algorithms in robotics with studies in human-robot interaction, discussing how to tackle interaction challenges in an algorithmic way, with the goal of enabling generalization across robots and tasks. We will also sharpen research skills: giving good talks, experimental design, statistical analysis, literature surveys. Algorithmic Human-Robot Interaction: Read More [+]

Student Learning Outcomes: Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to apply Bayesian inference and learning techniques to enhance coordination in collaborative tasks. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to apply optimization techniques to generate motion for HRI. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to contrast and relate model-based and model-free learning from demonstration. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to develop a basic understanding of verbal and non-verbal communication. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to ground algorithmic HRI in the relvant psychology background. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to tease out the intricacies of developing algorithms that support HRI. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to analyze and diagram the literature related to a particular topic. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to communicate scientific content to a peer audience. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to critique a scientific paper's experimental design and analysis.

Instructor: Dragan

Algorithmic Human-Robot Interaction: Read Less [-]

COMPSCI 288 Natural Language Processing 4 Units

Terms offered: Fall 2024, Fall 2023, Spring 2023 Methods and models for the analysis of natural (human) language data. Topics include: language modeling, speech recognition, linguistic analysis (syntactic parsing, semantic analysis, reference resolution, discourse modeling), machine translation, information extraction, question answering, and computational linguistics techniques. Natural Language Processing: Read More [+]

Prerequisites: COMPSCI 188 ; and COMPSCI 170 is recommended

Instructor: Klein

Natural Language Processing: Read Less [-]

COMPSCI 289A Introduction to Machine Learning 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This course provides an introduction to theoretical foundations, algorithms, and methodologies for machine learning, emphasizing the role of probability and optimization and exploring a variety of real-world applications. Students are expected to have a solid foundation in calculus and linear algebra as well as exposure to the basic tools of logic and probability, and should be familiar with at least one modern, high-level programming langua ge. Introduction to Machine Learning: Read More [+]

Prerequisites: MATH 53 , MATH 54 , COMPSCI 70 , and COMPSCI 188 ; or consent of instructor

Credit Restrictions: Students will receive no credit for Comp Sci 289A after taking Comp Sci 189.

Instructors: Listgarten, Malik, Recht, Sahai, Shewchuk

Introduction to Machine Learning: Read Less [-]

COMPSCI 294 Special Topics 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Topics will vary from semester to semester. See Computer Science Division announcements. Special Topics: Read More [+]

Fall and/or spring: 4 weeks - 3-15 hours of lecture per week 6 weeks - 3-9 hours of lecture per week 8 weeks - 2-6 hours of lecture per week 10 weeks - 2-5 hours of lecture per week 15 weeks - 1-3 hours of lecture per week

Additional Format: One to three hours of lecture per week for standard offering. In some instances, condensed special topics classes running from 2-10 weeks may also be offered usually to accommodate guest instructors. Total works hours will remain the same but more work in a given week will be required.

Special Topics: Read Less [-]

COMPSCI 297 Field Studies in Computer Science 12.0 Units

Terms offered: Fall 2022, Spring 2016, Fall 2015 Supervised experience in off-campus companies relevant to specific aspects and applications of electrical engineering and/or computer science. Written report required at the end of the semester. Field Studies in Computer Science: Read More [+]

Fall and/or spring: 15 weeks - 1-12 hours of independent study per week

Summer: 6 weeks - 1-30 hours of independent study per week 8 weeks - 1.5-22.5 hours of independent study per week 10 weeks - 1-18 hours of independent study per week

Additional Format: Independent study. Independent study.

Grading: Offered for satisfactory/unsatisfactory grade only.

Field Studies in Computer Science: Read Less [-]

COMPSCI 298 Group Studies Seminars, or Group Research 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Advanced study in various subjects through seminars on topics to be selected each year, informal group studies of special problems, group participation in comprehensive design problems, or group research on complete problems for analysis and experimentation. Group Studies Seminars, or Group Research: Read More [+]

Repeat rules: Course may be repeated for credit without restriction. Students may enroll in multiple sections of this course within the same semester.

Fall and/or spring: 15 weeks - 1-4 hours of lecture per week

Additional Format: One to four hours of lecture per week.

Grading: The grading option will be decided by the instructor when the class is offered.

Group Studies Seminars, or Group Research: Read Less [-]

COMPSCI 299 Individual Research 1 - 12 Units

Terms offered: Fall 2023, Fall 2022, Summer 2017 Second 6 Week Session Investigations of problems in computer science. Individual Research: Read More [+]

Fall and/or spring: 15 weeks - 0-1 hours of independent study per week

Summer: 6 weeks - 8-30 hours of independent study per week 8 weeks - 6-22.5 hours of independent study per week 10 weeks - 1.5-18 hours of independent study per week

Additional Format: Independent study. Forty-five hours of work per unit per term.

Individual Research: Read Less [-]

COMPSCI 300 Teaching Practice 1 - 6 Units

Terms offered: Fall 2012, Fall 2011, Spring 2011 Supervised teaching practice, in either a one-on-one tutorial or classroom discussion setting. Teaching Practice: Read More [+]

Fall and/or spring: 15 weeks - 0 hours of independent study per week

Summer: 6 weeks - 1-5 hours of independent study per week 8 weeks - 1-4 hours of independent study per week

Additional Format: Three to twenty hours of discussion and consulting per week.

Subject/Course Level: Computer Science/Professional course for teachers or prospective teachers

Teaching Practice: Read Less [-]

COMPSCI 302 Designing Computer Science Education 3 Units

Terms offered: Spring 2023, Spring 2022, Spring 2021 Discussion and review of research and practice relating to the teaching of computer science: knowledge organization and misconceptions, curriculum and topic organization, evaluation, collaborative learning, technology use, and administrative issues. As part of a semester-long project to design a computer science course, participants invent and refine a variety of homework and exam activities, and evaluate alternatives for textbooks, grading and other administrative policies, and innovative uses of technology. Designing Computer Science Education: Read More [+]

Prerequisites: COMPSCI 301 and two semesters of GSI experience

Fall and/or spring: 15 weeks - 2 hours of lecture per week

Additional Format: Two hours of lecture per week.

Instructor: Garcia

Designing Computer Science Education: Read Less [-]

COMPSCI 365 Introduction to Instructional Methods in Computer Science for Academic Interns 2 - 4 Units

Terms offered: Not yet offered This is a course for aspiring Academic Interns (AIs). It provides pedagogical training and guidance to students by introducing them to the Big Ideas of Teaching and Learning, and how to put them into practice. The course covers what makes a safe learning environment, how students learn, how to guide students toward mastery, and psychosocial factors that can negatively affect even the best students and best teachers. Class covers both theoretical and practical pedagogical aspects of teaching STEM subjects—specifically Computer Science. An integral feature of the course lies in the weekly AI experience that students perform to practice their teaching skills. Introduction to Instructional Methods in Computer Science for Academic Interns: Read More [+]

Prerequisites: Completion of any DS or CS lower-division course and concurrent participation in the Academic Intern experience in EECS at UC Berkeley

Fall and/or spring: 15 weeks - 2-2 hours of lecture and 3-9 hours of fieldwork per week

Summer: 8 weeks - 4-4 hours of lecture and 6-18 hours of fieldwork per week

Additional Format: Two hours of lecture and three to nine hours of fieldwork per week. Four hours of lecture and six to eightteen hours of fieldwork per week for 8 weeks.

Instructors: Hunn, Garcia

Introduction to Instructional Methods in Computer Science for Academic Interns: Read Less [-]

COMPSCI 370 Adaptive Instruction Methods in Computer Science 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This is a course for aspiring teachers or those who want to instruct with expertise from evidence-based research and proven equity-oriented practices. It provides pedagogical training by introducing the big ideas of teaching and learning, and illustrating how to put them into practice. The course is divided into three sections—instructing the individual; a group; and psycho-social factors that affect learning at any level. These sections are designed to enhance any intern’s, tutor’s, or TA’s teaching skillset. Class is discussion based, and covers theoretical and practical pedagogical aspects to teaching in STEM. An integral feature of the course involves providing weekly tutoring sessions. Adaptive Instruction Methods in Computer Science: Read More [+]

Prerequisites: Prerequisite satisfied Concurrently: experience tutoring or as an academic intern; or concurrently serving as an academic intern while taking course

Instructor: Hunn

Adaptive Instruction Methods in Computer Science: Read Less [-]

COMPSCI 375 Teaching Techniques for Computer Science 2 Units

Terms offered: Spring 2024, Spring 2023, Fall 2022 Discussion and practice of techniques for effective teaching, focusing on issues most relevant to teaching assistants in computer science courses. Teaching Techniques for Computer Science: Read More [+]

Prerequisites: Consent of instructor

Fall and/or spring: 15 weeks - 2 hours of discussion per week

Summer: 8 weeks - 4 hours of discussion per week

Additional Format: Two hours of discussion per week. Four hours of discussion per week for 8 weeks.

Instructors: Barsky, Garcia, Harvey

Teaching Techniques for Computer Science: Read Less [-]

COMPSCI 399 Professional Preparation: Supervised Teaching of Computer Science 1 or 2 Units

Terms offered: Spring 2020, Fall 2018, Fall 2016 Discussion, problem review and development, guidance of computer science laboratory sections, course development, supervised practice teaching. Professional Preparation: Supervised Teaching of Computer Science: Read More [+]

Prerequisites: Appointment as graduate student instructor

Fall and/or spring: 15 weeks - 1-2 hours of independent study per week

Summer: 8 weeks - 1-2 hours of independent study per week

Additional Format: One hour of meeting with instructor plus 10 hours (1 unit) or 20 hours(2 units) of teaching per week. One hour of meeting with instructor plus 20 hours (1 unit) or 40 hours (2 units) of teaching per week.

Professional Preparation: Supervised Teaching of Computer Science: Read Less [-]

COMPSCI 602 Individual Study for Doctoral Students 1 - 8 Units

Terms offered: Fall 2015, Fall 2014, Spring 2014 Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. (and other doctoral degrees). Individual Study for Doctoral Students: Read More [+]

Credit Restrictions: Course does not satisfy unit or residence requirements for doctoral degree.

Summer: 8 weeks - 6-45 hours of independent study per week

Additional Format: Forty-five hours of work per unit per term. Independent study, consultation with faculty member.

Subject/Course Level: Computer Science/Graduate examination preparation

Individual Study for Doctoral Students: Read Less [-]

EL ENG 206A Introduction to Robotics 4 Units

Terms offered: Fall 2017, Fall 2016, Fall 2015 An introduction to the kinematics, dynamics, and control of robot manipulators, robotic vision, and sensing. The course will cover forward and inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics and control-position, and force control. Proximity, tactile, and force sensing. Network modeling, stability, and fidelity in teleoperation and medical applications of robotics. Introduction to Robotics: Read More [+]

Prerequisites: 120 or equivalent, or consent of instructor

Credit Restrictions: Students will receive no credit for 206A after taking C125/Bioengineering C125 or EE C106A

Additional Format: Three hours of Lecture, One hour of Discussion, and Three hours of Laboratory per week for 15 weeks.

Subject/Course Level: Electrical Engineering/Graduate

Instructor: Bajcsy

Formerly known as: Electrical Engineering 215A

EL ENG 206B Robotic Manipulation and Interaction 4 Units

Terms offered: Spring 2018, Spring 2017 This course is a sequel to EECS 125/225, which covers kinematics, dynamics and control of a single robot. This course will cover dynamics and control of groups of robotic manipulators coordinating with each other and interacting with the environment. Concepts will include an introduction to grasping and the constrained manipulation, contacts and force control for interaction with the environment. We will also cover active perception guided manipulation, as well as the manipulation of non-rigid objects. Throughout, we will emphasize design and human-robot interactions, and applications to applications in manufacturing, service robotics, tele-surgery, and locomotion. Robotic Manipulation and Interaction: Read More [+]

Course Objectives: To teach students the connection between the geometry, physics of manipulators with experimental setups that include sensors, control of large degrees of freedom manipulators, mobile robots and different grippers.

Student Learning Outcomes: By the end of the course students will be able to build a complete system composed of perceptual planning and autonomously controlled manipulators and /or mobile systems, justified by predictive theoretical models of performance.

Prerequisites: EL ENG 206A / BIO ENG C125 ; or consent of the instructor

Additional Format: Three hours of lecture and three hours of laboratory and one hour of discussion per week.

EL ENG 210 Applied Electromagnetic Theory 3 Units

Terms offered: Spring 2011, Spring 2010, Fall 2006 Advanced treatment of classical electromagnetic theory with engineering applications. Boundary value problems in electrostatics. Applications of Maxwell's Equations to the study of waveguides, resonant cavities, optical fiber guides, Gaussian optics, diffraction, scattering, and antennas. Applied Electromagnetic Theory: Read More [+]

Prerequisites: EL ENG 117 ; or PHYSICS 110A and PHYSICS 110B

Formerly known as: 210A-210B

Applied Electromagnetic Theory: Read Less [-]

EL ENG 213A Power Electronics 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Power conversion circuits and techniques. Characterization and design of magnetic devices including transformers, inductors, and electromagnetic actuators. Characteristics of power semiconductor devices, including power diodes, SCRs, MOSFETs, IGBTs, and emerging wide bandgap devices. Applications to renewable energy systems, high-efficiency lighting, power management in mobile electronics, and electric machine drives. Simulation based laboratory and design project. Power Electronics: Read More [+]

Prerequisites: EL ENG 105 or background in circuit analysis (KVL, KCL, voltage/current relationships, etc.)

Instructors: Pilawa, Boles

Power Electronics: Read Less [-]

EL ENG 213B Power Electronics Design 4 Units

Terms offered: Spring 2024 This course is the second in a two-semester series to equip students with the skills needed to analyze, design, and prototype power electronic converters. While EE 113/213A provides an overview of power electronics fundamentals and applications, EE 113B/213B focuses on the practical design and hardware implementation of power converters. The primary focus of EE 113B/213B is time in the laboratory, with sequential modules on topics such as power electronic components , PCB layout, closed-loop control, and experimental validation. At the end of the course, students will have designed, prototyped, and validated a power converter from scratch, demonstrating a skill set that is critical for power electronics engineers in research and industry. Power Electronics Design: Read More [+]

Repeat rules: Course may be repeated for credit with instructor consent.

Fall and/or spring: 15 weeks - 1.5 hours of lecture and 6 hours of laboratory per week

Additional Format: One and one-half hours of lecture and six hours of laboratory per week.

Instructor: Boles

Power Electronics Design: Read Less [-]

EL ENG C213 X-rays and Extreme Ultraviolet Radiation 3 Units

Terms offered: Spring 2022, Spring 2021, Fall 2019 This course explores modern developments in the physics and applications of x-rays and extreme ultraviolet (EUV) radiation. It begins with a review of electromagnetic radiation at short wavelengths including dipole radiation, scattering and refractive index, using a semi-classical atomic model. Subject matter includes the generation of x-rays with synchrotron radiation, high harmonic generation, x-ray free electron lasers, laser-plasma sources. Spatial and temporal coherence concepts are explained. Optics appropriate for this spectral region are described. Applications include nanoscale and astrophysical imaging, femtosecond and attosecond probing of electron dynamics in molecules and solids, EUV lithography, and materials characteristics. X-rays and Extreme Ultraviolet Radiation: Read More [+]

Prerequisites: Physics 110, 137, and Mathematics 53, 54 or equivalent

Instructor: Attwood

Also listed as: AST C210

X-rays and Extreme Ultraviolet Radiation: Read Less [-]

EL ENG 218A Introduction to Optical Engineering 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Fundamental principles of optical systems. Geometrical optics and aberration theory. Stops and apertures, prisms, and mirrors. Diffraction and interference. Optical materials and coatings. Radiometry and photometry. Basic optical devices and the human eye. The design of optical systems. Lasers, fiber optics, and holography. Introduction to Optical Engineering: Read More [+]

Prerequisites: MATH 53 ; EECS 16A and EECS 16B , or MATH 54

Credit Restrictions: Students will receive no credit for Electrical Engineering 218A after taking Electrical Engineering 118 or 119.

Instructors: Waller, Kante

Introduction to Optical Engineering: Read Less [-]

EL ENG 219B Logic Synthesis 4 Units

Terms offered: Spring 2016, Spring 2015, Spring 2011 The course covers the fundamental techniques for the design and analysis of digital circuits. The goal is to provide a detailed understanding of basic logic synthesis and analysis algorithms, and to enable students to apply this knowledge in the design of digital systems and EDA tools. The course will present combinational circuit optimization (two-level and multi-level synthesis), sequential circuit optimization (state encoding, retiming) , timing analysis, testing, and logic verification. Logic Synthesis: Read More [+]

Additional Format: Three hours of Lecture and One hour of Discussion per week for 15 weeks.

Logic Synthesis: Read Less [-]

EL ENG C220A Advanced Control Systems I 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Input-output and state space representation of linear continuous and discrete time dynamic systems. Controllability, observability, and stability. Modeling and identification. Design and analysis of single and multi-variable feedback control systems in transform and time domain. State observer. Feedforward/preview control. Application to engineering systems. Advanced Control Systems I: Read More [+]

Instructors: Borrelli, Horowitz, Tomizuka, Tomlin

Also listed as: MEC ENG C232

Advanced Control Systems I: Read Less [-]

EL ENG C220B Experiential Advanced Control Design I 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Experience-based learning in the design of SISO and MIMO feedback controllers for linear systems. The student will master skills needed to apply linear control design and analysis tools to classical and modern control problems. In particular, the participant will be exposed to and develop expertise in two key control design technologies: frequency-domain control synthesis and time-domain optimization-based approach. Experiential Advanced Control Design I: Read More [+]

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

Additional Format: Three hours of Lecture and Two hours of Laboratory per week for 15 weeks.

Also listed as: MEC ENG C231A

Experiential Advanced Control Design I: Read Less [-]

EL ENG C220C Experiential Advanced Control Design II 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Experience-based learning in design, analysis, & verification of automatic control for uncertain systems. The course emphasizes use of practical algorithms, including thorough computer implementation for representative problems. The student will master skills needed to apply advanced model-based control analysis, design, and estimation to a variety of industrial applications. First-principles analysis is provided to explain and support the algorithms & methods. The course emphasizes model-based state estimation, including the Kalman filter, and particle filter. Optimal feedback control of uncertain systems is also discussed (the linear quadratic Gaussian problem) as well as considerations of transforming continuous-time to discrete time. Experiential Advanced Control Design II: Read More [+]

Prerequisites: Undergraduate controls course (e.g. MECENG 132, ELENG 128) Recommended: MECENG C231A/ELENG C220B and either MECENG C232/ELENG C220A or ELENG 221A

Instructor: Mueller

Also listed as: MEC ENG C231B

Experiential Advanced Control Design II: Read Less [-]

EL ENG C220D Input/Output Methods for Compositional System Analysis 2 Units

Terms offered: Prior to 2007 Introduction to input/output concepts from control theory, systems as operators in signal spaces, passivity and small-gain theorems, dissipativity theory, integral quadratic constraints. Compositional stabilility and performance certification for interconnected systems from subsystems input/output properties. Case studies in multi-agent systems, biological networks, Internet congestion control, and adaptive control. Input/Output Methods for Compositional System Analysis: Read More [+]

Course Objectives: Standard computational tools for control synthesis and verification do not scale well to large-scale, networked systems in emerging applications. This course presents a compositional methodology suitable when the subsystems are amenable to analytical and computational methods but the interconnection, taken as a whole, is beyond the reach of these methods. The main idea is to break up the task of certifying desired stability and performance properties into subproblems of manageable size using input/output properties. Students learn about the fundamental theory, as well as relevant algorithms and applications in several domains.

Instructors: Arcak, Packard

Also listed as: MEC ENG C220D

Input/Output Methods for Compositional System Analysis: Read Less [-]

EL ENG 221A Linear System Theory 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Basic system concepts; state-space and I/O representation. Properties of linear systems. Controllability, observability, minimality, state and output-feedback. Stability. Observers. Characteristic polynomial. Nyquist test. Linear System Theory: Read More [+]

Prerequisites: EL ENG 120 ; and MATH 110 recommended

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of recitation per week

Additional Format: Three hours of Lecture and Two hours of Recitation per week for 15 weeks.

Linear System Theory: Read Less [-]

EL ENG 222 Nonlinear Systems--Analysis, Stability and Control 3 Units

Terms offered: Spring 2017, Spring 2016, Spring 2015 Basic graduate course in non-linear systems. Second Order systems. Numerical solution methods, the describing function method, linearization. Stability - direct and indirect methods of Lyapunov. Applications to the Lure problem - Popov, circle criterion. Input-Output stability. Additional topics include: bifurcations of dynamical systems, introduction to the "geometric" theory of control for nonlinear systems, passivity concepts and dissipative dynamical systems. Nonlinear Systems--Analysis, Stability and Control: Read More [+]

Prerequisites: EL ENG 221A (may be taken concurrently)

Nonlinear Systems--Analysis, Stability and Control: Read Less [-]

EL ENG C222 Nonlinear Systems 3 Units

Terms offered: Spring 2023, Spring 2022, Spring 2021 Basic graduate course in nonlinear systems. Nonlinear phenomena, planar systems, bifurcations, center manifolds, existence and uniqueness theorems. Lyapunov’s direct and indirect methods, Lyapunov-based feedback stabilization. Input-to-state and input-output stability, and dissipativity theory. Computation techniques for nonlinear system analysis and design. Feedback linearization and sliding mode control methods. Nonlinear Systems: Read More [+]

Prerequisites: MATH 54 (undergraduate level ordinary differential equations and linear algebra)

Instructors: Arcak, Tomlin, Kameshwar

Also listed as: MEC ENG C237

Nonlinear Systems: Read Less [-]

EL ENG 223 Stochastic Systems: Estimation and Control 3 Units

Terms offered: Spring 2024, Fall 2022, Spring 2021 Parameter and state estimation. System identification. Nonlinear filtering. Stochastic control. Adaptive control. Stochastic Systems: Estimation and Control: Read More [+]

Prerequisites: EL ENG 226A (which students are encouraged to take concurrently)

Stochastic Systems: Estimation and Control: Read Less [-]

EL ENG 224A Digital Communications 4 Units

Terms offered: Fall 2010, Fall 2009, Fall 2008 Introduction to the basic principles of the design and analysis of modern digital communication systems. Topics include source coding; channel coding; baseband and passband modulation techniques; receiver design; channel equalization; information theoretic techniques; block, convolutional, and trellis coding techniques; multiuser communications and spread spectrum; multi-carrier techniques and FDM; carrier and symbol synchronization. Applications to design of digital telephone modems, compact disks, and digital wireless communication systems are illustrated. The concepts are illustrated by a sequence of MATLAB exercises. Digital Communications: Read More [+]

Prerequisites: EL ENG 120 and EL ENG 126

Additional Format: Four hours of Lecture and One hour of Discussion per week for 15 weeks.

Formerly known as: 224

Digital Communications: Read Less [-]

EL ENG 224B Fundamentals of Wireless Communication 3 Units

Terms offered: Spring 2013, Spring 2012, Spring 2010 Introduction of the fundamentals of wireless communication. Modeling of the wireless multipath fading channel and its basic physical parameters. Coherent and noncoherent reception. Diversity techniques over time, frequency, and space. Spread spectrum communication. Multiple access and interference management in wireless networks. Frequency re-use, sectorization. Multiple access techniques: TDMA, CDMA, OFDM. Capacity of wireless channels. Opportunistic communication. Multiple antenna systems: spatial multiplexing, space-time codes. Examples from existing wireless standards. Fundamentals of Wireless Communication: Read More [+]

Prerequisites: EL ENG 121 and EL ENG 226A

Instructor: Tse

Fundamentals of Wireless Communication: Read Less [-]

EL ENG 225D Audio Signal Processing in Humans and Machines 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Introduction to relevant signal processing and basics of pattern recognition. Introduction to coding, synthesis, and recognition. Models of speech and music production and perception. Signal processing for speech analysis. Pitch perception and auditory spectral analysis with applications to speech and music. Vocoders and music synthesizers. Statistical speech recognition, including introduction to Hidden Markov Model and Neural Network approac hes. Audio Signal Processing in Humans and Machines: Read More [+]

Prerequisites: EL ENG 123 and STAT 200A ; or graduate standing and consent of instructor

Instructor: Morgan

Audio Signal Processing in Humans and Machines: Read Less [-]

EL ENG C225E Principles of Magnetic Resonance Imaging 4 Units

Terms offered: Spring 2023, Spring 2021, Spring 2020, Spring 2019 Fundamentals of MRI including signal-to-noise ratio, resolution, and contrast as dictated by physics, pulse sequences, and instrumentation. Image reconstruction via 2D FFT methods. Fast imaging reconstruction via convolution-back projection and gridding methods and FFTs. Hardware for modern MRI scanners including main field, gradient fields, RF coils, and shim supplies. Software for MRI including imaging methods such as 2D FT , RARE, SSFP, spiral and echo planar imaging methods. Principles of Magnetic Resonance Imaging: Read More [+]

Course Objectives: Graduate level understanding of physics, hardware, and systems engineering description of image formation, and image reconstruction in MRI. Experience in Imaging with different MR Imaging systems. This course should enable students to begin graduate level research at Berkeley (Neuroscience labs, EECS and Bioengineering), LBNL or at UCSF (Radiology and Bioengineering) at an advanced level and make research-level contribution

Prerequisites: EL ENG 120 or BIO ENG C165 / EL ENG C145B or consent of instructor

Credit Restrictions: Students will receive no credit for Bioengineering C265/El Engineering C225E after taking El Engineering 265.

Repeat rules: Course may be repeated for credit under special circumstances: Students can only receive credit for 1 of the 2 versions of the class,BioEc265 or EE c225e, not both

Instructors: Conolly, Vandsburger

Also listed as: BIO ENG C265/NUC ENG C235

Principles of Magnetic Resonance Imaging: Read Less [-]

EL ENG 226A Random Processes in Systems 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Probability, random variables and their convergence, random processes. Filtering of wide sense stationary processes, spectral density, Wiener and Kalman filters. Markov processes and Markov chains. Gaussian, birth and death, poisson and shot noise processes. Elementary queueing analysis. Detection of signals in Gaussian and shot noise, elementary parameter estimation. Random Processes in Systems: Read More [+]

Prerequisites: EL ENG 120 and STAT 200A

Instructor: Anantharam

Formerly known as: 226

Random Processes in Systems: Read Less [-]

EL ENG 226B Applications of Stochastic Process Theory 2 Units

Terms offered: Spring 2017, Spring 2013, Spring 1997 Advanced topics such as: Martingale theory, stochastic calculus, random fields, queueing networks, stochastic control. Applications of Stochastic Process Theory: Read More [+]

Prerequisites: EL ENG 226A

Instructors: Anantharam, Varaiya

Applications of Stochastic Process Theory: Read Less [-]

EL ENG 227BT Convex Optimization 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Convex optimization is a class of nonlinear optimization problems where the objective to be minimized, and the constraints, are both convex. The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. The course includes laboratory assignments, which consist of hands-on experiments with the optimization software CVX, and a discussion section. Convex Optimization: Read More [+]

Prerequisites: MATH 54 and STAT 2

Instructors: El Ghaoui, Wainwright

Convex Optimization: Read Less [-]

EL ENG C227C Convex Optimization and Approximation 3 Units

Terms offered: Spring 2022, Spring 2021, Spring 2020, Spring 2019, Spring 2018, Spring 2017 Convex optimization as a systematic approximation tool for hard decision problems. Approximations of combinatorial optimization problems, of stochastic programming problems, of robust optimization problems (i.e., with optimization problems with unknown but bounded data), of optimal control problems. Quality estimates of the resulting approximation. Applications in robust engineering design, statistics , control, finance, data mining, operations research. Convex Optimization and Approximation: Read More [+]

Prerequisites: 227A or consent of instructor

Also listed as: IND ENG C227B

Convex Optimization and Approximation: Read Less [-]

EL ENG C227T Introduction to Convex Optimization 4 Units

Terms offered: Prior to 2007 The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. The course includes laboratory assignments, which consist of hands-on experience. Introduction to Convex Optimization: Read More [+]

Additional Format: Three hours of lecture and two hours of laboratory and one hour of discussion per week.

Formerly known as: Electrical Engineering C227A/Industrial Engin and Oper Research C227A

Also listed as: IND ENG C227A

Introduction to Convex Optimization: Read Less [-]

EL ENG 228A High Speed Communications Networks 3 Units

Terms offered: Fall 2014, Spring 2014, Fall 2011 Descriptions, models, and approaches to the design and management of networks. Optical transmission and switching technologies are described and analyzed using deterministic, stochastic, and simulation models. FDDI, DQDB, SMDS, Frame Relay, ATM, networks, and SONET. Applications demanding high-speed communication. High Speed Communications Networks: Read More [+]

Prerequisites: EL ENG 122 ; and EL ENG 226A (may be taken concurrently)

High Speed Communications Networks: Read Less [-]

EL ENG 229A Information Theory and Coding 3 Units

Terms offered: Fall 2024, Fall 2022, Fall 2021 Fundamental bounds of Shannon theory and their application. Source and channel coding theorems. Galois field theory, algebraic error-correction codes. Private and public-key cryptographic systems. Information Theory and Coding: Read More [+]

Prerequisites: STAT 200A ; and EL ENG 226 recommended

Instructors: Anantharam, Tse

Formerly known as: 229

Information Theory and Coding: Read Less [-]

EL ENG 229B Error Control Coding 3 Units

Terms offered: Spring 2019, Spring 2016, Fall 2013 Error control codes are an integral part of most communication and recording systems where they are primarily used to provide resiliency to noise. In this course, we will cover the basics of error control coding for reliable digital transmission and storage. We will discuss the major classes of codes that are important in practice, including Reed Muller codes, cyclic codes, Reed Solomon codes, convolutional codes, concatenated codes, turbo codes, and low density parity check codes. The relevant background material from finite field and polynomial algebra will be developed as part of the course. Overview of topics: binary linear block codes; Reed Muller codes; Galois fields; linear block codes over a finite field; cyclic codes; BCH and Reed Solomon codes; convolutional codes and trellis based decoding, message passing decoding algorithms; trellis based soft decision decoding of block codes; turbo codes; low density parity check codes. Error Control Coding: Read More [+]

Prerequisites: 126 or equivalent (some familiarity with basic probability). Prior exposure to information theory not necessary

Instructor: Anatharam

Error Control Coding: Read Less [-]

EL ENG 230A Integrated-Circuit Devices 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Overview of electronic properties of semiconductors. Metal-semiconductor contacts, pn junctions, bipolar transistors, and MOS field-effect transistors. Properties that are significant to device operation for integrated circuits. Silicon device fabrication technology. Integrated-Circuit Devices: Read More [+]

Prerequisites: EECS 16A AND EECS 16B

Credit Restrictions: Students will receive no credit for EL ENG 230A after completing EL ENG 130 , EL ENG 230M, or EL ENG W230A . A deficient grade in EL ENG 230A may be removed by taking EL ENG W230A .

Formerly known as: Electrical Engineering 230M

Integrated-Circuit Devices: Read Less [-]

EL ENG 230B Solid State Devices 4 Units

Terms offered: Fall 2020, Spring 2019, Spring 2018 Physical principles and operational characteristics of semiconductor devices. Emphasis is on MOS field-effect transistors and their behaviors dictated by present and probable future technologies. Metal-oxide-semiconductor systems, short-channel and high field effects, device modeling, and impact on analog, digital circuits. Solid State Devices: Read More [+]

Prerequisites: EL ENG 130

Credit Restrictions: Students will receive no credit for EL ENG 230B after completing EL ENG 231, or EL ENG W230B . A deficient grade in EL ENG 230B may be removed by taking EL ENG W230B .

Instructors: Subramanian, King Liu, Salahuddin

Formerly known as: Electrical Engineering 231

Solid State Devices: Read Less [-]

EL ENG 230C Solid State Electronics 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2018 Crystal structure and symmetries. Energy-band theory. Cyclotron resonance. Tensor effective mass. Statistics of electronic state population. Recombination theory. Carrier transport theory. Interface properties. Optical processes and properties. Solid State Electronics: Read More [+]

Prerequisites: EL ENG 131; and PHYSICS 137B

Instructors: Bokor, Salahuddin

Formerly known as: Electrical Engineering 230

Solid State Electronics: Read Less [-]

EL ENG W230A Integrated-Circuit Devices 4 Units

Terms offered: Spring 2019, Spring 2018, Spring 2017 Overview of electronic properties of semiconductors. Metal-semiconductor contacts, pn junctions, bipolar transistors, and MOS field-effect transistors. Properties that are significant to device operation for integrated circuits. Silicon device fabrication technology. Integrated-Circuit Devices: Read More [+]

Prerequisites: MAS-IC students only

Credit Restrictions: Students will receive no credit for Electrical Engineering W230A after taking Electrical Engineering 130, Electrical Engineering W130 or Electrical Engineering 230A.

Fall and/or spring: 15 weeks - 3 hours of web-based lecture and 1 hour of web-based discussion per week

Summer: 10 weeks - 4.5 hours of web-based lecture and 1.5 hours of web-based discussion per week

Additional Format: Three hours of Web-based lecture and One hour of Web-based discussion per week for 15 weeks. Four and one-half hours of Web-based lecture and One and one-half hours of Web-based discussion per week for 10 weeks.

Instructors: Javey, Subramanian, King Liu

Formerly known as: Electrical Engineering W130

EL ENG W230B Solid State Devices 4 Units

Terms offered: Fall 2015 Physical principles and operational characteristics of semiconductor devices. Emphasis is on MOS field-effect transistors and their behaviors dictated by present and probable future technologies. Metal-oxide-semiconductor systems, short-channel and high field effects, device modeling, and impact on analog, digital circuits. Solid State Devices: Read More [+]

Prerequisites: EL ENG W230A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W230B after taking EE 230B.

Formerly known as: Electrical Engineering W231

EL ENG 232 Lightwave Devices 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course is designed to give an introduction and overview of the fundamentals of optoelectronic devices. Topics such as optical gain and absorption spectra, quantization effects, strained quantum wells, optical waveguiding and coupling, and hetero p-n junction will be covered. This course will focus on basic physics and design principles of semiconductor diode lasers, light emitting diodes, photodetectors and integrated optics. Practical applications of the devices will be also discussed. Lightwave Devices: Read More [+]

Prerequisites: EL ENG 130 ; PHYSICS 137A ; and EL ENG 117 recommended

Instructor: Wu

Lightwave Devices: Read Less [-]

EL ENG 234A Fundamentals of Photovoltaic Devices 4 Units

Terms offered: Not yet offered This course is designed to give an introduction, and overview of, the fundamentals of photovoltaic devices. Students will learn how solar cells work, understand the concepts and models of solar cell device physics, and formulate and solve relevant physical problems related to photovoltaic devices. Monocrystalline, thin film and third generation solar cells will be discussed and analyzed. Light management and economic considerations in a solar cell system will also be covered. Fundamentals of Photovoltaic Devices: Read More [+]

Prerequisites: EECS 16A and EECS 16B , or Math 54 and Physics 7B, or equivalent

Instructor: Arias

Fundamentals of Photovoltaic Devices: Read Less [-]

EL ENG C235 Nanoscale Fabrication 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022, Spring 2016, Spring 2015, Spring 2013 This course discusses various top-down and bottom-up approaches to synthesizing and processing nanostructured materials. The topics include fundamentals of self assembly, nano-imprint lithography, electron beam lithography, nanowire and nanotube synthesis, quantum dot synthesis (strain patterned and colloidal), postsynthesis modification (oxidation, doping, diffusion, surface interactions, and etching techniques). In addition, techniques to bridging length scales such as heterogeneous integration will be discussed. We will discuss new electronic, optical, thermal, mechanical, and chemical properties brought forth by the very small sizes. Nanoscale Fabrication: Read More [+]

Instructor: Chang-Hasnain

Also listed as: NSE C203

Nanoscale Fabrication: Read Less [-]

EL ENG 236A Quantum and Optical Electronics 3 Units

Terms offered: Fall 2023, Fall 2022, Spring 2021 Interaction of radiation with atomic and semiconductor systems, density matrix treatment, semiclassical laser theory (Lamb's), laser resonators, specific laser systems, laser dynamics, Q-switching and mode-locking, noise in lasers and optical amplifiers. Nonlinear optics, phase-conjugation, electrooptics, acoustooptics and magnetooptics, coherent optics, stimulated Raman and Brillouin scattering. Quantum and Optical Electronics: Read More [+]

Prerequisites: EL ENG 117A and PHYSICS 137A

Quantum and Optical Electronics: Read Less [-]

EL ENG C239 Partially Ionized Plasmas 3 Units

Terms offered: Spring 2010, Spring 2009, Spring 2007 Introduction to partially ionized, chemically reactive plasmas, including collisional processes, diffusion, sources, sheaths, boundaries, and diagnostics. DC, RF, and microwave discharges. Applications to plasma-assisted materials processing and to plasma wall interactions. Partially Ionized Plasmas: Read More [+]

Prerequisites: An upper division course in electromagnetics or fluid dynamics

Additional Format: Forty-five hours of lecture per term.

Formerly known as: 239

Also listed as: AST C239

Partially Ionized Plasmas: Read Less [-]

EL ENG 240A Analog Integrated Circuits 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Single and multiple stage transistor amplifiers. Operational amplifiers. Feedback amplifiers, 2-port formulation, source, load, and feedback network loading. Frequency response of cascaded amplifiers, gain-bandwidth exchange, compensation, dominant pole techniques, root locus. Supply and temperature independent biasing and references. Selected applications of analog circuits such as analog-to-digital converters, switched capacitor filters, and comparators. Hardware laboratory and design project. Analog Integrated Circuits: Read More [+]

Prerequisites: EL ENG 105

Credit Restrictions: Students will receive no credit for EL ENG 240A after completing EL ENG 140 , or EL ENG W240A . A deficient grade in EL ENG 240A may be removed by taking EL ENG W240A .

Instructors: Sanders, Nguyen

Analog Integrated Circuits: Read Less [-]

EL ENG 240B Advanced Analog Integrated Circuits 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Analysis and optimized design of monolithic operational amplifiers and wide-band amplifiers; methods of achieving wide-band amplification, gain-bandwidth considerations; analysis of noise in integrated circuits and low noise design. Precision passive elements, analog switches, amplifiers and comparators, voltage reference in NMOS and CMOS circuits, Serial, successive-approximation, and parallel analog-to-digital converters. Switched-capacitor and CCD filters. Applications to codecs, modems. Advanced Analog Integrated Circuits: Read More [+]

Prerequisites: EL ENG 140 / EL ENG 240A

Credit Restrictions: Students will receive no credit for EL ENG 240B after completing EL ENG 240, or EL ENG W240B . A deficient grade in EL ENG 240B may be removed by taking EL ENG W240B .

Advanced Analog Integrated Circuits: Read Less [-]

EL ENG 240C Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits 3 Units

Terms offered: Fall 2024, Spring 2023, Fall 2019 Architectural and circuit level design and analysis of integrated analog-to-digital and digital-to-analog interfaces in CMOS and BiCMOS VLSI technology. Analog-digital converters, digital-analog converters, sample/hold amplifiers, continuous and switched-capacitor filters. RF integrated electronics including synthesizers, LNA's, and baseband processing. Low power mixed signal design. Data communications functions including clock recovery. CAD tools for analog design including simulation and synthesis. Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read More [+]

Prerequisites: EL ENG 140

Credit Restrictions: Students will receive no credit for EL ENG 240C after completing EL ENG 290Y , or EL ENG W240C . A deficient grade in EL ENG 240C may be removed by taking EL ENG W240C .

Instructor: Boser

Formerly known as: Electrical Engineering 247

Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read Less [-]

EL ENG W240A Analog Integrated Circuits 4 Units

Terms offered: Spring 2020, Spring 2019, Spring 2018 Single and multiple stage transistor amplifiers. Operational amplifiers. Feedback amplifiers, 2-port formulation, source, load, and feedback network loading. Frequency response of cascaded amplifiers, gain-bandwidth exchange, compensation, dominant pole techniques, root locus. Supply and temperature independent biasing and references. Selected applications of analog circuits such as analog-to-digital converters, switched capacitor filters , and comparators. Analog Integrated Circuits: Read More [+]

Credit Restrictions: Students will receive no credit for EE W240A after taking EE 140 or EE 240A.

Instructors: Alon, Sanders, Nguyen

EL ENG W240B Advanced Analog Integrated Circuits 3 Units

Terms offered: Spring 2020, Spring 2019, Fall 2015 Analysis and optimized design of monolithic operational amplifiers and wide-band amplifiers; methods of achieving wide-band amplification, gain-bandwidth considerations; analysis of noise in integrated circuits and low noise design. Precision passive elements, analog switches, amplifiers and comparators, voltage reference in NMOS and CMOS circuits, Serial, successive-approximation, and parallel analog-to-digital converts. Switched-capacitor and CCD filters. Applications to codecs, modems. Advanced Analog Integrated Circuits: Read More [+]

Prerequisites: EL ENG W240A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W240B after taking EE 240B.

Summer: 10 weeks - 4.5 hours of web-based lecture per week

Additional Format: Three hours of Web-based lecture per week for 15 weeks. Four and one-half hours of Web-based lecture per week for 10 weeks.

Formerly known as: Electrical Engineering W240

EL ENG W240C Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits 3 Units

Terms offered: Spring 2017, Spring 2016 Architectural and circuit level design and analysis of integrated analog-to-digital and digital-to-analog interfaces in modern CMOS and BiCMOS VLSI technology. Analog-digital converters, digital-analog converters, sample/hold amplifiers, continuous and switched-capacitor filters. Low power mixed signal design techniques. Data communications systems including interface circuity. CAD tools for analog design for simulation and synthesis. Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read More [+]

Credit Restrictions: Students will receive no credit for EE W240C after taking EE 240C.

Formerly known as: Electrical Engineering W247

EL ENG 241B Advanced Digital Integrated Circuits 3 Units

Terms offered: Spring 2021, Spring 2020, Spring 2019 Analysis and design of MOS and bipolar large-scale integrated circuits at the circuit level. Fabrication processes, device characteristics, parasitic effects static and dynamic digital circuits for logic and memory functions. Calculation of speed and power consumption from layout and fabrication parameters. ROM, RAM, EEPROM circuit design. Use of SPICE and other computer aids. Advanced Digital Integrated Circuits: Read More [+]

Prerequisites: EL ENG 141

Credit Restrictions: Students will receive no credit for EL ENG 241B after completing EL ENG 241, or EL ENG W241B . A deficient grade in EL ENG 241B may be removed by taking EL ENG W241B .

Instructors: Nikolic, Rabaey

Formerly known as: Electrical Engineering 241

Advanced Digital Integrated Circuits: Read Less [-]

EL ENG W241A Introduction to Digital Integrated Circuits 4 Units

Terms offered: Fall 2015, Fall 2014, Spring 2014 CMOS devices and deep sub-micron manufacturing technology. CMOS inverters and complex gates. Modeling of interconnect wires. Optimization of designs with respect to a number of metrics: cost, reliability, performance, and power dissipation. Sequential circuits, timing considerations, and clocking approaches. Design of large system blocks, including arithmetic, interconnect, memories, and programmable logic arrays. Introduction to design methodologies , including laboratory experience. Introduction to Digital Integrated Circuits: Read More [+]

Credit Restrictions: Students will receive no credit for W241A after taking EE 141 or EE 241A.

Fall and/or spring: 15 weeks - 3 hours of web-based lecture and 4 hours of web-based discussion per week

Summer: 10 weeks - 4.5 hours of web-based lecture and 6 hours of web-based discussion per week

Additional Format: F/Sp: Three hours of web-based lecture, one hour of web-based discussion, and three hours of web-based laboratory per week. Su: Four and one-half hours of web-based lecture, one and one-half hours of web-based discussion, and four and one-half hours of web-based laboratory per week for ten weeks.

Instructors: Alon, Rabaey, Nikolic

Introduction to Digital Integrated Circuits: Read Less [-]

EL ENG W241B Advanced Digital Integrated Circuits 3 Units

Terms offered: Spring 2017, Spring 2016, Spring 2015 Analysis and design of MOS and bipolar large-scale integrated circuits at the circuit level. Fabrication processes, device characteristics, parasitic effects static and dynamic digital circuits for logic and memory functions. Calculation of speed and power consumption from layout and fabrication parameters. ROM, RAM, EEPROM circuit design. Use of SPICE and other computer aids. Advanced Digital Integrated Circuits: Read More [+]

Prerequisites: EL ENG W241A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W241B after taking EE 241B.

Formerly known as: Electrical Engineering W241

EL ENG 242A Integrated Circuits for Communications 4 Units

Terms offered: Fall 2023, Spring 2023, Spring 2022 Analysis and design of electronic circuits for communication systems, with an emphasis on integrated circuits for wireless communication systems. Analysis of noise and distortion in amplifiers with application to radio receiver design. Power amplifier design with application to wireless radio transmitters. Radio-frequency mixers, oscillators, phase-locked loops, modulators, and demodulators. Integrated Circuits for Communications: Read More [+]

Prerequisites: EL ENG 140 /240A or equivalent

Credit Restrictions: Students will receive no credit for Electrical Engineering 242A after taking Electrical Engineering 142.

Formerly known as: Electrical Engineering 242M

Integrated Circuits for Communications: Read Less [-]

EL ENG 242B Advanced Integrated Circuits for Communications 3 Units

Terms offered: Fall 2024, Fall 2020, Fall 2014 Analysis, evaluation and design of present-day integrated circuits for communications application, particularly those for which nonlinear response must be included. MOS, bipolar and BICMOS circuits, audio and video power amplifiers, optimum performance of near-sinusoidal oscillators and frequency-translation circuits. Phase-locked loop ICs, analog multipliers and voltage-controlled oscillators; advanced components for telecommunication circuits. Use of new CAD tools and systems. Advanced Integrated Circuits for Communications: Read More [+]

Prerequisites: EL ENG 142 and EL ENG 240

Credit Restrictions: Students will receive no credit for EL ENG 242B after completing EL ENG 242, or EL ENG W242B . A deficient grade in EL ENG 242B may be removed by taking EL ENG W242B .

Instructor: Niknejad

Formerly known as: Electrical Engineering 242

Advanced Integrated Circuits for Communications: Read Less [-]

EL ENG W242A Integrated Circuits for Communications 4 Units

Terms offered: Spring 2020, Spring 2019, Spring 2018 Analysis and design of electronic circuits for communication systems, with an emphasis on integrated circuits for wireless communication systems. Analysis of noise and distortion in amplifiers with application to radio receiver design. Power amplifier design with application to wireless radio transmitters. Radio-frequency mixers, oscillators, phase-locked loops, modulators, and demodulators. Integrated Circuits for Communications: Read More [+]

Credit Restrictions: Students will receive no credit for EE W242A after taking EE 142, EE 242A, or EE 242B.

Formerly known as: Electrical Engineering W142

EL ENG W242B Advanced Integrated Circuits for Communications 3 Units

Terms offered: Spring 2017, Spring 2016 Analysis, evaluation, and design of present-day integrated circuits for communications application, particularly those for which nonlinear response must be included. MOS, bipolar and BICMOS circuits, audio and video power amplifiers, optimum performance of near-sinusoidal oscillators and frequency-translation circuits. Phase-locked loop ICs, analog multipliers and voltage-controlled oscillators; advanced components for telecommunication circuits. Use of new CAD tools and systems. Advanced Integrated Circuits for Communications: Read More [+]

Prerequisites: EL ENG W240A ; EL ENG W242A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W242B after taking EE 242B.

Formerly known as: Electrical Engineering W242

EL ENG 243 Advanced IC Processing and Layout 3 Units

Terms offered: Spring 2014, Spring 2012, Spring 2011 The key processes for the fabrication of integrated circuits. Optical, X-ray, and e-beam lithography, ion implantation, oxidation and diffusion. Thin film deposition. Wet and dry etching and ion milling. Effect of phase and defect equilibria on process control. Advanced IC Processing and Layout: Read More [+]

Prerequisites: EL ENG 143 ; and either EL ENG 140 or EL ENG 141

Advanced IC Processing and Layout: Read Less [-]

EL ENG 244 Fundamental Algorithms for Systems Modeling, Analysis, and Optimization 4 Units

Terms offered: Fall 2016, Fall 2015, Fall 2014 The modeling, analysis, and optimization of complex systems requires a range of algorithms and design software. This course reviews the fundamental techniques underlying the design methodology for complex systems, using integrated circuit design as example. Topics include design flows, discrete and continuous models and algorithms, and strategies for implementing algorithms efficiently and correctly in software. Laboratory assignments and a class project will expose students to state-of-the-art. Fundamental Algorithms for Systems Modeling, Analysis, and Optimization: Read More [+]

Prerequisites: Graduate standing

Credit Restrictions: Students will receive no credit for EL ENG 244 after completing EL ENG W244 .

Instructors: Keutzer, Lee, Roychowdhury, Seshia

Fundamental Algorithms for Systems Modeling, Analysis, and Optimization: Read Less [-]

EL ENG W244 Fundamental Algorithms for System Modeling, Analysis, and Optimization 4 Units

Terms offered: Fall 2015 The modeling, analysis, and optimization of complex systems require a range of algorithms and design tools. This course reviews the fundamental techniques underlying the design methodology for complex systems, using integrated circuit design as an example. Topics include design flows, discrete and continuous models and algorithms, and strategies for implementing algorithms efficiently and correctly in software. Fundamental Algorithms for System Modeling, Analysis, and Optimization: Read More [+]

Credit Restrictions: Students will receive no credit for W244 after taking 144 and 244.

Fundamental Algorithms for System Modeling, Analysis, and Optimization: Read Less [-]

EL ENG C246 Parametric and Optimal Design of MEMS 3 Units

Terms offered: Spring 2013, Spring 2012, Spring 2011 Parametric design and optimal design of MEMS. Emphasis on design, not fabrication. Analytic solution of MEMS design problems to determine the dimensions of MEMS structures for specified function. Trade-off of various performance requirements despite conflicting design requirements. Structures include flexure systems, accelerometers, and rate sensors. Parametric and Optimal Design of MEMS: Read More [+]

Prerequisites: Graduate standing or consent of instructor

Instructors: Lin, Pisano

Formerly known as: 219

Also listed as: MEC ENG C219

Parametric and Optimal Design of MEMS: Read Less [-]

EL ENG 247A Introduction to Microelectromechanical Systems (MEMS) 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course will teach fundamentals of micromachining and microfabrication techniques, including planar thin-film process technologies, photolithographic techniques, deposition and etching techniques, and the other technologies that are central to MEMS fabrication. It will pay special attention to teaching of fundamentals necessary for the design and analysis of devices and systems in mechanical, electrical, fluidic, and thermal energy/signal domains , and will teach basic techniques for multi-domain analysis. Fundamentals of sensing and transduction mechanisms including capacitive and piezoresistive techniques, and design and analysis of micmicromachined miniature sensors and actuators using these techniques will be covered. Introduction to Microelectromechanical Systems (MEMS): Read More [+]

Prerequisites: EECS 16A and EECS 16B ; or consent of instructor required

Credit Restrictions: Students will receive no credit for EE 247A after taking EE 147.

Instructors: Maharbiz, Nguyen, Pister

Introduction to Microelectromechanical Systems (MEMS): Read Less [-]

EL ENG C247B Introduction to MEMS Design 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022, Spring 2021, Spring 2020 Physics, fabrication, and design of micro-electromechanical systems (MEMS). Micro and nanofabrication processes, including silicon surface and bulk micromachining and non-silicon micromachining. Integration strategies and assembly processes. Microsensor and microactuator devices: electrostatic, piezoresistive, piezoelectric, thermal, magnetic transduction. Electronic position-sensing circuits and electrical and mechanical noise. CAD for MEMS. Design project is required. Introduction to MEMS Design: Read More [+]

Prerequisites: Graduate standing in engineering or science; undergraduates with consent of instructor

Instructors: Nguyen, Pister

Formerly known as: Electrical Engineering C245, Mechanical Engineering C218

Also listed as: MEC ENG C218

Introduction to MEMS Design: Read Less [-]

EL ENG W247B Introduction to MEMS Design 4 Units

Terms offered: Prior to 2007 Physics, fabrication and design of micro electromechanical systems (MEMS). Micro and nano-fabrication processes, including silicon surface and bulk micromachining and non-silicon micromachining. Integration strategies and assembly processes. Microsensor and microactuator devices: electrostatic, piezoresistive, piezoelectric, thermal, and magnetic transduction. Electronic position-sensing circuits and electrical and mechanical noise. CAD for MEMS. Design project is required. Introduction to MEMS Design: Read More [+]

Credit Restrictions: Students will receive no credit for EE W247B after taking EE C247B or Mechanical Engineering C218.

Formerly known as: Electrical Engineering W245

EL ENG 248C Numerical Modeling and Analysis: Nonlinear Systems and Noise 4 Units

Terms offered: Prior to 2007 Numerical modelling and analysis techniques are widely used in scientific and engineering practice; they are also an excellent vehicle for understanding and concretizing theory. This course covers topics important for a proper understanding of nonlinearity and noise: periodic steady state and envelope ("RF") analyses; oscillatory systems; nonstationary and phase noise; and homotopy/continuation techniques for solving "difficult" equation systems. An underlying theme of the course is relevance to different physical domains, from electronics (e.g., analog/RF/mixed-signal circuits, high-speed digital circuits, interconnect, etc.) to optics, nanotechnology, chemistry, biology and mechanics. Hands-on coding using the MATLAB-based Berkeley Model Numerical Modeling and Analysis: Nonlinear Systems and Noise: Read More [+]

Course Objectives: Homotopy techniques for robust nonlinear equation solution Modelling and analysis of oscillatory systems - harmonic, ring and relaxation oscillators - oscillator steady state analysis - perturbation analysis of amplitude-stable oscillators RF (nonlinear periodic steady state) analysis - harmonic balance and shooting - Multi-time PDE and envelope methods - perturbation analysis of periodic systems (Floquet theory) RF (nonlinear, nonstationary) noise concepts and their application - cyclostationary noise analysis - concepts of phase noise in oscillators Using MAPP for fast/convenient modelling and analysis

Student Learning Outcomes: Students will develop a facility in the above topics and be able to apply them widely across science and engineering.

Prerequisites: Consent of Instructor

Numerical Modeling and Analysis: Nonlinear Systems and Noise: Read Less [-]

EL ENG C249A Introduction to Embedded Systems 4 Units

Also listed as: COMPSCI C249A

EL ENG C261 Medical Imaging Signals and Systems 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Biomedical imaging is a clinically important application of engineering, applied mathematics, physics, and medicine. In this course, we apply linear systems theory and basic physics to analyze X-ray imaging, computerized tomography, nuclear medicine, and MRI. We cover the basic physics and instrumentation that characterizes medical image as an ideal perfect-resolution image blurred by an impulse response. This material could prepare the student for a career in designing new medical imaging systems that reliably detect small tumors or infarcts. Medical Imaging Signals and Systems: Read More [+]

Course Objectives: • understand how 2D impulse response or 2D spatial frequency transfer function (or Modulation Transfer Function) allow one to quantify the spatial resolution of an imaging system. • understand 2D sampling requirements to avoid aliasing • understand 2D filtered backprojection reconstruction from projections based on the projection-slice theorem of Fourier Transforms • understand the concept of image reconstruction as solving a mathematical inverse problem. • understand the limitations of poorly conditioned inverse problems and noise amplification • understand how diffraction can limit resolution---but not for the imaging systems in this class • understand the hardware components of an X-ray imaging scanner • • understand the physics and hardware limits to spatial resolution of an X-ray imaging system • understand tradeoffs between depth, contrast, and dose for X-ray sources • understand resolution limits for CT scanners • understand how to reconstruct a 2D CT image from projection data using the filtered backprojection algorithm • understand the hardware and physics of Nuclear Medicine scanners • understand how PET and SPECT images are created using filtered backprojection • understand resolution limits of nuclear medicine scanners • understand MRI hardware components, resolution limits and image reconstruction via a 2D FFT • understand how to construct a medical imaging scanner that will achieve a desired spatial resolution specification.

Student Learning Outcomes: • students will be tested for their understanding of the key concepts above • undergraduate students will apply to graduate programs and be admitted • students will apply this knowledge to their research at Berkeley, UCSF, the national labs or elsewhere • students will be hired by companies that create, sell, operate or consult in biomedical imaging

Prerequisites: Undergraduate level course work covering integral and differential calculus, two classes in engineering-level physics, introductory level linear algebra, introductory level statistics, at least 1 course in LTI system theory including (analog convolution, Fourier transforms, and Nyquist sampling theory). The recommended undergrad course prerequisites are introductory level skills in Python or Matlab and either EECS 16A , EECS 16B and EL ENG 120 , or MATH 54 , BIO ENG 101 , and BIO ENG 105

Instructor: Conolly

Also listed as: BIO ENG C261/NUC ENG C231

Medical Imaging Signals and Systems: Read Less [-]

EL ENG 290 Advanced Topics in Electrical Engineering 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Read More [+]

Repeat rules: Course may be repeated for credit when topic changes.

Additional Format: One to three hours of lecture per week. Two to five hours of lecture per week for 10 weeks. Two to six hours of lecture per week for 8 weeks. Three to nine hours of lecture per week for 6 weeks. Three to fifteen hours of lecture per week for four weeks.

Advanced Topics in Electrical Engineering: Read Less [-]

EL ENG 290A Advanced Topics in Electrical Engineering: Advanced Topics in Computer-Aided Design 1 - 3 Units

Terms offered: Spring 2016, Spring 2015, Fall 2014 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Computer-Aided Design: Read More [+]

Fall and/or spring: 15 weeks - 1-3 hours of lecture per week

Additional Format: One to Three hour of Lecture per week for 15 weeks.

Advanced Topics in Electrical Engineering: Advanced Topics in Computer-Aided Design: Read Less [-]

EL ENG 290B Advanced Topics in Electrical Engineering: Advanced Topics in Solid State Devices 1 - 3 Units

Terms offered: Spring 2021, Spring 2020, Spring 2019 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Solid State Devices: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Solid State Devices: Read Less [-]

EL ENG 290C Advanced Topics in Electrical Engineering: Advanced Topics in Circuit Design 1 - 3 Units

Terms offered: Spring 2019, Fall 2018, Spring 2018 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Circuit Design: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Circuit Design: Read Less [-]

EL ENG 290D Advanced Topics in Electrical Engineering: Advanced Topics in Semiconductor Technology 1 - 3 Units

Terms offered: Spring 2021, Fall 2014, Fall 2013 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Semiconductor Technology: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Semiconductor Technology: Read Less [-]

EL ENG 290F Advanced Topics in Electrical Engineering: Advanced Topics in Photonics 1 - 3 Units

Terms offered: Spring 2014, Fall 2013, Fall 2012 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Photonics: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Photonics: Read Less [-]

EL ENG 290G Advanced Topics in Electrical Engineering: Advanced Topics in Mems, Microsensors, and Microactuators 1 - 3 Units

Terms offered: Fall 2017, Fall 2016, Spring 2002 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Mems, Microsensors, and Microactuators: Read More [+]

Formerly known as: Engineering 210

Advanced Topics in Electrical Engineering: Advanced Topics in Mems, Microsensors, and Microactuators: Read Less [-]

EL ENG 290N Advanced Topics in Electrical Engineering: Advanced Topics in System Theory 1 - 3 Units

Terms offered: Fall 2018, Fall 2017, Fall 2015 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in System Theory: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in System Theory: Read Less [-]

EL ENG 290O Advanced Topics in Electrical Engineering: Advanced Topics in Control 1 - 3 Units

Terms offered: Spring 2019, Fall 2018, Fall 2017 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Control: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Control: Read Less [-]

EL ENG 290P Advanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics 1 - 3 Units

Terms offered: Spring 2019, Spring 2018, Fall 2017 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics: Read Less [-]

EL ENG 290Q Advanced Topics in Electrical Engineering: Advanced Topics in Communication Networks 1 - 3 Units

Terms offered: Spring 2017, Spring 2016, Fall 2014 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Communication Networks: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Communication Networks: Read Less [-]

EL ENG 290S Advanced Topics in Electrical Engineering: Advanced Topics in Communications and Information Theory 1 - 3 Units

Terms offered: Fall 2018, Fall 2016, Fall 2009 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Communications and Information Theory: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Communications and Information Theory: Read Less [-]

EL ENG 290T Advanced Topics in Electrical Engineering: Advanced Topics in Signal Processing 1 - 3 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Signal Processing: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Signal Processing: Read Less [-]

EL ENG 290Y Advanced Topics in Electrical Engineering: Organic Materials in Electronics 3 Units

Terms offered: Spring 2014, Spring 2013, Fall 2009 Organic materials are seeing increasing application in electronics applications. This course will provide an overview of the properties of the major classes of organic materials with relevance to electronics. Students will study the technology, physics, and chemistry of their use in the three most rapidly growing major applications--energy conversion/generation devices (fuel cells and photovoltaics), organic light-emitting diodes, and organic transistors. Advanced Topics in Electrical Engineering: Organic Materials in Electronics: Read More [+]

Prerequisites: EL ENG 130 ; and undergraduate general chemistry

Instructor: Subramanian

Advanced Topics in Electrical Engineering: Organic Materials in Electronics: Read Less [-]

EL ENG W290C Advanced Topics in Circuit Design 3 Units

Terms offered: Prior to 2007 Seminar-style course presenting an in-depth perspective on one specific domain of integrated circuit design. Most often, this will address an application space that has become particularly relevant in recent times. Examples are serial links, ultra low-power design, wireless transceiver design, etc. Advanced Topics in Circuit Design: Read More [+]

Credit Restrictions: Students will receive no credit for W290C after taking 290C.

Advanced Topics in Circuit Design: Read Less [-]

EL ENG C291 Control and Optimization of Distributed Parameters Systems 3 Units

Terms offered: Fall 2017, Spring 2016, Spring 2015, Spring 2014 Distributed systems and PDE models of physical phenomena (propagation of waves, network traffic, water distribution, fluid mechanics, electromagnetism, blood vessels, beams, road pavement, structures, etc.). Fundamental solution methods for PDEs: separation of variables, self-similar solutions, characteristics, numerical methods, spectral methods. Stability analysis. Adjoint-based optimization. Lyapunov stabilization. Differential flatness. Viability control. Hamilton-Jacobi-based control. Control and Optimization of Distributed Parameters Systems: Read More [+]

Prerequisites: ENGIN 7 and MATH 54 ; or consent of instructor

Also listed as: CIV ENG C291F/MEC ENG C236

Control and Optimization of Distributed Parameters Systems: Read Less [-]

EL ENG C291E Hybrid Systems and Intelligent Control 3 Units

Terms offered: Spring 2021, Spring 2020, Spring 2018 Analysis of hybrid systems formed by the interaction of continuous time dynamics and discrete-event controllers. Discrete-event systems models and language descriptions. Finite-state machines and automata. Model verification and control of hybrid systems. Signal-to-symbol conversion and logic controllers. Adaptive, neural, and fuzzy-control systems. Applications to robotics and Intelligent Vehicle and Highway Systems (IVHS). Hybrid Systems and Intelligent Control: Read More [+]

Formerly known as: 291E

Also listed as: MEC ENG C290S

Hybrid Systems and Intelligent Control: Read Less [-]

EL ENG 297 Field Studies in Electrical Engineering 12 Units

Terms offered: Summer 2024 8 Week Session, Fall 2023, Summer 2023 8 Week Session Supervised experience in off-campus companies relevant to specific aspects and applications of electrical engineering. Written report required at the end of the semester. Field Studies in Electrical Engineering: Read More [+]

Summer: 8 weeks - 1-12 hours of independent study per week

Additional Format: Individual conferences. Individual conferences.

Field Studies in Electrical Engineering: Read Less [-]

EL ENG 298 Group Studies, Seminars, or Group Research 1 - 4 Units

Terms offered: Spring 2023, Spring 2022, Spring 2021 Advanced study in various subjects through special seminars on topics to be selected each year, informal group studies of special problems, group participation in comprehensive design problems, or group research on complete problems for analysis and experimentation. Group Studies, Seminars, or Group Research: Read More [+]

Fall and/or spring: 15 weeks - 0 hours of lecture per week

Additional Format: One to four hours of lectures per unit.

Group Studies, Seminars, or Group Research: Read Less [-]

EL ENG 299 Individual Research 1 - 12 Units

Terms offered: Summer 2024 10 Week Session, Summer 2023 10 Week Session, Spring 2023 Investigation of problems in electrical engineering. Individual Research: Read More [+]

Summer: 6 weeks - 2.5-30 hours of independent study per week 8 weeks - 1.5-22.5 hours of independent study per week

Additional Format: Independent, individual study or investigation. Independent, individual study or investigation. Forty-five hours of work per unit per term.

EL ENG 375 Teaching Techniques for Electrical Engineering 2 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Discussion of effective teaching techniques. Use of educational objectives, alternative forms of instruction, and proven techniques to enhance student learning. This course is intended to orient new student instructors to more effectively teach courses offered by the Department of Electrical Engineering and Computer Sciences at UC Berkeley. Teaching Techniques for Electrical Engineering: Read More [+]

Prerequisites: Teaching assistant or graduate student

Fall and/or spring: 15 weeks - 1.5 hours of seminar per week

Additional Format: One and one-half hours of seminar per week.

Subject/Course Level: Electrical Engineering/Professional course for teachers or prospective teachers

Teaching Techniques for Electrical Engineering: Read Less [-]

EL ENG 602 Individual Study for Doctoral Students 1 - 8 Units

Terms offered: Fall 2016, Fall 2015, Fall 2014 Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. (and other doctoral degrees). Individual Study for Doctoral Students: Read More [+]

Additional Format: Forty-five hours of work per unit per term. Independent study, in consultation with faculty member.

Subject/Course Level: Electrical Engineering/Graduate examination preparation

Contact Information

Department of electrical engineering and computer sciences.

253 Cory Hall

Phone: 510-642-3214

Fax: 510-643-7846

Department Chair

Claire Tomlin, PhD

225 Cory Hall

Phone: 510.642.0253

[email protected]

Vice Chair of Graduate Study and Prelims

Ana Claudia Arias, PhD

508 Cory Hall

[email protected]

John Wawrzynek, PhD

631 Soda Hall

Phone: 510-643-9434

[email protected]

Vice Chair, Masters’ Degree Programs (MEng and MS)

Murat Arcak, PhD

569 Cory Hall

[email protected]

Executive Director, EECS Student Affairs

Susanne Kauer

221 Cory Hall

Phone: 510-642-3694

[email protected]

Director, Graduate Matters; EE Graduate Advisor

Shirley Salanio

217 Cory Hall

Phone: 510-643-8347

[email protected]

Master's Degree Programs Advisor

Michael Sun

215 Cory Hall

Phone: 510-643-8107

[email protected]

CS Graduate Advisor

Jean Nguyen

Phone: 510-642-9413

[email protected]

EE Graduate Admissions

Phone: 510-642-9265

[email protected]

CS Graduate Admissions

Glenna Anton

Phone: 510-642-6285

[email protected]

EECS Graduate Advisor

Tiffany Grimsley

[email protected]

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Simons Institute for the Theory of Computing

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Dear friends, I deeply mourn the passing of Jim Simons, the founding benefactor of the Simons Institute for the Theory of Computing, whose vision, generosity, and commitment to mathematics and the sciences have changed the face of theoretical computer science and the Berkeley campus. His legacy will be felt for generations.

In celebration of Avi Wigderson’s receipt of the ACM A.M. Turing Award this year, we put together a playlist of Avi...

In April, the Simons Institute launched a Research Pod on Resilience in Brain, Natural, and Algorithmic Systems...

Sublinear Algorithms

Machine Learning Research Pod

Quantum Research Pod

Resilience Research Pod

Summer Cluster: AI, Psychology, and Neuroscience

Extended Reunion: Theoretical Foundations of Computer...

Modern Paradigms in Generalization

Special Year on Large Language Models and Transformers...

Cryptography 10 Years Later: Obfuscation, Proof Systems...

Complexity and Linear Algebra

Federated and Collaborative Learning

Communicating Algorithmic Science to the Public | Theoretically Speaking

Using Erasure Codes to Enable interactions over Networks

Logical Quantum Processor Based on Reconfigurable Atom Arrays

Discrepancy Minimization via Regularization

If you think you have been exposed or are a  close contact , you can take an over-the-counter rapid antigen test 3-5 days after exposure. People who have symptoms should stay home, wear a mask, and get  tested .

There are currently no masking requirements on campus. Regardless of COVID-19 case rates, the CDC recommends masking:

• If you have symptoms of a respiratory infection, such as a cough, runny nose, or sore throat
• In crowded indoor settings, such as in airplanes, trains, and buses 
• In public areas around people who are older or have medical conditions that put them at increased risk for severe COVID-19
• For 10 days after having a significant exposure to someone who has tested positive for COVID-19
• If you have tested positive, see additional guidance at  What to Do If You Test Positive for COVID-19 (link is external)

Applications are invited for postdoctoral positions in the research pods on Quantum Computing , on Machine Learning , and on Resilience . 

More information on how to participate in the life of the Institute. 

Quantum Coding Theory (CS 292-242) John Wright Tuesday/Thursday, 11 a.m. – 12:30 p.m., Soda 230

Quantum Algorithms for Scientific Computation  (Math 275) Lin Lin Tuesday/Thursday, 3:30 – 5 p.m.,  Evans 9 (Floor G)

Computational Complexity Theory  (CS 278) Avishay Tal Tuesday/Thursday, 1 – 3:30 p.m., Soda 405

Electrical Engineering & Computer Sciences MS/PhD

The Department of Electrical Engineering and Computer Sciences offers three graduate programs in Electrical Engineering: the Master of Engineering (MEng) in Electrical Engineering and Computer Sciences, the Master of Science (MS), and the Doctor of Philosophy (PhD).

Master of Engineering (MEng)

The Master of Engineering (MEng) in Electrical Engineering & Computer Sciences, first offered by the EECS Department in the 2011-2012 academic year, is a professional masters with a larger tuition than our other programs and is for students who plan to join the engineering profession immediately following graduation. This accelerated program is designed to train professional engineering leaders who understand the technical, economic, and social issues around technology. The interdisciplinary experience spans one academic year and includes three major components: (1) an area of technical concentration, (2) courses in leadership skills, and (3) a rigorous capstone project experience.

Master of Science (MS)

The Master of Science (MS) emphasizes research preparation and experience and, for most students, provides an opportunity to lay the groundwork for pursuing a PhD.

Doctor of Philosophy (PhD)

The Berkeley PhD in EECS combines coursework and original research with some of the finest EECS faculty in the US, allowing students to prepare for careers in academia or industry. Our alumni have gone on to hold amazing positions around the world.

Contact Info

[email protected]

253 Cory Hall

Berkeley, CA 94720

At a Glance

Department(s)

Electrical Engineering & Computer Sciences

Admit Term(s)

Application Deadline

December 11, 2023

Degree Type(s)

Masters / Professional, Doctoral / PhD

Degree Awarded

GRE Requirements

Molecular and Cell Biology

Spring 2024 | New Faculty Profile Wagner

New Faculty Profile | Allon Wagner At the Intersection of Computation and Biology

By Kirsten Mickelwait

Allon Wagner’s interests have always spanned both the sciences and linguistics. Before coming to UC Berkeley to pursue his PhD and postdoc in computer science with Nir Yosef, he earned two master’s degrees: one in computer science and the other in Near Eastern history and languages. These studies fueled a passion for translation that continues to inform his career, only now he studies two scientific languages: molecular biology and computational biology.   

Wagner joined MCB on January 1 as an assistant professor of immunology and molecular medicine, holding a joint appointment with Electrical Engineering & Computer Sciences (EECS) and the Center for Computational Biology (CCB). Specifically, he works at the junction of computational single-cell genomics, immunology, and cellular metabolism. His lab develops data-driven algorithms to study metabolic dysregulation of immunity in cancer, autoimmunity, and other diseases.  

Cellular metabolism regulates our immune system's response to pathogens, tumors, and tissue injury. When immune cells fail to mount the correct metabolic processes, it leads to an ineffective or even adverse immune response. For example, in autoimmune diseases like multiple sclerosis, dysregulated metabolic programs are one of the factors that turn the immune system on the body itself.  

Wagner and his students develop computational approaches to infer the metabolic programs of single cells in our organs, how these cells metabolically interact with one another, and how these processes are dysregulated in disease. Wagner’s lab and collaborators use such in silico methods to study state-of-the-art transcriptomic, multiomic and spatial single-cell assays to achieve a systems-level understanding of metabolism.  

The ways that single-cell genomics have changed biology can be compared to the development of the first microscope—we can suddenly collect a whole new universe of data and reveal an entirely new level of biology. Contrasting these new tools back to when he started his PhD, “It's like comparing a car to a horse-drawn carriage,” Wagner laughs. “And now, our goal is to develop the computational methods needed to take advantage of these new data worlds.”   

Wagner collaborates with MCB faculty studying diverse subjects in immunology—such as Russell Vance, Ellen Robey, and Greg Barton—enabling them to look more deeply at the data generated in their labs. Data-driven work like Wagner’s can suggest new connections in genomic datasets that translate to new hypotheses, which his experimental collaborators can test in the lab, generating more data to analyze. This way, experimentation and computation support one another in deriving novel biological insights. One translational impact of such collaborations will ultimately be identifying metabolic enzymes that can be targeted for therapeutic purposes, for example, bolstering the immune response against tumors, or suppressing it in patients suffering from autoimmunity.  

“I strongly believe in the integration of biological scientists and computational scientists,” Wagner says. “In our field, there are no longer projects led by just one investigator—these projects require multiple leaders.” For these collaborations to work, he says, all sides need to be able to speak with one another. “My lab, for example, brings computational expertise, but we’re versed in the language of immunology and comprehend the experiments and the biological contexts of our work.” It’s necessary to be “bilingual” across disciplines because Wagner’s work is to develop data-science approaches specific to biology's intricacies. “You can’t apply out-of-the-box data-science methods to genomic data because living cells are so unique.”   

Such interdisciplinary collaboration is why Wagner holds his troika of joint appointments at Berkeley. And it’s also characteristic of the university’s vision for the future of science. “It's rare to have a place that’s so globally outstanding, both in computer science and in the immunological sciences,” Wagner says, “coupled with such a supportive ecosystem, as we do with the new College of Data Science (CDSS), the California Institute for Quantitative Biosciences (QB3-Berkeley), UCSF, and the Chan Zuckerberg Biohub. If I could imagine a best-case scenario for my particular field, this appointment would be it. How could I not take it?”   

As he begins his teaching career, Wagner hopes to inspire future generations of students to be bilingual in both the biology and the computational sides, and he’s exploring how to bring those two disciplines closer together at the undergraduate and graduate levels. “ How do you teach an MCB seminar with computational aspects in it?” he asks. “And how do you create a class that’s both effective and enriching for students from all three units—MCB, EECS, and CCB?”   

Currently building his lab in Stanley Hall, he’s excited about the students that will join his group and the culture they’ll create together. He’s curious about the individual perspectives and new ideas they’ll bring to his work; “I say to every student who walks in that I want them to eventually tell me, ‘I have a better approach,’” he says.   

Thinking toward where his discipline is headed, Wagner is curious to see how computation will accelerate discoveries in molecular biology and transform medicine as we know it. “Fifty years into the future, they’ll be looking at our medicine the way we look at the medicine of Pharaonic times,” he says. “And that future will come from the integration of computation and biology.”  

Learn more about research in the Wagner Lab: allonwagnerlab.org

Banner image: Keegan Houser

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Engineering Physics

• Undergraduate Program

Introduction to the Major

The Engineering Science (ES) program is a multi-departmental and interdisciplinary undergraduate program that encompasses closely-related areas of the physical sciences, mathematics and engineering. Students in the ES program acquire knowledge of engineering methods and can pursue their interests in areas of natural science, as well as advanced study in engineering, science, or mathematics. Students choose one of four majors: energy engineering, engineering mathematics and statistics, engineering physics, or environmental engineering science. A minor in energy engineering is also offered .

“ The classes across a variety of departments have allowed me to take a very interdisciplinary approach to engineering. And the great community within this major has taught me how to work with a team .” -  T.G. Mekenzi Roberts, Energy Engineering Science, Class of 2020

ES Major Options 

Energy Engineering interweaves the fundamentals of classical and modern physics, chemistry, and mathematics with energy engineering applications.

Engineering Mathematics and Statistics is the s tudy of pure and applied mathematics as essential components of modern engineering. 

Engineering Physics interweaves classical and modern physics, chemistry, and mathematics with their engineering applications.

Environmental Engineering pairs engineering fundamentals with courses in the environmental and natural sciences.

Amplify Your Major

Get involved with a student group such as Society of Engineering Sciences .

Apply to GLOBE Ambassadors , a learning and travel program for Engineering students .

Pursue a research opportunity for Engineering students .

• Enrich your studies with a minor in Energy and Resources or Sustainability .
• Four-Year Student Timeline

Explore Your Major

Meet with your ESS advisor to discuss your academic plans .

Familiarize yourself with ma jor and college requirements .

Talk to an ES advisor about department programs and research opportunities .

Enroll in ENGIN 98: The Insider's Guide to Berkeley Engineering .

Connect and Build Community

Take advantage of tutoring and workshops for Engineering students .

Find academic support at the Student Learning Center and Center for Access to Engineering Excellence .

Find student opportunities in the ESS newsletter and new student podcast .

Discover Your Passions

Browse research taking place in Engineering centers, institutes, and labs .

Attend the Undergraduate Research and Scholarships Fair in October .

Discover new interests in a Freshman Seminar or student-run DeCal course .

Broaden your perspective by attending Newton Series or View from the Top lectures .

Engage Locally and Globally

Attend the Calapalooza student activities fair and get involved with a student organization .

Find service opportunities through the Public Service Center .

Connect with other students during Engineers Week .

Reflect and Plan Your Future

Visit Berkeley Career Engagement and the Career Counseling Library .

Sign up for Handshake and CareerMail .

Explore career resources on the Engineering website .

• Attend an ESS workshop to create a resume and LinkedIn page .

Second Year

Talk to ESS peer advisors about life in the major .

Meet with your ESS advisor to discuss your academic progress .

Complete lower division prerequisites and start planning your upper division courses .

• Plan now if considering a double major , simultaneous degree , minor , or study abroad .

Join an Engineering student group such as Society of Engineering Sciences .

Get to know Engineering professors and graduate student instructors during their office hours .

Find study space and resources in the Kresge Engineering Library .

Consider pursuing a research opportunity for Engineering and ES students .

Apply to a REU research program. Check Berkeley Lab and UCSF for more research options .

Check out design and maker opportunities at the Jacobs Institute .

Work with a community organization in an American Cultures Engaged Scholarship course such as ENGIN 157AC .

Mentor local youth with Pioneers in Engineering, Berkeley Engineers and Mentors , or Engineering for Kids .

Discuss career options and goals with a Career Educator .

Explore career opportunities through a winter externship and informational interviews .

Learn about graduate and professional school .

• Pursue an internship and attend an internship career fair .

Focus on upper division requirements and electives .

Continue meeting with your ESS advisor to review your academic progress .

Submit paperwork for a double major, simultaneous degree, minor, or study abroad .

Give back by becoming an ESS peer advisor .

Join the Berkeley Engineering group on LinkedIn .

Explore student groups outside of Engineering, and deepen your involvement with an Engineering student group .

Explore your mission and impact as an Engineer through the LeaderShape Institute .

Consider the Sutardja Certificate in Entrepreneurship and Technology or a summer abroad through the European Innovation Academy .

Apply for a research opportunity if you haven’t done so already .

Take your engineering skills international through Engineers Without Borders .

Consider a Berkeley Global Internship such as the Engineering Internship in Toronto .

Experience life at another UC or college on a visitor and exchange program .

• Planning a summer internship abroad? Apply for travel funding from GLOBE Scholars .

Attend career and graduate school fairs such as the STEM Career & Internship Fair .

Discuss graduate school options with advisors and professors .

Sign up for a ESS career workshop , networking dinner , or career conference .

• Make an advising appointment in ESS and explore options such as 5th year MS, MEng, and PhD .

Fourth Year

Meet with your ESS advisor to do an official degree check and plan for your final year .

Complete any “bucket list” courses and remaining major, college, and campus requirements .

Join a professional association such as the Association of Energy Engineers or American Physical Society .

Continue attending tutoring and workshops, and reading the weekly ESS newsletter .

Connect with alumni groups and leverage your network as you prepare to graduate .

Teach your own DeCal course .

Consider being an instructor for ENGIN 98 .

Continue to pursue your interests through a fellowship or gap year after graduation .

• Choose your post-baccalaureate plans based upon your intended mission and impact as an Engineer .

Serve as a student representative on a college committee .

Hone your leadership skills with the Peter E. Haas Public Service Leaders program .

Explore service opportunities after graduation, such as Peace Corps , Teach for America , or U.S. Department of State .

Ask professors and graduate student instructors for recommendation letters .

Utilize job board tools in your job search.  Meet employers at Employer Info Sessions and On-Campus Recruiting .

Attend the job offer negotiation workshop in ESS .

• Apply to jobs, graduate school, and other opportunities .

What Can I Do With My Major?

Graduates in Engineering Science gain a broad foundation for graduate studies in theoretical branches of engineering, as well as in mathematics, and are prepared for careers in specific sectors of industry or business, such as green technology, solar engineering, and environmental firms to name a few.

Jobs and Employers

Data Engineer, Capital One

Data Scientist, Barclays Capital 

Engineer, Northrop Grumman

Hybrid Calibration Engineer, General Motors

Project Coordinator, Climate Corps

Software Engineer, Primus Power

Project Engineer, New Energy Equity 

Research Assistant, California Institute of Technology

Graduate Programs

Aerospace, Aeronautical, and Astronautical Artificial Intelligence and Robotics, PhD

Atomic/Molecular Physics, PhD

Electrical, Electronics, and Communications Engineering, Masters

Engineering, Masters

Materials Engineering, PhD

Physics, PhD

Examples gathered from the First Destination Survey of recent Berkeley graduates .

Connect With Us

Come to Berkeley’s annual Open House in April for information sessions, campus tours, special talks, and more .

Golden Bear Orientation

Join your peers in the campus-wide UC Berkeley orientation program for all new students .

Attend program events with students, staff, and faculty. Visit engineeringscience.berkeley.edu for news and updates .

Visit Engineering Student Services in 230 Bechtel  for advising on academic difficulty, change of major/double majors/simultaneous degrees, withdrawal/readmission, degree completion, education abroad, academic progress, and petitions and exceptions. See engineering.berkeley.edu/students/advising-counseling/ .

Contact the ES Undergraduate Advisor at [email protected] about registration, departmental policy, and campus resources. Meet with an ES Faculty Advisor about coursework, careers in ES, graduate school, letters of recommendation, and summer internships. See engineeringscience.berkeley.edu/faculty/

How to Use this Map

Use this map to help plan and guide your experience at UC Berkeley, including academic, co-curricular, and discovery opportunities. Everyone’s Berkeley experience is different and activities in this map are suggestions. Always consult with your advisors whenever possible for new opportunities and updates.

• What Can I Do with My Major?

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