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Department of Computer Science | Institute of Theoretical Computer Science

CADMO - Center for Algorithms, Discrete Mathematics and Optimization

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Our graduate school offers an exciting opportunity to obtain a PhD in the fields of algorithms, discrete mathematics, and optimization.

Short Bio Marc Pollefeys is a full professor in the Dept. of Computer Science of ETH Zurich which he joined in 2007 and where he leads the Computer Vision and Geometry lab. He is also the Director of the Microsoft Mixed Reality and AI Zurich Lab.   Previously he was with the Dept. of Computer Science of the University of North Carolina at Chapel Hill where he started as an assistant professor in 2002 and became an   associate professor in 2005.   Before he was a postdoctoral researcher at the Katholieke Universiteit Leuven in Belgium , where he also received his M.S. and Ph.D. degrees in 1994 and 1999, respectively. His main area of research is computer vision.  One of his main research goals is to develop flexible approaches to capture visual representations of real world objects, scenes and events. Dr. Pollefeys has received several prizes for his research, including a Marr prize, an NSF CAREER award, a Packard Fellowship and a ERC Starting Grant. He is the author or co-author of more than 280 peer-reviewed papers.  He will be a general chair for ICCV 2019, and was general chair for ECCV 2014 in Zurich, program chair for the IEEE Conf. on Computer Vision  and Pattern Recognition 2009 and was co-chair of the Third Symposium on 3D Data Processing, Visualization and Transmission and has organized workshops and courses at major vision and graphics conferences and has served on the program committees of many conferences. He is a regular reviewer for most of the major vision, graphics and photogrammetry journals.  Prof. Pollefeys is/was on the Editorial Board of the IEEE Transactions on Pattern Analysis and Machine Intelligence, the International Journal of Computer Vision, Foundations and Trends in Computer Graphics and Computer Vision and several other journals.  He is an IEEE Fellow. --> Short Bio Marc Pollefeys is a Professor of Computer Science at ETH Zurich and the Director of the Microsoft Mixed Reality and AI Lab in Zurich where he works with a team of scientists and engineers to develop advanced perception capabilities for HoloLens and Mixed Reality. He was elected Fellow of the IEEE in 2012. He obtained his PhD from the KU Leuven in 1999 and was a professor at UNC Chapel Hill before joining ETH Zurich. He is best known for his work in 3D computer vision, having been the first to develop a software pipeline to automatically turn photographs into 3D models, but also works on robotics, graphics and machine learning problems. Other noteworthy projects he worked on are real-time 3D scanning with mobile devices, a real-time pipeline for 3D reconstruction of cities from vehicle mounted-cameras, camera-based self-driving cars and the first fully autonomous vision-based drone. Most recently his academic research has focused on combining 3D reconstruction with semantic scene understanding. Curriculum vitae - short version [ pdf ] extended version [ pdf ] Research interests Computer vision; 3D-from-video; (self-)calibration; structure-from-motion; simultaneous-localization and mapping (SLAM); camera tracking; camera networks; active vision; robot vision; multiple view geometry; omnidirectional vision; projector-camera systems; image-based modeling and rendering; video-based rendering; computational photography; image and video analysis; applications of computer vision to archaeology, urban modeling, terrain modeling, human-computer interaction, robotics, entertainment, medecine , etc. [ see research page] EPFL EE Distinguished Lecture, April 2014 [ YouTube ] Zurich.Minds 2011 short research presentation [ YouTube ] We work with Google on Project Tango [ YouTube ] [ NBC News ] Relive an event from multiple viewpoints: unstructured video-based rendering [ webpage ] (incudes interactive demo) Reconstructing Rome: 3 million images, 1 day, 1PC... read more about it [BBC] [Chronicle of Higher Education] [Newswise] [UNC] [ReadWriteWeb] [physorg] [futurity] [smarthouse] [CrowdSourcing.org] Some cool stuff with helicopters [ PixHawk ] Video from a 2007 talk at Google [video] Postdoctoral researchers & senior researchers Lubor Ladicky ,  2013- Torsten Sattler , 2013- Martin Oswald , 2014- Current PhD students Yagız Aksoy , Federico Camposeco, Ian Cherabier, Andrea Cohen, Christian Haene, Dominik Honegger, Bastien Jacquet, Peidong Liu, Fabio Maninchedda, Lorenz Meier, Nikolay Savinov, Thomas Schoeps, Lukas Schneider, Pablo Speciale  

Alumni/Past Group Members

• Olivier Saurer, Ph.D. 2016, Immersive Environments: From Virtual Indoor Tours to Outdoor Modelling, now working on start-up project.
• Petri Tanskanen, Ph.D. 2016, Live 3D Reconstruction on Mobile Phones, now working on start-up project.
• Bernhard Zeisl, Ph.D. 2015, Leveraging Geometric Priors and Measurements in 3D Modeling, Calibration and Registration, now at Google Zurich.
• Aparna Taneja , Ph.D. 2014, Geometric Change Detection and Image Registration in Large Scale Urban Environments, now at Google Zurich.
• Lionel Heng, Ph.D. 2014, Toward Full Autonomy for Vision-Guided Robots: From Self-Calibration to Self-Directed Exploration, now at DSO Singapore.
• Jens Puwein, Ph.D. 2014, Computer Vision for Sports Broadcasts and Human Performances, now at VizRT.
• Gim Hee Lee , Ph.D. 2014, Visual Mapping and Pose Estimation for Self-Driving Cars, now assistant professor at NUS. 
• Alexander Schwing , Ph.D. 2013, Inference and Learning Algorithms with Applications to 3D Indoor Scene Understanding, now postdoc at University of Toronto.
• Georges Baatz, Ph.D. 2012, Visual Location Recognition in Man-Made and Natural Environments, now at Google Zurich.
• Roland Angst , Ph.D. 2012, Subspaces in Geometric Computer Vision: Models and Algorithms, now Junior Group Leader at MPI Saarbruecken.
• Rahul Raguram , Ph.D. 2012, co-advised with Jan-Michael Frahm, efficient algorithms for robust estimation, now at Apple.
• David Gallup , Ph.D. 2010, efficient 3D reconstruction of large-scale urban environments from street-level video, now at Google.
• Brian Clipp , Ph.D. 2010, Multi-Camera Simultaneous Localization and Mapping, now at UCR Ventures
• Changchang Wu , Ph.D. 2010, Geometry-driven Feature Detection, now at Google.
• Li Guan , Ph.D. 2009, Multi-view Dynamic Scene Modeling, now at Zillow.
• Sudipta Sinha , Ph.D. 2008, Silhouettes for calibration and reconstruction from multiple views, now researcher at MSR (Redmond)
• Seon Joo Kim , Ph.D. 2008, Radiometric Calibration Methods from Image Sequences, assistant professor at Yonsei University (Korea)
• Jingyu Yan , Ph.D. 2006, Articulated Non-Rigid Shape, Motion and Kinematic Chain Recovery from Video, now at Amazon
• Luca Ballan , 2009-2015, now senior researcher at Apple.
• Amael Delaunoy , 2012-2015, now senior research engineer at Apple.
• Kalin Kolev, 2012-2015, now senior developer at Seene.
• Jean-Charles Bazin , 2011-present, 3D tele-presence
• Friedrich Fraundorfer , 2007-2012, now assistant professor at TUGraz.
• Kevin Koeser , 2009-2013, now senior researcher at GEOMAR.
• Roland Memisevic , 2010-2010, now assistant professor at the Universite de Montreal.
• Christopher Zach , 2008-2011, urban 3D modeling, now researcher at Toshiba Research Cambridge
• Gabriel Brostow , postdoc 2008-2009, 4D event modeling, now senior lecturer at UCL
• Jean- Sebastien Franco , postdoc 2006-2007, multi-view dynamic scene reconstruction, now assistant professor at ENSIMAG /INRIA Grenoble.
• Philippos Mordohai , postdoc 2005-2007, urban 3D modeling, now associate professor at  Stevens Institute of Technology.
• Jan-Michael Frahm , postdoc 2005-2007, associate professsor at UNC-Chapel Hill
• Paul Merrell, M.S. 2007, urban 3D modeling, now with the Gamma group at UNC.
• Sriram-Thirthala Venkata , M.S. 2006, multi-view geometry of non-central cameras, now at Google
• Jason Repko , M.S. 2005, 3D-from-video and 3D-TV, now at USPTO (Graphics and Animation group)
• Oisin Mac Aodha , 2008-2009, optical flow and learning
• Pranav Singh, 2009-2011, multi-view object reconstruction
• Olga Diamanti , 2008-2010 (ETH MS)
• Golnoosh Samei , 2008-2010 (ETH MS)

Main collaborators

• Jan-Michael Frahm ( University of North Carolina )
• Otmar Hilliges, Markus Gross (ETH Zurich)
• Leuven castle image sequence (27 images)

If you want to refer to 3D modeling results we have obtained on these sequences the following paper is a good reference: M. Pollefeys , L. Van Gool , M. Vergauwen , F. Verbiest , K. Cornelis , J. Tops, R. Koch, Visual modeling with a hand-held camera , International Journal of Computer Vision 59(3), 207-232, 2004. [IJCV] [ pdf ]

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ETH AI Center  

Fellowship programs.

Applications for the ETH AI Center PhD and Post-Doc Fellowships are now closed.

We are involved in three phd and post-doc programs.

Please see below a brief overview of the different types of PhD programs. Please note that while the programs are well coordinated, you need to apply for each of the programs separately and also ensure that your motivational letter fits with the corresponding program goals.

ETH AI Center PhD and Post-Doc Fellowships

Our flagship PhD & Post-Doc Program is focused on advancing interdisciplinary AI research . This can be inside the AI foundations or between AI foundations and application areas. The idea is that fellows work with two excellent PIs which carry both equal weights in the supervision.  Fellows have their primary office at the physical space of the ETH AI Center and become part of a cohort . Key selection criteria are ability for research excellence, interdisciplinary collaboration, and impact-orientation.

CLS PhD Fellowships

The Center for Learning Systems (CLS) is a joint initiative between Max Planck Institute for Intelligent Systems (MPI-IS) in Tübingen/Stuttgart, Germany and ETH Zurich. The core idea is to benefit from a combination of advisors of these two AI hubs through a mobility phase where the fellows have spend one year at the institution of the co-advisor.

CLS PhD Fellowship application:  external page https://learning-systems.org/ call_made

ELLIS PhD Fellowships

We are a proud member of  ELLIS - the European Laboratory for Learning and Intelligent Systems - which is a pan-European AI network of excellence which focuses on fundamental science, technical innovation and societal impact. The ELLIS PhD Fellowships enable a single central application for PhD positions across Europe. A mobility component to spend at least 6 month at another ELLIS unit or choose an industry track is a must.

ELLIS PhD application: external page https://ellis.eu/phd-postdoc call_made

Opportunities in the lab

We have open PhD positions. At ETH Zürich, PhD students can start anytime during the year as long as they have finished their master's degree . Suitable backgrounds for candidates include Computer Science , Software Engineering , Electrical Engineering , Robotics , Statistics , Bioengineering .

Application materials

No cover letter necessary, but please submit:

• research plan (≤ 0.5 pages on your desired area of work, goals, and ambitions),
• alignment (≤ 0.5 pages on how your plan relates to two or more of our recent projects ),
• names and contacts of two references,
• transcripts of records (both undergrad and master's).

Make your application complete and specific to our research!

Direct Doctorate: PhD program for bachelor graduates

The Direct Doctorate is open to exceptional undergrad applicants at the Department of Computer Science. Included are tuition waivers and a stipend for a 2-year research master's program. Candidates automatically enroll into the regular PhD program afterwards.

Applications for the Direct Doctorate ( link ) are open November 15 – December 15, 2024.

PhD positions (for master graduates)

If you have a strong background in one of the areas listed up top, your application for a PhD should include a couple of sentences on your projects so far, your interests, and how they relate to our research. Check out some of our recent projects before getting in touch!

Make sure to include the four items listed above in your application! We won't be able to respond to incomplete applications unfortunately.

Postdoc positions

We support strong candidates for their application to the ETH Fellowship program (deadlines March 1 and September 1). This opportunity fully funds a two-year postdoc stay including salary, equipment, travel, conference attendance, etc. If you have a strong track record in one of the areas listed above, please read through the program description and contact us.

External master's theses

If your interests and skills are an exceptionally good fit with our research projects, ETH can admit external students for a master's thesis. In this case, please contact Christian with a short motivation statement and include the documents listed above.

Research internships

We have hosted strong candidates for research internships or visits in the past , which often resulted in publications at top-tier venues. Research internships are available for exceptional students with a strong track record who are enrolled in master's or PhD programs outside ETH Zürich.

Student Summer Research Fellowship (next deadline: December 2024)

Bachelor's and master's students can apply for the Student Summer Research Fellowship at the Department of Computer Science. The program covers two-month internship including a VISA, travel, a stipend, and housing in Zurich .

Apply by December 15, 2024 on the department's summer fellowship application portal .

ETH Zurich is a great place to work on exciting research! Check out the teaser:

Facts about ETH Zürich

• ETH has one of the top computer science departments in the world (e.g., 3rd in a recent CS ranking ).
• ETH has world-class researchers, plenty of collaborations, and excellent facilities and funding.
• Full scholarships are available for outstanding B.Sc./M.Sc. students interested in a PhD.
• PhD and postdoc positions are fully funded including social benefits.
• Zürich is consistently ranked among the top destinations in the world for quality of life.

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Institute for Machine Learning

Main content.

We are dedicated to learning and inference of large statistical models from data. Our focus includes optimization of machine learning models, validation of algorithms and large scale data analytics. Data driven scientific modeling permeates all areas of natural science, engineering, social science and more recently also humanities. The resulting methodological challenges strongly suggest to combine high performance algorithmics and cutting edge statistical modeling. Applications range from medicine and the life sciences to distributed sensing and natural language processing.

The institute includes ten research groups:

• Computational genetics and epigenetics of cancer (Prof. Valentina Boeva)
• Information Science and Engineering (Prof. Joachim Buhmann)
• external page Rycolab (Prof. Ryan Cotterell) call_made
• Optimization and Decision Intelligence Group (Prof. Niao He)
• Data Analytics (Prof. Thomas Hofmann)
• Learning and Adaptive Systems (Prof. Andreas Krause)
• Biomedical Informatics (Prof. Gunnar Rätsch)
• external page Learning Reasoning and Education Lab (Prof. Mrinmaya Sachan) call_made
• Medical Data Science (Prof. Julia Vogt)
• Statistical Machine Learning (Prof. Fanny Yang)  

The institute administrates

the external page Max Planck ETH Center for Learning Systems call_made

I am looking for highly motivated postdocs and PhD students with a strong background in theoretical computer science, mathematics, or statistics. If you are interested, please contact me.

selected events ( all events )

Curriculum vitæ ( web / pdf ).

• ERC Consolidator Grant, 2019
• Microsoft Research Faculty Fellowship, 2014
• Simons Collaboration: Algorithms and Geometry, 2014–2017
• Alfred P. Sloan Research Fellowship, 2014
• NSF CAREER Award, 2014
• NSF AF Medium 1408673, 2014

group and alumni

• Stefan Tiegel (PhD student, 2020–now)
• Rares Buhai (PhD student, 2020–now)
• Jingqiu Ding (PhD student, 2020–now)
• Rajai Nasser (postdoc, 2020–now)
• Chih-Hung Liu (postdoc, 2020–now)
• Tommaso d'Orsi (PhD student, 2018–now)
• Gleb Novikov (PhD student, 2017–now)
• Sam Hopkins (PhD student 2013–2018; now: Miller fellow at UC Berkeley; starting 2021–2022: tenure-track assistant professor at MIT)
• Jonathan Shi (PhD student 2013–2019; now: postdoc with Luca Trevisan at Università Bocconi)
• Aaron Potechin (postdoc 2017; now: assistant professor at U Chicago)

selected papers ( all papers )

Program committees.

• COLT 2020 (senior pc)
• APPROX 2018 (chair)
• APPROX 2015
• APPROX 2012

• About Background Center Activities Participation
• Research Research Topics Publications
• People Organization Members PostDoc PhD Students
• PhD Program Program Basics Student Information How to Apply

Max Planck ETH Center for Learning Systems

A joint research center of ETH Zurich and the Max Planck Society

The Max Planck ETH Center for Learning Systems (CLS) addresses cross-disciplinary research questions in the design and analysis of natural and man-made learning systems. The excellent engineering competences of the faculty and research team members at the ETH Zurich in Switzerland ideally complement the competences in natural sciences and computer science at the Max Planck Institute for Intelligent Systems, Tübingen/Stuttgart in Germany. Together we want to build a lighthouse for machine learning and modern artificial intelligence in Europe.

The cooperation spans all levels, from leading experts at the senior scientist level to junior scientists obtaining their PhD. Around 50 faculty members are engaged at CLS, drawn from professors from ETH Zurich, directors and group leaders from the Max Planck Institute for Intelligent Systems and selected faculty from external partners. For how to get involved with CLS, see Participation .

At the heart of CLS is its doctoral fellowship program . PhD students profit from joint supervision by ETH Professors and MPI Directors and Group Leaders, ultimately obtaining their doctoral degree from ETH Zurich. With these unique opportunities, CLS offers young academics in the field of learning systems the chance of outstanding collaborations and exchange early in in their careers. Students and young scientists also benefit from summer schools, workshops, and internships. Currently, around 30 PhD researchers are following this route. For application information, see How to Apply .

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The Max Planck Institute for Intelligent Systems has campuses in Stuttgart  and Tübingen. The institute combines theory, software, and hardware expertise in the research field of intelligent systems. The Tübingen campus focuses on how intelligent systems process information to perceive, act, and learn through research in the areas of machine learning, computer vision, and human-scale robotics. Our Stuttgart campus has world-leading expertise in micro- and nano-robotic systems, haptic perception, human-robot interaction, bio-hybrid systems, and medical robotics.

With its first-class infrastructure and international faculty, ETH Zurich is regularly ranked amongst the very best European Universities. ETH Zurich is also one of the universities in Europe which focuses most intensively on research; doctoral students account for 4,100 out of a total of 21,000 students. Around 35 ETH professors participate in CLS, primarily drawn from the Departments of Computer Science (D-INFK); Information Technology and Electrical Engineering (D-ITET); and Mechanical and Process Engineering (D-MAVT); as well as from the Departments of Mathematics (D-MATH); Physics (D-PHYS); Civil, Environmental and Geomatic Engineering (D-BAUG); Biosystems Science and Engineering (D-BSSE); and Health Sciences and Technology (D-HEST).

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Department of Astrophysics

Quicklinks und sprachwechsel, main navigation, phd programme in computational science, graduate school for computational science.

The PhD programme in Computational Science is a joint initiative of the University of Zurich and ETH Zurich, which aims to:

• Foster sustained interactions between PhD students, faculty and staff
• Promote exchanges between academia, industry and society enabled by computational science

Computational Science (CS) integrates Information Technology and Mathematics to address scientific and societal challenges such as Energy, Health, Climate and Finance.

CS is a unique method of scientific inquiry, complementing theory and experiments, providing us with unprecedented potential for scientific discovery and innovation.

Application and enrollment

The Computational Sciences PhD programme is a 3-4year MSc to PhD program. Participating students can choose from a broad selection of research topics and classes. Enrollment into the program is decided by an admission committee. In order to graduate, students need to fulfill the following requirements:

• Acquisition of 12 credit points of advanced courses
• Deposition and defense of a Ph.D. thesis describing the student’s original research work,
• Additional requirements imposed by the host institution (University of Zurich or ETH Zurich) so as to comply fully with their regulations.

The final degree is conferred by either the University of Zurich or the ETH Zürich, depending on the academic affiliation of the host laboratory.

Official PhD regulations for the Computational Science Programme: PDF EN / PDF DE

Link to the Regulations Governing PhD Studies at the MNF

Link to the official page of the Graduate School for CS

Admissions to Promotion - Useful links and documents:

Admission to Doctoral Studies at the University of Zurich

Factsheet Admission to a PhD Program at the Department of Astrophysics (PDF, 621 KB) Checklist for PhD students

"Confirmation of Supervision for Doctoral Students" (PDF, 242 KB)

"Acceptance Confirmation Structured Doctoral Program" (PDF, 225 KB) Annual committee meeting minutes (PDF, 1 MB)

Confirmation Sheet - TA Hours (PDF, 224 KB)

Fact sheet PhD Defence (EN) / Merkblatt (DE)

Program Director Prof. Dr. Robert Feldmann [email protected]

Computer-Science Majors Graduate Into a World of Fewer Opportunities

C omputer science is hotter than ever at U.S. universities. But students graduating this month are discovering their degrees are no longer a surefire ticket to tech-industry riches.

In fact, many are finding it harder than they ever thought it would be to land a job.

Tech giants that were expanding aggressively just a few years ago now have less need for entry-level hires—or are shedding jobs. They are also, increasingly, turning their focus to artificial intelligence, a technology many fear could reduce the need for coders. Postings on jobs website Indeed for software-development roles, a proxy for computer science, have dropped 30% from prepandemic levels.

At the same time, companies have a burgeoning supply of new grads to choose from. The number of students in the U.S. majoring in computer and information science has jumped 40% in five years, to more than 600,000 as of 2023. The number of bachelor’s degrees conferred in those majors topped 100,000 in 2021, according to the Department of Education, a 140% rise from 10 years earlier.

Students who once had jobs and summer internships lined up by Thanksgiving are now broadening their searches.

“When I tell people I’m in computer science, they’re like, ‘Lucky you. You’re going to make a lot of money. You can do anything you want,’” said Ben Riesett, a 22-year-old who graduated this month from Catholic University of America. “The truth is, when you start looking right now, it’s impossible to get hired.”

Just a few years ago, Riesett heard constantly that employers needed staff with his skill set; now, his classmates with jobs got them through friends or family, or from internships. Riesett, in Washington, D.C., said he’s applied to entry-level roles all over the country and received only a few responses.

To be sure, comp-sci majors from top-tier schools can still get jobs. Pay, projected to be at about $75,000, is at the high end of majors reviewed by the National Association of Colleges and Employers, or NACE. They are just not all going to Facebook or Google.

“Job seekers need to reset their expectations,” said Tim Herbert, chief research officer at CompTIA, a trade group that follows the tech sector. “New grads may need to adjust where they’re willing to work, in some cases what salary, perks or signing bonus they’ll receive, and the type of firm they’ll work for.”

And while big tech companies are hiring for AI-related jobs, Herbert said, many of those positions require more experience than a new grad would have.

Salaries for this year’s graduates in computer science are expected to be just 2.7% higher than last year’s, the smallest increase of eight fields reviewed by NACE.

In the past 18 months, job growth has remained flat for software publishers, a group of employers that includes software developers, according to the Labor Department. On the student jobs platform Handshake, the number of full-time jobs recently posted for tech companies is down 30% from the year-ago period.

Looking farther afield

Jarin Rahman, an information-science major who just completed her junior year at Cornell University, had long assumed she would try to land a job at a large tech company. As a first-generation college student, she thought it was the best path.

But Rahman knows lots of seniors who interned at big tech companies last summer and didn’t get return offers. With the tech industry appearing less stable than she anticipated, she started looking elsewhere.”

“It made me really stressed out because I know that I have to find a way to support my family,” she said.

This summer, she’ll intern at a real-estate investment management firm.

Stephanie Johnson, a career development lead with the computer-science department at the University of North Carolina, said her students on average were applying to 150 or more jobs. Many were still waiting for offers in the spring.

Just a few years ago, they might apply for 20 or 40 positions—and get a final offer in the fall.

Johnson said she has seen more students focused on companies where tech is a service and not the product. She knows two students who were offered roles at big tech companies but declined: One went to a bank, the other to a retailer.

The University of Virginia typically offers a large career fair called Tech Night Takeover, hosting big companies for a night of networking. This year, the school expanded the event to include employers in healthcare, energy and financial services hiring for tech roles.

Hand-delivered applications

The pipeline is bursting with comp-sci students who will need jobs in the next few years. Computer and information science is the fastest-growing top-20 major in the U.S. at four-year colleges, according to the National Student Clearinghouse Research Center. It is the fourth-most-popular major overall. Between 2018 and 2023, the number of students majoring in computer and information science jumped from about 444,000 to 628,000.

Pierce Avner, who just finished his junior year at the University of Colorado, Boulder, said he applied online for hundreds of internships last year. The majority never responded. Of those that did, many sent back a rejection note within 15 seconds to a minute.

This year, he took an old-fashioned route, hand-delivering about 20 applications to tech companies around Denver and Boulder. He also sent emails to alumni from the Theta Tau professional engineering fraternity. He is set to intern at an aerospace company this summer, one of the employers he found through his fraternity networking.

Alex Giang, a computer-science major who just finished his junior year at Cornell, is preparing to start a software-engineering internship at a digital-advertising company. Applications to Uber, Airbnb, Tesla, Facebook, Apple and Amazon didn’t pan out.

Friends are adjusting, too. Their new mentality is: “If you get a job, even if you don’t like it, you need to take it because you don’t know if you’re going to get anything else,” Giang said.

Write to Katherine Bindley at [email protected], Corrie Driebusch at [email protected] and Lindsay Ellis at [email protected]

NSF Awards Graduate Research Fellowships and Honors to Natural Sciences Students

Dozens of College of Natural Science students received recognition through the National Science Foundation program.

The National Science Foundation recently announced Graduate Research Fellowships (NSF GRFP) and honorable mentions for dozens of College of Natural Sciences graduate students and graduating undergraduates at The University of Texas at Austin. 

The NSF GRFP is a prestigious and competitive fellowship that supports outstanding graduate research across the country. Fellows are anticipated to become knowledge experts who can contribute significantly to research, teaching and innovations in science and engineering. These individuals are crucial to maintaining and advancing the nation’s technological infrastructure and national security as well as contributing to the economic well-being of society at large. 

Fellows receive a three-year annual stipend of $37,000 as well as a cost-of-education allowance of $16,000 to conduct research at any accredited U.S. graduate school. 

This spring, six Texas Science undergraduates and nine graduate students learned they had won fellowships. Additionally, two undergraduate students and 12 graduate students were awarded honorable mentions. Below are the recipients, their fields of study and their research focuses.

2024 Graduate Research Fellowship Recipients 

• Luisa Marie Velasco, Mathematics graduate student — Analysis
• Audrick Moses Pyronneau, Mathematics graduate student — Topology
• Shankar Padmanabhan, Mathematics undergraduate — Natural Language Processing
• Leo Orshansky, Computer Science undergraduate student — Unconventional Computing, Quantum Computing
• Sara Nixon, Chemistry graduate student — Chemistry of Life Processes
• Lacie Larschan Levy, Biology undergraduate student — Geosciences, Chemical Oceanography
• Alexandra Lee, Physics graduate student — Condensed Matter Physics
• Mathea Kurtz-Shaw, Marine Science graduate student — Geosciences, Arctic-Antarctic
• Stephane Hatgis-Kessell, Computer Science undergraduate student — Artificial Intelligence
• Jennie Elizabeth DeVore, Ecology, Evolution and Behavior graduate student — Animal Behavior 
• Hailey A. Currie, Physics undergraduate student — Physics of Living Systems
• Jason R. Boynewicz, Physics graduate student — Atomic, Molecular and Optical Physics
• Duaa N. Ansari, Chemistry undergraduate student — Chemistry of Life Processes
• Scott A. Wicker - Chemistry graduate student — Chemical Structure, Dynamics and Mechanism
• Hollis B. Akins, Astronomy graduate student — Astronomy and Astrophysics

2024 Honorable Mentions 

• Alexandra Boville, Ecology, Evolution and Behavior graduate student — Evolutionary Biology
• Tynan Gardner, Cell and Molecular Biology graduate student — Systems and Molecular Biology
• Ravi K. Koka, Physics undergraduate student — Nuclear Physics
• Zachary Lee, Mathematics graduate student — Mathematical Sciences
• Crystal B. Li, Human Development and Family Sciences graduate student — Human Development and Family Sciences
• Connor A. Painter, Astronomy graduate student — Astronomy and Astrophysics
• Livia Raulinaitis, Ecology, Evolution and Behavior graduate student — Ecology
• Abhishek Shivkumar, Mathematics graduate student — Topology
• Michael H. Updike, Physics undergraduate student — Theoretical Physics
• Arthur King Zhang, Computer Science graduate student — Robotics
• Elijah S. Garcia, Chemistry graduate student — Chemistry of Life Processes
• Jason Ho, Computer Sciences graduate student — Computer Architecture 
• Jacob L. Block, Computer Sciences graduate student — Machine Learning
• Tamara M. Rivera, Marine Science graduate student — Marine Biology

Adapted from a post by the Graduate School .

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• Graduate Students
• College and Campus

Celebrating the 2024 College of Natural Sciences Dean’s Honored Graduates

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Computer Science Ph.D.

Learning objectives, success rates.

8-year graduation rate

Avg. years to degree

MEDIAN EARNINGS

10 years after graduation

5 years after graduation

1 year after graduation

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CompSci student awarded PhD scholarship

The Department of Computer Science is delighted to announce that Nicholas Leisegang has been awarded a Faculty of Science PhD scholarship.

Originally from KwaZulu-Natal, Nicholas moved to Cape Town 2018 to start his academic career, however he did not initially pursue computer science, but instead took mathematics for both his Bachelors and Masters degrees. "While studying logic in a pure maths setting, I became increasingly interested in the uses of logic in computer science, and when I heard about Prof. Meyer’s research unit I was immediately fascinated by the types of logics and their AI applications which he works on. I also thought that the postgrad students and academics who I engaged with in the department before signing up seemed like really nice people to work with," said Nicholas.

With an interest in various different types of formal logics that occur within the context of Artificial Intelligence, Nicholas is fascinated by Defeasible Reasoning, Standpoint Logics, and Description logics, and their applications in explainable AI and large database systems. Working under the supervision of Professor Tommie Meyer, co-director of the Artificial Intelligence Research Unit (AIRU), Nicholas hopes to work with other international academics, in particular Prof Sebastian Rudolph at the computational logic research unit at the Technical University of Dresden.

For students starting out, Nicholas advises  being actively engaged with your work and with other students/researchers around you. "I’ve found this is the thing that’s kept me most motivated and helped me discover fields of study that I am passionate about."

The  Artificial Intelligence Research Unit (AIRU) aims to provide research and thought leadership to academia, industry, government and civil society, so that they can leverage AI to improve people’s lives. AIRU is based in the Department of Computer Science in the Faculty of Science at the University of Cape Town, and is led by Prof Tommie Meyer and Prof Deshen Moodley. 

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Information-theoretic measures in machine learning

May 15, 2024 By Katie Satterlee

• Current Students
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From recognizing complex patterns to making predictions about future outcomes, the applications of machine learning are constantly evolving. 

Dr. Ruida Zhou, a recent electrical and computer engineering doctoral graduate, is working on designing and analyzing algorithms to solve machine learning problems using information theory. Information theory is understanding how to store, transmit, process and measure information.

One concept he’s researching is called generalization. 

“Imagine you give a machine images of cats and dogs,” Zhou said. “Somehow, it can understand the concepts of cats and dogs. This is called generalization.”

Zhou is using information theory to quantify and interpret errors that occur in generalization. 

Generalization error refers to the difference in how well a model performs when given new data versus training data, which is data it is already familiar with. Information-theoretic measures capture the amount of information acquired by the model from the training data, giving a reasonable generalization interpretation.

We have used information-theoretic measures to facilitate the design of a safe and robust learning agent that can make decisions such as autonomous driving.

“Interpreting generalization is one example of what information-theoretic measures are capable of doing,” Zhou said. “We have used information-theoretic measures to facilitate the design of a safe and robust learning agent that can make decisions such as autonomous driving.”

Zhou took this research a step further to study an aspect of machine learning called reinforcement learning by collaborating with professors from the Department of Electrical and Computer Engineering, which include his advisor, Dr. Chao Tian, along with Dr. Dileep Kalathil and Dr. P. R. Kumar. 

Reinforcement learning is when a computer program learns to make decisions through trial and error. The program receives feedback in the form of rewards and punishments, adjusting its behavior over time.

Zhou received the 2024 Association of Former Students Distinguished Graduate Excellence in Research Doctoral Award for his work. This accolade recognizes exceptional contributions to research and dedication to innovation. 

“We’re witnessing the rapid development of artificial intelligence technologies,” Zhou said. “This is an exciting time for conducting machine-learning research as an information theorist.”

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Meet the Outstanding Faculty Award Recipients of 2024

• by Jessica Heath and Matt Marcure
• May 20, 2024

Recognizing excellence in research and teaching, the College of Engineering celebrates its faculty members with outstanding faculty awards yearly. Departments nominate faculty members who are selected to receive the honors by the college's Faculty Awards Committee.    

Four faculty members will receive outstanding faculty awards at the College of Engineering Awards Celebration on June 3.    

Outstanding Faculty Teaching Award   

Angelique louie , biomedical engineering   .

Angelique Louie's passion for education has left an indelible mark on the Department of Biomedical Engineering.   

It is because of her pioneering work that the department's undergraduate senior design series focuses on training through translational design, offering students the opportunity to solve real-world problems from institutions like the UC Davis School of Veterinary Medicine. Louie has also created several keystone courses for the undergraduate program and has developed classes meant to foster student success, such as one for freshmen and sophomores interested in undergraduate research.   

Her dedication to teaching has led to several leadership positions. Louie served as the faculty director of the Undergraduate Research Center from 2013–16 and provided critical support for the university, receiving high-profile awards like the Beckman Scholars Program from the Arnold and Mabel Beckman Foundation. It was also due to her spearheading efforts that the Department of Biomedical Engineering received the first ABET accreditation for its undergraduate program, which ensures graduates have the requisite skills to enter the global workforce.   

Louie joined UC Davis in 2002. She received her bachelor's in electrical engineering from UC Davis and holds a master's in electrical engineering from UCLA and a Ph.D. in cell biology from UC Irvine.    

Outstanding Junior Faculty Award   

Scott j. mccormack , materials science and engineering   .

An expert in high-temperature ceramics, Scott McCormack joined the college in 2019, where he applies his skills in materials synthesis, crystallography and calorimetry for the discovery of new materials that can withstand the harshest conditions, like ultra-high temperatures, for applications in hypersonic platforms, space exploration and nuclear fission and fusion reactors.   

To experiment on materials at temperatures up to 4,000 degrees Celsius, McCormack designed an environmental conical nozzle levitator with dual-wavelength lasers to study ultra-high temperature materials and ceramics in a controlled, container-free environment.    

McCormack is currently involved in two projects funded by the Air Force Office of Scientific Research. He is the lead PI on a congressionally funded program that focuses on using ultra-high-temperature ceramics for hypersonic platform manufacturing. He also serves as co-PI on a project supported through the office's Multi-University Research Initiative, which focuses on investigating compositionally complex ceramics for hypersonic platforms.   

McCormack earned his bachelor's degree in materials engineering from the University of Wollongong in Australia and his Ph.D. in materials science and engineering from the University of Illinois at Urbana-Champaign. He received an NSF CAREER Award in 2021 and an Excellence in Teaching Award from the UC Davis College of Engineering in 2023.    

Outstanding Mid-Career Faculty Award  

Vladimir filkov , computer science   .

Vladimir Filkov turns working with data into an art form by seeing what is possible within the details. A consummate data scientist, Filkov has been applying data science, AI and machine learning methods to several areas including software engineering, life sciences and, most recently, medicine.   

Since arriving at UC Davis in 2002, his contributions have been recognized many times over, including five Distinguished Paper Awards and two Test of Time Awards from the Association for Computing Machinery, or ACM, International Conference on the Foundations of Software Engineering. The latter honors were for his formative papers on the community structure within open-source software projects and the potential impact of biased datasets on the performance of prediction techniques of bug-detection software.   

As the director of translational data science for the UC Davis DataLab, Filkov oversees projects and programs that build data science bridges with UC Davis Health. In one such project with UC Davis doctors, he and his team developed and deployed a model of patient admissions during the pandemic, used daily by nurses and doctors to anticipate resource needs.  

Filkov received his M.S. and Ph.D. in computer science at Stony Brook University. He was named an ACM Distinguished Member in 2020.  

Outstanding Senior Faculty Award   

Chen-nee chuah , electrical and computer engineering   .

A global leader in communication networks and data science, Chen-Nee Chuah has published over 240 refereed papers on networking, cybersecurity and machine learning. Her significant contributions include the development of Multiple-Input-Multiple-Output wireless systems (resulting in Wi-Fi networks dozens of times faster than previous models) and large-scale internet measurement techniques. Recently, Chuah has focused on leveraging smart devices and edge intelligence to improve on- and off-site medical care.   

Chuah has led or co-led 19 NSF, NIH and DoD funded projects, including a recent NSF TRIPOD award that has helped establish UC Davis as a data science hub, as well as grants through the UC Noyce Initiative to advance cybersecurity and computational health. She has also received the UC Davis ADVANCE award for her commitment to increasing opportunities for women in engineering and computer science and recently became a fellow of the American Association for the Advancement of Science.   

Before joining UC Davis in 2002, Chuah earned her Ph.D. and master's from the Department of Electrical Engineering and Computer Sciences at UC Berkeley. She is the first woman to hold the title of Child Family Endowed Professorship in Engineering, a distinction she received for her nationally recognized scholarship.    

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