master thesis data science

Thesis/Capstone for Master's in Data Science | Northwestern SPS - Northwestern School of Professional Studies

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Data Science

Capstone and thesis overview.

Capstone and thesis are similar in that they both represent a culminating, scholarly effort of high quality. Both should clearly state a problem or issue to be addressed. Both will allow students to complete a larger project and produce a product or publication that can be highlighted on their resumes. Students should consider the factors below when deciding whether a capstone or thesis may be more appropriate to pursue.

A capstone is a practical or real-world project that can emphasize preparation for professional practice. A capstone is more appropriate if:

• you don't necessarily need or want the experience of the research process or writing a big publication
• you want more input on your project, from fellow students and instructors
• you want more structure to your project, including assignment deadlines and due dates
• you want to complete the project or graduate in a timely manner

A student can enroll in MSDS 498 Capstone in any term. However, capstone specialization courses can provide a unique student experience and may be offered only twice a year. 

A thesis is an academic-focused research project with broader applicability. A thesis is more appropriate if:

• you want to get a PhD or other advanced degree and want the experience of the research process and writing for publication
• you want to work individually with a specific faculty member who serves as your thesis adviser
• you are more self-directed, are good at managing your own projects with very little supervision, and have a clear direction for your work
• you have a project that requires more time to pursue

Students can enroll in MSDS 590 Thesis as long as there is an approved thesis project proposal, identified thesis adviser, and all other required documentation at least two weeks before the start of any term.

From Faculty Director, Thomas W. Miller, PhD

Capstone projects and thesis research give students a chance to study topics of special interest to them. Students can highlight analytical skills developed in the program. Work on capstone and thesis research projects often leads to publications that students can highlight on their resumes.”

A thesis is an individual research project that usually takes two to four terms to complete. Capstone course sections, on the other hand, represent a one-term commitment.

Students need to evaluate their options prior to choosing a capstone course section because capstones vary widely from one instructor to the next. There are both general and specialization-focused capstone sections. Some capstone sections offer in individual research projects, others offer team research projects, and a few give students a choice of individual or team projects.

Students should refer to the SPS Graduate Student Handbook for more information regarding registration for either MSDS 590 Thesis or MSDS 498 Capstone.

Capstone Experience

If students wish to engage with an outside organization to work on a project for capstone, they can refer to this checklist and lessons learned for some helpful tips.

Capstone Checklist

• Start early — set aside a minimum of one to two months prior to the capstone quarter to determine the industry and modeling interests.
• Networking — pitch your idea to potential organizations for projects and focus on the business benefits you can provide.
• Permission request — make sure your final project can be shared with others in the course and the information can be made public.
• Engagement — engage with the capstone professor prior to and immediately after getting the dataset to ensure appropriate scope for the 10 weeks.
• Teambuilding — recruit team members who have similar interests for the type of project during the first week of the course.

Capstone Lesson Learned

• Access to company data can take longer than expected; not having this access before or at the start of the term can severely delay the progress
• Project timeline should align with coursework timeline as closely as possible
• One point of contact (POC) for business facing to ensure streamlined messages and more effective time management with the organization
• Expectation management on both sides: (business) this is pro-bono (students) this does not guarantee internship or job opportunities
• Data security/masking not executed in time can risk the opportunity completely

Publication of Work

Northwestern University Libraries offers an option for students to publish their master’s thesis or capstone in Arch, Northwestern’s open access research and data repository.

Benefits for publishing your thesis:

• Your work will be indexed by search engines and discoverable by researchers around the world, extending your work’s impact beyond Northwestern
• Your work will be assigned a Digital Object Identifier (DOI) to ensure perpetual online access and to facilitate scholarly citation
• Your work will help accelerate discovery and increase knowledge in your subject domain by adding to the global corpus of public scholarly information

Get started:

• Visit Arch online
• Log in with your NetID
• Describe your thesis: title, author, date, keywords, rights, license, subject, etc.
• Upload your thesis or capstone PDF and any related supplemental files (data, code, images, presentations, documentation, etc.)
• Select a visibility: Public, Northwestern-only, Embargo (i.e. delayed release)
• Save your work to the repository

Your thesis manuscript or capstone report will then be published on the MSDS page. You can view other published work here .

For questions or support in publishing your thesis or capstone, please contact [email protected] .

LIBRARIES | ARCH

Data science masters theses.

The Master of Science in Data Science program requires the successful completion of 12 courses to obtain a degree. These requirements cover six core courses, a leadership or project management course, two required courses corresponding to a declared specialization, two electives, and a capstone project or thesis. This collection contains a selection of masters theses or capstone projects by MSDS graduates.

Collection Details

• Thesis Option

Data Science master’s students can choose to satisfy the research experience requirement by selecting the thesis option. Students will spend the majority of their second year working on a substantial data science project that culminates in the submission and oral defense of a master’s thesis. While all thesis projects must be related to data science, students are given leeway in finding a project in a domain of study that fits with their background and interest.

All students choosing the thesis option must find a research advisor and submit a thesis proposal by mid-April of their first year of study. Thesis proposals will be evaluated by the Data Science faculty committee and only those students whose proposals are accepted will be allowed to continue with the thesis option.  

To account for the time spent on thesis research, students choosing the thesis option are able substitute three required courses (the Capstone and two "free" elective courses (as defined in the final bullet point on the degree requirement page )) with AC 302.

In Applied Computation

• How to Apply
• Learning Outcomes
• Master of Science Degree Requirements
• Master of Engineering Degree Requirements
• CSE courses
• Degree Requirements
• Data Science courses
• Data Science FAQ
• Secondary Field Requirements
• Advising and Other Activities
• AB/SM Information
• Alumni Stories
• Financing the Degree
• Student FAQ

MIT Libraries home DSpace@MIT

• DSpace@MIT Home
• MIT Libraries

This collection of MIT Theses in DSpace contains selected theses and dissertations from all MIT departments. Please note that this is NOT a complete collection of MIT theses. To search all MIT theses, use MIT Libraries' catalog .

MIT's DSpace contains more than 58,000 theses completed at MIT dating as far back as the mid 1800's. Theses in this collection have been scanned by the MIT Libraries or submitted in electronic format by thesis authors. Since 2004 all new Masters and Ph.D. theses are scanned and added to this collection after degrees are awarded.

MIT Theses are openly available to all readers. Please share how this access affects or benefits you. Your story matters.

If you have questions about MIT theses in DSpace, [email protected] . See also Access & Availability Questions or About MIT Theses in DSpace .

If you are a recent MIT graduate, your thesis will be added to DSpace within 3-6 months after your graduation date. Please email [email protected] with any questions.

Permissions

MIT Theses may be protected by copyright. Please refer to the MIT Libraries Permissions Policy for permission information. Note that the copyright holder for most MIT theses is identified on the title page of the thesis.

Theses by Department

• Comparative Media Studies
• Computation for Design and Optimization
• Computational and Systems Biology
• Department of Aeronautics and Astronautics
• Department of Architecture
• Department of Biological Engineering
• Department of Biology
• Department of Brain and Cognitive Sciences
• Department of Chemical Engineering
• Department of Chemistry
• Department of Civil and Environmental Engineering
• Department of Earth, Atmospheric, and Planetary Sciences
• Department of Economics
• Department of Electrical Engineering and Computer Sciences
• Department of Humanities
• Department of Linguistics and Philosophy
• Department of Materials Science and Engineering
• Department of Mathematics
• Department of Mechanical Engineering
• Department of Nuclear Science and Engineering
• Department of Ocean Engineering
• Department of Physics
• Department of Political Science
• Department of Urban Studies and Planning
• Engineering Systems Division
• Harvard-MIT Program of Health Sciences and Technology
• Institute for Data, Systems, and Society
• Media Arts & Sciences
• Operations Research Center
• Program in Real Estate Development
• Program in Writing and Humanistic Studies
• Science, Technology & Society
• Science Writing
• Sloan School of Management
• Supply Chain Management
• System Design & Management
• Technology and Policy Program

Collections in this community

Doctoral theses, graduate theses, undergraduate theses, recent submissions.

The pulse amplifier in theory and experiment 

Optical studies of the nature of metallic surfaces 

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Department of Computer Science

Thesis projects and research in ds.

The Master's thesis is a mandatory course of the Master's program in Data Science. The thesis is supervised by a professor of the data science faculty list .

Research in Data Science is a core elective for students in Data Science under the supervision of a data science professor.

Research in Data Science

The project is in independent work under the supervision of a member of the faculty in data science

Only students who have passed at least one core course in Data Management and Processing, and one core course in Data Analysis can start with a research project.

Before starting, the project must be registered in mystudies and a project description must be submitted at the start of the project to the studies administration by e-mail (address see Contact in right column).

Master's Thesis

The Master's Thesis requires 6 months of full time study/work, and we strongly discourage you from attending any courses in parallel. We recommend that you acquire all course credits before the start of the Master’s thesis. The topic for the Master’s thesis must be chosen within Data Science.

Before starting a Master’s thesis, it is important to agree with your supervisor on the task and the assessment scheme. Both have to be documented thoroughly. You electronically register the Master’s thesis in mystudies.

It is possible to complete the Master’s thesis in industry provided that a professor involved in the Data Science Master’s program supervises the thesis and your tutor approves it.

Further details on internal regulations of the Master’s thesis can be downloaded from the following website: www.inf.ethz.ch/studies/forms-and-documents.html .

Overview Master's Theses Projects

Chair of programming methodology.

• Prof. Dr. Martin Vechev

Institute for Computing Platform

• Prof. Dr. Gustavo Alonso
• Prof. Dr. Torsten Hoefler
• Prof. Dr. Ana Klimovic
• Prof. Dr. Timothy Roscoe

Institute for Machine Learning

• Prof. Dr. Valentina Boeva
• Prof. Dr. Joachim Buhmann
• Prof. Dr. Ryan Cotterell    
• external page Prof. Dr. Menna El-Assady call_made   
• Prof. Dr. Niao He
• Prof. Dr. Thomas Hofmann
• Prof. Dr. Andreas Krause
• external page Prof. Dr. Fernando Perez Cruz call_made
• Prof. Dr. Gunnar Rätsch
• external page Prof. Dr. Mrinmaya Sachan call_made
• external page Prof. Dr. Bernhard Schölkopf call_made  
• Prof. Dr. Julia Vogt

Institute for Persasive Computing

• Prof. Dr. Otmar Hilliges

Institute of Computer Systems

• Prof. Dr. Markus Püschel

Institute of Information Security

• Prof. Dr. David Basin
• Prof. Dr. Srdjan Capkun
• external page Prof. Dr. Florian Tramèr call_made

Institute of Theoretical Computer Science

• Prof. Dr. Bernd Gärtner

Institute of Visual Computing

• Prof. Dr. Markus Gross
• Prof. Dr. Marc Pollefeys
• Prof. Dr. Olga Sorkine
• Prof. Dr. Siyu Tang

Disney Research Zurich

• external page Prof. Dr. Robert Sumner call_made

Automatic Control Laboratory

• Prof. Dr. Florian Dörfler
• Prof. Dr. John Lygeros

Communication Technology Laboratory

• Prof. Dr. Helmut Bölcskei

Computer Engineering and Networks Laboratory

• Prof. Dr. Laurent Vanbever
• Prof. Dr. Roger Wattenhofer

Computer Vision Laboratory

• Prof. Dr. Ender Konukoglu
• Prof. Dr. Luc Van Gool
• Prof. Dr. Fisher Yu

Institute for Biomedical Engineering

• Prof. Dr. Klaas Enno Stephan

Integrated Systems Laboratory

• Prof. Dr. Luca Benini
• Prof. Dr. Christoph Studer

Signal and Information Processing Laboratory (ISI)

• Prof. Dr. Amos Lapidoth
• Prof. Dr. Hans-Andrea Loeliger

D-MATH does not publish Master's Theses projects. In case of interest contact the professor directly.

FIM - Insitute for Mathematical Research

• Prof. Dr. Alessio Figalli

Financial Mathematics

• Prof. Dr. Josef Teichmann

Institute for Operations Research

• Prof. Dr. Robert Weismantel
• Prof. Dr. Rico Zenklusen

RiskLab Switzerland

• external page Prof. Dr. Patrick Cheridito call_made
• external page Prof. Dr. Mario Valentin Wüthrich call_made

Seminar for Applied Mathematics

• Prof. Dr. Rima Alaifari
• Prof. Dr. Siddhartha Mishra

Seminar for Statistics

• Prof. Dr. Afonso Bandeira
• Prof. Dr. Peter Bühlmann
• Prof. Dr. Yuansi Chen
• Prof. Dr. Nicolai Meinshausen
• Prof. Dr. Jonas Peters
• Prof. Dr. Johanna Ziegel

Law, Economics, and Data Science Group

• Prof. Dr. Eliott Ash , D-GESS)

Institute for Geodesy and Photogrammetry

• Prof. Dr. Konrad Schindler (D-BSSE)

PROFESSIONAL MASTER'S PROGRAM

Master of Data Science

Rice University's Master of Data Science program is a professional, non-thesis degree designed to support the needs of interdisciplinary professionals. Taught by world-class faculty, the program offers students online or on-campus options.

Master of Data Science (MDS): Online & On-Campus Programs

Program Overview

The MDS degree will be offered with both an on-campus and an online option. Students must apply to either the online or on-campus program and will be explicitly admitted to one program or the other.

Rice’s Master of Data Science (MDS) program is designed to support the needs of interdisciplinary professionals who want to apply data science knowledge, theory, and techniques to solve real-world problems.

The program offers:

• Multidisciplinary, interdepartmental and intercollegiate instruction
• Customizable, specialized degrees comprised of 31 graduate credit hours
• Same online & in-person degrees

Program Learning Outcomes

Upon completing the MDS degree, students will have proficiency in:

• Understanding the computational and statistical foundations of Data Science
• Knowing and understanding how to use the core methods of Data Science as applied to an area of specialization or across a breadth of areas
• Applying Data Science knowledge, theory, and techniques to solve difficult, real-world problems, beginning with raw data and ending with actionable insights
• Effectively communicating written and orally about Data Science methods and results to a lay audience

Curriculum Overview

This non-thesis curriculum requires the completion of a minimum of 31 credits. It is a rigorous blend of courses that deliver the skills you need to collect, evaluate, interpret and communicate data for effective decision-making across a variety of industries.

• Core Courses: Your curriculum includes core courses designed to help you gain an understanding of the computational and statistical foundations of data science.
• Specialization: You’ll gain deeper knowledge in data science by choosing a specialization in business analytics, machine learning or image processing.* Currently, image processing coursework is only offered for the on-campus program.
• Electives: You’ll further customize your program of study with an elective in ethics, cybersecurity, or security and privacy.
• Capstone: Then, to give you experience applying your knowledge to a real-world problem, you’ll participate in a capstone project that will help you demonstrate your skill, collaborative ability and problem-solving acumen.

View the MDS Curriculum to learn more about core courses, specializations, electives and our data science capstone project.

Online or On-Campus, which is right for you?

The Online MDS is a part-time program that allows working professionals to get the same benefits and curriculum of a full-time, on-campus program in an online environment. Students have access to best-in-class materials and resources and can connect with peers and world-class educators. Learn More.

On-Campus MDS

The On-Campus MDS is a full-time program at the Rice University campus in Houston, Texas. The program hosts a lively and invigorating community of scholars in the Department of Computer Science, the largest academic department at Rice. Learn More .

Engineering Professional Master's Programs

The following professional master's programs also offer non-thesis, advanced degrees involving data science:

• Master of Computational and Applied Mathematics The Professional Master of Computational and Applied Mathematics (MCAM) is designed for students interested in a technical career path in industry or business.
• Master in Computational Science and Engineering The Professional Master in Computational Science and Engineering (MCSE) is offered jointly by the Department of Computational and Applied Mathematics, Computer Science and Statistics in the School of Engineering.
• Master of Computer Science The Professional Master of Computer Science (MCS) degree is a terminal degree for students intending to pursue a technical career in the computer industry.
• Master of Electrical and Computer Engineering The Department of Electrical and Computer Engineering offers a Professional Master of Electrical and Computer Engineering (MECE) program with a focus in Data Science.
• Master of Industrial Engineering The School of Engineering offers a Professional Master of Industrial Engineering for students seeking a deeper understanding of how sophisticated decision models can optimize complex systems in any industry as well as the nonprofit sector.
• Master of Statistics The Department of Statistics offers a Professional Master of Statistics (MStat) program that includes a solid foundation in statistical computing, statistical modeling, experimental design, and mathematical statistics, plus electives in statistical methods and/or theory.
• Professional Science Master's Program The Subsurface-Geoscience Professional Science Master’s program offers a program focus area in Energy Data Management.

Instructions for MSc Thesis

Before the thesis.

Before you start work on your thesis, it is important to put some thought into the choice of topic and familiarize yourself with the criteria and procedure. To do that, follow these steps, in this order:

Step 0: Read the university instructions .

Read the MSc thesis instructions and grading criteria on the university website. Computer Science Master's program: [link] . Data Science Master's program: [ link ].

Step 1: Choose a topic .

Choose a topic among the ones listed on the group's webpage [ link ].

You can also propose your own topic. In this case, you must explain what the main contribution of the thesis will be and identify at least one scientific publication that is related to the topic you propose.

Step 2: Contact us .

Submit the application form [ link ] to let us know of your interest to do your thesis in the group. Note : If you contact us, then please be ready to start work on the thesis within one month .

Step 3: Agree on the topic .

We have a brief discussion about the topic and devise a high-level plan for thesis work and content. We also discuss a start date , when you start work on the thesis. In addition, you should contact a second evaluator for the thesis.

Thesis timeline

Below you find the milestones after you have started work on the thesis. In parenthesis, you find an estimate of when each milestone occurs. The thesis work ends when you submit it for approval. The total duration from start to end of the thesis should be about four months.

Milestone #0: Thesis outline (at most 3 weeks from the start) .

You create a first outline of the thesis. The outline should contain the titles of the chapters, along with a (tentative) list of sections and contents. An indicative template for the outline is shown below on this page.

Milestone #1: A draft with first results (about 2 months from start) .

All chapters should contain some readable content (not necessarily polished). Most importantly, some results should already be described. Ideally, you should be able to complete and refine the results within one more month.

Milestone #2: A draft with all results (about 1 month before the end).

Most content should now be in the draft. Some polishing remains and some results may still be refined. Notify the second evaluator that you are near the end of the thesis work. Optionally, you may send the thesis draft and receive preliminary comments from the second evaluator.

Milestone #3: Submit the thesis for approval (end of thesis work).

You will receive a grade and comments after the next program board's meeting.

Supervision

What you can expect from the supervisor:

• Comments for the thesis draft after each milestone (see timeline above) and, if necessary, a meeting.
• Suggestions for how to proceed in cases when you encounter a major hurdle.

In addition, you are welcome to participate in the group meetings and discuss your thesis work with other group members.

Note however that one of the grading criteria for the thesis is whether you worked independently -- and in the end, the thesis should be your own work.

Template for Thesis Outline

Below you find a suggested template for the outline of the thesis. You may adapt it to your work, of course (e.g., change chapter titles or structure).

A summary of the thesis that mentions the broader topic of the thesis and why it is important; the research question or technical problem addressed by the thesis; the main thesis contributions (e.g., data gathering, developed methods and algorithms, experimental evaluation) and results.

Chapter 1: Introduction

The introduction should motivate the thesis and give a longer summary. It should be written in a way that allows anyone in your program to understand it, even if they are not experts in the topic.

• What is the broader topic of the thesis?
• Why is it important?
• What research question(s) or technical problems does the thesis address?
• What are the most related works from the literature on the topic? How does the thesis differ from what has already been done?
• What are the main thesis contributions (e.g., data gathering, developed methods and algorithms, experimental evaluation)?
• What are the results?

Chapter 2: Related literature

Organize this chapter in sections, with one section for each research area that is related to your thesis. For each research area, cite all the publications that are related to your topic, and describe at least the most important of them.

Chapter 3: Preliminaries

In this chapter, place the information that is necessary for you to describe the contributions and results of the thesis. It may be different from thesis to thesis, but could include sections about:

Setting. Define the terms and notation you will be using. State any assumptions you make across the thesis. Background on Methods . Describe existing methods from the literature (e.g., algorithms or ML models) that you use for your work. Data (esp. for a Data Science thesis). If the main contribution is data analysis, then describe the data here, before the analysis.

Chapter 4: Methodological contribution

For a Computer Science thesis, this part typically describes the algorithm(s) developed for the thesis. For a Data Science thesis, this part typically describes the method for the analysis.

Chapter 5: Results

This chapter describes the results obtained when the methods of Chapter 4 are used on data.

For a Computer Science thesis, this part typically describes the performance of the developed algorithm(s) on various synthetic and real datasets. For a Data Science thesis, this part typically describes the findings of the analysis.

The chapter should also describe what insights are obtained from the results.

Chapter 6: Conclusion

• Summarize the contribution of the thesis.
• Provide an evaluation: are the results conclusive, are there limitations in the contribution?
• How would you extend the thesis, what can be done next on the same topic?

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Bachelor and Master Thesis

We offer a variety of cutting-edge and exciting research topics for Bachelor's and Master's theses. We cover a wide range of topics from Data Science, Natural Language Processing, Argument Mining, the Use of AI in Business, Ethics in AI and Multimodal AI. We are always open to suggestions for your own topics, so please feel free to contact us. We supervise students from all disciplines of business administration, business informatics, computer science and industrial engineering.

Thesis Topics

Example topics could be:

• Conversational Artificial Intelligence in Insurance and Finance
• Natural Language Processing for Understanding Financial Narratives: An Overview
• Ethics at the Intersection of Finance and AI: A Comprehensive Literature Review
• Explainable Natural Language Processing for Credit Risk Assessment Models: A Literature Review

Thesis Template

• Latex Template for bachelor and master theses
• How to use the latex template

Q1: How many pages do I need to write?

A: In general, the number of pages is only a poor indicator of the quality of a thesis. However, as a rule of thumb, bachelor theses should have around 30 pages, while master theses should be around 60 pages of main content (that is, without the appendix and lists of tables, symbols, figures, references etc.).

Q2: How often should I meet with my supervisor?

A: Your supervisors are typically very busy people. However, don't hesitate to ask in case you have questions. For instance, if you are unsure of some requirements, or in case you have methodological problems, it is absolutely necessary to talk to your supervisor. As a rule of thumb, you should meet at least three times (once in the beginning, once in the middle, and once before the submission).

Q3: Am I allowed to use any AI models in the process of writing my thesis?

A: In general, we neither forbid nor recommend the use of AI for writing support. However, if you use AI, please inform your supervisor. Also, you need to adhere to the recommendations on the use of AI writing assistants given by the faculty.

Q4: How much time do I have?

A: The exact timing is dependent on your study program! Thus, please check the examination requirements before the official start of your thesis -- you are responsible for sticking to the rules.

Department of Data Science

Master Thesis

Master theses.

Below you find our current topic proposals as pdf-files.

If you are interested in a certain topic, please send an e-mail to wima-abschlussarbeiten[at]lists.fau.de. Please refrain from writing emails to other addresses.

Your e-mail should include

• your transcript of records
• a letter of motivation (approximately half a page)
• desired date at which you want to start
• latest possible date of submission.

In your letter of motivation please state which of the topic proposals you are interested in. If none of these proposals interest you please state which type of thesis you desire (e.g. literature study) and which field you are interested in.

Topic proposals (with corresponding advisers)

• Optimization of Optical Particle Properties under Uncertainty (Frauke Liers)
• Analysis and Prediction of Asynchronous Event Sequences Considering Uncertainty @ Medical Technology (Frauke Liers, thesis together with Siemens Healthineers, Erlangen)
• Optimized Qubit Routing for Commuting Gates (Frauke Liers)

Furthermore, students are welcome to contact abschlussarbeiten[at]lists.fau.de to jointly define a thesis topic in one of the following areas:

• Optimization under uncertainty (Frauke Liers)
• Integration of data analysis with optimization (Frauke Liers)

Previous Theses

• Adviser: Alexander Martin
• Adviser: Kevin-Martin Aigner, Fauke Liers
• Adviser: Jan Rolfes, Timm Oertel
• Adviser: Jan Rolfes, Frauke Liers
• Adviser: Jan Rolfes, Jana Dienstbier, Frauke Liers
• Adviser: Martina Kuchlbauer, Frauke Liers
• Adviser: Martina Kuchlbauer, Jana Dienstbier, Frauke Liers
• Adviser: Yiannis Giannakopoulos
• Adviser: Andreas Bärmann, Alexander Martin
• Adviser: Jan Krause, Andreas Bärmann, Alexander Martin
• Adviser: Christian Biefel, Frauke Liers
• Adviser: Jonasz Staszek, Alexander Martin
• Adviser: Lukas Hümbs, Alexander Martin
• Adviser:Kristin Braun, Frauke Liers
• Adviser: Lukas Glomb, Florian Rösel, Frauke Liers
• Adviser: Bismark Singh, Alexander Martin
• Adviser:Kristin Braun, Johannes Thürauf, Robert Burlacu,Frauke Liers
• Adviser: Oskar Schneider, Alexander Martin
• Optimization of energy supply in critical infrastructures using battery electric vehicles Adviser: Bismark Singh, Alexander Martin
• Approximations to the Clustered Traveling Salesman Problem with an Application in Perm, Russia Adviser: Bismark Singh, Alexander Martin
• Decomposition methods for energy optimization models Adviser: Bismark Singh, Alexander Martin
• Aircraft Trajectory Optimization and Disjoint Paths Adviser: Benno Hoch, Frauke Liers
• Obere und untere Schranken für das Set-Cover mittels Lasserre Hierachie Adviser: Jan Rolfes, Alexander Martin
• Separation Algorithms and Reformulations for Single-Item Lot-Sizing with Non-Delivery Penalties Adviser: Dieter Weninger
• Optimales Scheduling an Maschinen Adviser: Kevin-Martin Aigner, Jan Rolfes, Alexander Martin
• Optimization of scenario-expanded tail assignment problems including maintenance Adviser: Lukas Glomb, Florian Rösel, Frauke Liers
• Anti-Lifting: Sparsifizierung bei gemischt-ganzzahligen Optimierungsproblemen Adviser: Katrin Halbig, Alexander Martin
• Solving Mixed-Integer Problems using Machine Learning for the Optimization of Energy Production Adviser: Christian Biefel, Lukas Hümbs, Alexander Martin
• Preprocessing Techniques for Mixed-Integer Bilevel Problems Adviser: Thomas Kleinert, Dieter Weninger, Alexander Martin
• Projection and Farkas Type Lemmas for Mixed Integer Programs Adviser: Richard Krug, Alexander Martin
• Gamma-robuste lineare Komplementaritätssysteme Adviser: Vanessa Krebs, Martin Schmidt
• Verseiloptimierung (Kooperation mit LEONI) Adviser: Alexander Martin
• Data-based Methods for Chance Constraints in DC Optimal Power Flow with Extension to Curtailment Adviser: Kevin-Martin Aigner, Frauke Liers
• Different Concepts of Distributionally Robust Vehicle Routing Problems Adviser: Sebastian Tschuppik, Dennis Adelhütte, Frauke Liers
• Optimierungsmethoden für Logistikprozesse im Krankenhaus Adviser: Andreas Bärmann, Dieter Weninger, Alexander Martin
• Lagrange Relaxierung Energienetze Kooperation Jülich Adviser: Johannes Thürauf, Lars Schwee
• The price of robustness in the European entry-exit market Adviser: Thomas Kleinert, Frauke Liers
• Kosteneffizienter Betrieb von Smart Grids mit Gomory Schnittebenen Adviser: Martin Schmidt, Galina Orlinskaya
• On finding sparse descriptions of polyhedra with mixed-integer programming Adviser: Alexander Martin, Patrick Gemander, Oskar Schneider
• Mathematische Optimierung für chromatographische Verfahren zur Trennung von Stoffgemischen Adviser: Frauke Liers, Robert Burlacu
• Machine Learning gestützte Prognose der Performance zukünftiger Lieferungen unter Verwendung von adaptiven Algorithmen und geeigneten Datenstrukturen im Transportmanagement Adviser: Frauke Liers, Andreas Bärmann
• Discrete optimization for optimal train control Adviser: Alexander Martin, Andreas Bärmann
• Optimierte Flottenplanung in der Luftfahrt unter Berücksichtigung der Betankungsstrategie Adviser: Alexander Martin, Andreas Bärmann
• Lipschitzoptimierung am Beispiel des europäischen Gasmarktes Adviser: Martin Schmidt, Thomas Kleinert
• Graphzerlegungen und Alternating Direction Methode für Gasnetzwerke Adviser: Martin Schmidt
• Multikriterielle Optimierung für Graphendekompositionen in der Gasnetzoptimierung Adviser: Martin Schmidt
• Robuste Gleichgewichtsprobleme im Energiebereich Adviser: Martin Schmidt, Vanessa Krebs
• MIP Methoden in der Fördertechnik Adviser: Alexander Martin, Andreas Bärmann, Patrick Gemander
• Verwendung von SVMs für medizinische Diagnostik Adviser: Frauke Liers, Dieter Weninger
• Ausbauplanung für städtische Verkehrsnetze Adviser: Alexander Martin, Andreas Bärmann
• Zuschnittoptimierung und Parametervariation in der Flachglasindustrie Adviser: Lars Schewe
• Ermittlung optimaler Höchstabfluggewichte unter Unsicherheit Adviser: Alexander Martin, Lena Hupp, Martin Weibelzahl
• Online-Optimierung in Hinblick auf Prognoseunsicherheiten bei erneuerbaren Energien mittels basisorientierter Szenarienreduktion Adviser: Alexander Martin, Christoph Thurner
• A variable decomposition algorithm for production planning Adviser: Alexander Martin, Dieter Weninger
• Mixed integer moving horizon control for flexible energy storage systems Adviser: Martin Schmidt
• Mathematische Analyse von Kompaktheitsmaßen in der Gebietsplanung anhand eines Modells zur Dienstleisterauswahl bei Transportausschreibungen Adviser: Alexander Martin
• Methoden zur Laufzeitverbesserung eines Mixed-Integer Program in der Entsorgungslogistik Adviser: Alexander Martin
• Aktuelle Erkenntnisse bei Pivotregeln des Simplexverfahrens Adviser: Frauke Liers
• Radius of robust feasibility for the robust stochastic nomination validition problem in passive gas networks Adviser: Frauke Liers, Denis Aßmann
• Mathematische Modelle und Optimierung für die automatische Permutation von Schließanlagen Adviser: Alexander Martin
• Mathematische Modellierung von Stromnetzen: Ein Vergleich AC- und DC-Modell hinsichtlich Investitionsentscheidungen Adviser: Frauke Liers
• Diskrete Optimierung im Immobilien-Investing Adviser: Alexander Martin
• Polyedrische und komplexitätstheoretische Untersuchungen von bipartiten Matchingproblemen mit quadratischen Termen Adviser: Frauke Liers
• Discrete Selection of Diameters for Constructing Optimal Hydrogen Pipeline Networks Adviser: Lars Schewe
• Optimierte Tourenplanung im Krankentransport unter Berücksichtigung von Zeitfenstern Adviser: Frauke Liers
• Optimale Preiszonen und Investitionsentscheidungen unter Berücksichtigung von Stromspeichern – Eine modelltheoretische Analyse des Strommarkts Adviser: Alexander Martin
• Robuste Eigenanteilplanung und Belegungsplanung sowie Personalplanung für ein Pflegeheim Adviser: Frauke Liers
• Dynamische automatisierte Rampensteuerung Adviser: Alexander Martin
• A combinatorial splitting algorithm for checking feasibility of passive gas networks under uncertain injection patterns Adviser: Frauke Liers, Denis Aßmann
• Integrated Optimization Problems in the Airline Industry Adviser: Alexander Martin
• Mathematische Modellierung eines Produktionshochlaufs bei Kromberg & Schubert Adviser: Frauke Liers
• On the uniqueness of competitive market equilibria on DC networks Adviser: Martin Schmidt
• The Clique-Problem under Multiple-Choice Constraints with Cycle-Free Dependency Graphs Adviser: Alexander Martin, Andreas Bärmann
• A Decomposition Approach for a Multilevel Graph Partitioning Model of the German Electricity Market Adviser: Martin Schmidt
• Zyklisches Scheduling in der Kirche – Mathematische Modellierung und Optimierung Adviser: Alexander Martin, Thorsten Ramsauer
• Optimierung von Flugbahnen: Ein gemischt-ganzzahliges Modell zur Berechnung von optimalen Trajektorien-Netzwerken Adviser: Frauke Liers
• Robuste Optimierungsmethoden für Nominierungsvalidierung in Gasnetzwerken bei Nachfrageunsicherheiten Adviser: Frauke Liers, Denis Aßmann
• Stable Set Problem with Multiple Choice Constraints on Staircase Graphs Adviser: Alexander Martin
• Anwendung von robusten Flussproblemen für die optimale Speichersteuerung im Smart Grid Adviser: Frauke Liers
• Das Sternsingerproblem: Planung, Modellierung und mathematische Optimierung Adviser: Alexander Martin, Martin Weibelzahl
• A Bilevel Optimization Model for Holy Mass Planning Adviser: Alexander Martin, Martin Weibelzahl
• Optimal Personnel Management in Church: A Robust Optimization Approach for Operative and Strategic Planning Adviser: Frauke Liers, Martin Weibelzahl
• Active-Passive-Vehicle-Routing-Problem Adviser: Alexander Martin, Michael Drexl (Fraunhofer SCS)
• Robuste Optimierung in der Flugplanung: Entwicklung eines statischen sowie eines zeitexpandierten Modells zur robusten Zeitfenster-Zuordnung in der prätaktischen Phase Adviser: Frauke Liers
• Personaleinsatzplanung im Einzelhandel unter Berücksichtigung von Unsicherheiten mithilfe mathematischer Optimierung Adviser: Alexander Martin, Falk Meyerholz (Fraunhofer ILS)
• Clustering von Bahnweichen und Analyse von Störungen zur Optimierung der Instandhaltungsmaßnahmen Adviser: Frauke Liers, Thomas Böhm (DLR)
• Mikroökonomische Haushaltstheorie unter Unsicherheit: mathematische Perspektive Adviser: Alexander Martin, Martin Weibelzahl
• Lösung von realen Probleminstanzen bei der Tourenplanung in der ambulanten Pflege mit Hilfe eines Cluster-First-Route-Second-Ansatzes Adviser: Alexander Martin
• Revenue Management als Netzwerkproblem unter Unsicherheiten mit Anwendung im Fernbusmarkt Adviser: Alexander Martin, Lars Schewe
• Das Partial Digest Problem mit absoluten Fehlern als Matching und Anwendung der Lagrange-Relaxierung Adviser: Frauke Liers
• Optimierung im Produktionsablauf bei Elektrolux Rothenburg – Laserline (schriftliche Hausarbeit im Rahmen der Ersten Staatsprüfung für das Lehramt an Gymnasien in Bayern) Adviser: Alexander Martin
• Memetische Optimierung des Generalized Travelling Salesman Problems Adviser: Alexander Martin
• Estimation the optimal schedule of a Vehicle Routing Problem arising in Bulk Distribution Network Optimisation Adviser: Alexander Martin
• Nominierungsvalidierung bei Gasnetzen: Einfluss und mögliche Behandlung von Unsicherheiten Adviser: Frauke Liers
• Ein exaktes Lösungsverfahren für das Optimierungsproblem des Partial Digest mit absoluten Fehlern Adviser: Frauke Liers
• Mathematische Optimierung des Bidmanagements in der Reisebranche Adviser: Alexander Martin, Lars Schewe
• Optimierung der Staffeleinteilung in der Fußball-Landesliga Bayern und Konzipierung vereinsfreundlicher Spielpläne Adviser: Alexander Martin, Andreas Heidt
• Potentialanalyse für die Transportlogistik im Krankenhauswesen Adviser: Alexander Martin, Andrea Peter
• Anpassungstests mit Nuisanceparmetern für das lineare Regressionsmodell Adviser: Alexander Martin
• Robuste Optimierung für Scheduling Probleme im Luftverkehrsmanagement Adviser: Alexander Martin, Andreas Heidt
• Reisewegbasierte Flottenoptimierung bei differenzierter Passagiernachfrage Adviser: Alexander Martin
• Gemischt  bivariate Verteilungen unter Verwendung einer doppelt-stochastischen Summe und ihre Anwendunge n Adviser: Alexander Martin, Ingo Klein
• Optimierung einer Indoor-Navigation am Flughafen München mittels adaptivem, heuristischem Dijkstra-Verfahren basierend auf partieller Gridgraphenstruktur Adviser: Alexander Martin, Andreas Bärmann
• A Comparison of cutting-plane closures in R² and R³ Adviser: Alexander Martin, Sebastian Pokutta
• Tourenplanung bei der Abokiste Adviser: Alexander Martin, Andreas Bärmann
• Klassifizierung und Strukturanalyse von Produktionsplanungsmodellen, die durch gemischt-ganzzahlig-lineare Programme modelliert sind. Adviser: Alexander Martin, Dieter Weninger
• Performance-oriented Optimization Techniques for Facility Design Floorplanning Problems Adviser: Alexander Martin, Stefan Schmieder
• Standortoptimierung als Entscheidungshilfe für Familien bei der Wahl eines Wohnortes unter Berücksichtigung der Infrastruktur Adviser: Alexander Martin
• Kapazitätsbestimmung in linearen Netzwerken Adviser: Alexander Martin, Lars Schewe
• Portfoliooptimierung – Der Ansatz von Markowitz unter realen Nebenbedingungen Adviser: Alexander Martin
• Entwicklung eines Steuerungstools for Cross-Docking Prozesse bei der BMW Group Adviser: Alexander Martin
• Modellierung und Lösung eines mehrperiodischen deterministischen Standortplanungsproblems unter volantilen Bedarfsmengen Adviser: Alexander Martin
• Analytische Optimierung von Netzschutzkennlinien Adviser: Alexander Martin, Lars Schewe
• Hedging von Katastrophenrisiken durch den Einsatz von Industry Loss Warranties Adviser: Alexander Martin, Nadine Gatzert
• Anwendung mathematischer Werkzeuge zur Umlaufoptimierung am praktischen Beispiel Adviser: Alexander Martin
• Optimization Methods for Asset Liability Management in a Non-Life Insurance Company Adviser: Alexander Martin, Nadine Gatzert
• Parametrisierung von 3D-Blattmodellen zur Detektion und Ergänzung unvollständiger Messdaten Adviser: Alexander Martin, Günther Greiner
• Conducting Optimal Risk Classification for Substandard Annities in the Presence of Underwriting Risk Adviser: Alexander Martin
• Incorporating Convexe Hull into an Algorithmic Approach for Territory Design Problems Adviser: Alexander Martin, Sonja Friedrich
• Expanding Brand & Bound for binary integer programs with a pseudo-boolean solver and a SAT based Presolver Adviser: Alexander Martin
• “Effiziente randomisierte Algorithmen für das Erfüllbarkeitsproblem” – Beschleunigung durch Variableninvertierung Adviser: Alexander Martin
• Preprocessingansätze für die Planung von gekoppelten Strom-, Gas- und Wärmenetzen  Adviser: Alexander Martin, Andrea Zelmer, Debora Mahlke
• Die kontinuierliche Analyse der kooperativen Effizienz in Gesundheit mit Hilfe parametrischer iund nicht-parametrischer Verfahren Adviser: Alexander Martin, Freimut Bodendorf
• Incorporating Convex hulls into an Algorithmic Approach for Territory Design Problems Adviser: Alexander Martin
• Auffalten von orthogonalen Bäumen Adviser: Alexander Martin, Ute Günther
• Heuristic Approaches for the Gate Assignment Problem Adviser: Alexander Martin, Andrea Peter
• Synchronisation von Tauschpunkten für Flugbesatzungen und technische Anforderungen in der Rotationsplanung von  Verkehrsflugzeugen Adviser: Alexander Martin, Andrea Peter in co-operation with Lufthansa Passage
• Verteilte vs. ganzheitliche Optimierung in der Luftfahrt Adviser: Alexander Martin, Sebastian Pokutta, Andrea Peter in co-operation with DLR Braunschweig
• Optimale Schichtenerstellung zur Personalbedarfsermittlung Adviser: Alexander Martin, Henning Homfeld in co-operation with DB Schenker Rail
• Polyedrische Untersuchungen von Multiple Knapsack Ungleichungen Adviser: Alexander Martin, Henning Homfeld
• Using vehicle routing heuristics to estimate costs in gas cylinder delivery Adviser: Alexander Martin in co-operation with Linde Gas
• An Overview on Algorithms for Graph Reliability and possible Transfor for Dynamic Graph Reliability Adviser: Alexander Martin,  Sebastian Pokutta, Nicole  Nowak
• Eine Verallgemeinerung des Quadratic Bottleneck Assignment Problem und Anwendung Adviser: Alexander Martin, Lars Schewe, Sonja Friedrich
• Vergleich von Optimierungsmodellen beim Erdgashandel Adviser: Alexander Martin, Johannes Müller
• Branch and Cut Verfahren in der Standortplanung Adviser: Wolfgang Domschke, Alexander Martin
• Evolution of the Performance of Separate Scheduling Solvers Forced to Cooperate Adviser: Alexander Martin, Andrea Peter
• Heuristische Ansätze für den Umgang mit Fertigungsrestriktionen in der Herstellung von Blechprofilen Adviser: Alexander Martin, Ute Günther
• An overview of algorithms for graph reliability and possible transfer dynamic graph reliability Advisor: Alexander Martin, Nicole Ziems
• Analyzing and modeling of selected parameters of the facade construction of a building with respect to the sustainability and efficiency of the building Adviser: Alexander Martin
• Preprocessingtechniken in der Gasnetzwerkoptimierung Adviser: Alexander Martin, Björn Geißler
• Optimierungsmethoden für die Kopplung von Day-Ahead-Strommärkten Adviser: Alexander Martin, Antonio Morsi, Björn Geißler
• Ein pfadbasiertes Modell für das Routing von Güterwagen im Einzelwagenverkehr Adviser: Alexander Martin, Henning Homfeld
• Azyklische Fahrzeugeinsatz- und Instandhaltungsoptimierung im Schienenpersonennahverkehr Adviser: Alexander Martin, Henning Homfeld
• Ein Arboreszenzmodell für das Leitwegproblem Adviser: Alexander Martin, Henning Homfeld
• Approximation einer Hyperbel in der diskreten Optimierung Adviser: Alexander Martin, Henning Homfeld
• Dynamische Programmierung in der Gasnetzwerkoptimierung Adviser: Alexander Martin, Susanne Moritz, Björn Geißler, Antonio Morsi
• Lösungsmethoden für das Pin Assignment Problem Adviser: Alexander Martin, Antonio Morsi, Björn Geißler
• Exploiting Heuristics for the Vehicle Routing Problem to estimate Gas Delivery Costs Adviser: Alexander Martin
• Ganzzahlige Optimierung zur Bestimmung konsistenter Eröffnungspreise von Futures-Kontrakten und ihrer Kombinationen Advisor: Alexander Martin
• Verfahren zur Lösung des soft rectangle packing problem Adviser: Alexander Martin, Armin Fügenschuh
• Optimierung der Leitwegeplanung im Schienengüterverkehr Adviser: Alexander Martin, Armin Fügenschuh, Henning Homfeld
• Optimierungsmethoden zur Berechnung von Cross-Border-Flow beim Market-Coupling im europäischen Stromhandel Adviser: Alexander Martin, Antonio Morsi, Björn Geißler
• Gemischt-ganzzahliges Modell zur Entwicklung optimaler Erneuerungsstrategien für Wasserversorgungsnetze Adviser: Alexander Martin, Antonio Morsi
• Algorithmische Behandlung des All-Different Constraints im Branch&Cut Adviser: Alexander Martin, Thorsten Gellermann
• Empiric Analysis of Convex Underestimators in Mixed Integer Nonlinear Optimization Adviser: Alexander Martin, Thorsten Gellermann
• Partial Reverse Search Adviser: Alexander Martin, Lars Schewe
• Zufallsbasierte Heuristik für gekoppelte Netzwerke in der dezentralen Energieversorgung Adviser: Alexander Martin, Debora Mahlke, Andrea Zelmer
• Polyedrische Untersuchungen an einem stochastischen Optimierungsproblem aus der regenerativen Energieversorgung Adviser: Alexander Martin, Debora Mahlke, Andrea Zelmer
• Relax & Fix Heuristik für ein stochastisches Problem aus der regenerativen Energieversorgung Adviser: Alexander Martin, Debora Mahlke, Andrea Zelmer
• Test Sets for Spanning Tree Problems with Side Constraints Adviser: Alexander Martin, Ute Günther
• Polyedrische Untersuchungen zur Kostenoptimierung der Geldautomatenbefüllung Adviser: Alexander Martin, Ute Günther
• Effiziente stückweise lineare Approximation bivariater Funktionen Adviser: Ulrich Reif, Armin Fügenschuh, Andrea Peter
• The 3-Steiner Ratio in Octilinear Geometry Adviser: Karsten Weihe, Alexander Martin
• An empirical investigation of local search algorithms to minimize the weighted number of tardy jobs in Single Machine Scheduling Adviser: T. Stützle, Alexander Martin
• Optimization of Collateralization concerning Large Exposures Adviser: S. Dewal, Alexander Martin
• Optimierungsmodelle zur Linienbündelung im ÖPNV Adviser: Alexander Martin, Armin Fügenschuh
• Solving dynamic Scheduling Problems with Unary Resources Adviser: Alexander Martin
• Parameteranalyse in der Optimierungssoftware Carmen-PAC Adviser: Armin Fügenschuh, Alexander Martin
• Ein Data Mining Ansatz zur Abschätzung von zyklischen Werkstoffkennwerten Adviser: Armin Fügenschuh
• Automatische Parameteroptimierung im Crew Assignment System Carmen Adviser: Armin Fügenschuh, Alexander Martin
• Branch and Price-Verfahren für Losgrößenprobleme Adviser: Wolfgang Domschke, Alexander Martin
• Pin Assignment im Multilayer Chip Design Adviser: Alexander Martin, Karsten Weihe
• Augmentierende Vektoren mit beschränktem Support Adviser: Alexander Martin, Karsten Weihe
• An LP-based Rounding Approach to Coupled Supply Network Planning Adviser: Alexander Martin, Debora Mahlke, Andrea Zelmer
• Bounded Diameter Minimum Spanning Tree Adviser: Alexander Martin, Ute Günther
• Degree and Diameter Bounded Minimum Spanning Trees Adviser: Alexander Martin, Ute Günther
• Ein MILP, ein MINLP und ein graphentheoretischer Ansatz für die Free-Flight Optimierung Adviser: Alexander Martin, Armin Fügenschuh
• Vehicle Routing for Mobile Nurses  Adviser: Alexander Martin, Armin Fügenschuh
• Linearization Methods for the Optimization of Screening Processes in the Recovered Paper Production Adviser: Mirjam Duer, Armin Fügenschuh
• Solving Real-World Vehicle Routing Problems using MILP and PGreedy Heuristics Adviser: Alexander Martin, Armin Fügenschuh
• Supporting Geo-based Routing in Pub/Sub Middleware Adviser: A. Buchmann, Alexander Martin
• Towards Adaptive Optimization of Advice Dispatch Adviser: Alexander Martin, M. Mezini
• Optimierung von Lokumläufen in Schienengüterverkehr Adviser: Alexander Martin, Armin Fügenschuh
• An Approximation Algorithm for Edge-Coloring of Multigraphs Adviser: Alexander Martin, Daniel Junglas
• Modelling nonlinear stock holding costs in a facility location problem airsing in supply network optimisation  Adviser: Alexander Martin, Björn Samuelsson
• Selected General Purpose Heuristics for Solving Mixed Integer Programs  Adviser: Alexander Martin, Marzena Fügenschuh
• Ein Genetischer Algorithmus für das Proteinfaltungsproblem im HP-Modell Adviser: Alexander Martin
• Algorithmic Approaches for Two Fundamental Optimization Problems: Workload-Balancing And Planar Steiner Trees Adviser: Alexander Martin, Matthias Müller-Hannemann
• Stundenplan-Optimierung: Modelle und Software Adviser: Alexander Martin, Armin Fügenschuh
• Vehicle Routing: Modelle und Software Adviser: Alexander Martin, Armin Fügenschuh
• Parametrized GRASP Heuristics for Combinatorial Optimization Problems  Adviser: Alexander Martin, Armin Fügenschuh
• Optimal Unrolling of Integral Branched Sheet Metal Components Adviser: Alexander Martin, Daniel Junglas
• Leerwagenoptimierung im Schienengüterverkehr Adviser: Alexander Martin, Armin Fügenschuh, Gerald Pfau, DB AG
• Mathematische Modelle und Methoden in der Entscheidungsfindung im Supply Chain Management  Adviser: Alexander Martin, Simone Göttlich
• Modifikation des Approximationsalgorithmus von Hart und Istrail für das Proteinfaltungsproblem im HP-Modell Adviser: Alexander Martin, Agnes Dittel
• Ein Verbesserungsalgorithmus der Proteinfaltung mit dem HP-Modell von Ken Dill Adviser: Alexander Martin, Agnes Dittel
• Entwurf und Evaluation von MILP-Modellierungen zur Optimierung einer synchronisierten Abfüll- und Verpackungsstufe in der Produktionsfeinplanung  Adviser: Alexander Martin, Heinrich Braun, SAP AG, Thomas Kasper, SAP AG
• Integration von Strafkosten für zu niedrige Sicherheitsbestände bei Losgrößenmodellen  Adviser: Hartmut Stadtler, Institut für Betriebswirtschaftslehre, Christian Seipl, Institut für Betriebswirtschaftslehre, Alexander Martin
• Vergleich von Algorithmen zur Lösung ganzzahliger linearer Ungleichungssysteme mit höchstens zwei Variablen pro Ungleichung  Adviser: Alexander Martin, Armin Fügenschuh
• Schaltbedingungen bei der Optimierung von Gasnetzen: Polyedrische Untersuchungen und Schnittebenen  Adviser: Alexander Martin, Susanne Moritz
• Der Simulated Annealing Algorithmus zur transienten Optimierung von Gasnetzen  Adviser: Alexander Martin, Susanne Moritz
• Kantenfärbung in Multigraphen Adviser: Alexander Martin, Daniel Junglas
• Didaktische Aspekte einer Einbeziehung von Geschichte in den Mathematikunterricht am Beispiel von Kartographie Adviser: Alexander Martin
• Vehicle Routing Adviser: Alexander Martin, Armin Fügenschuh
• Ein genetischer Algorithmus zur Lösung eines multiplen Traveling Salesman Problems mit gekoppelten Zeitfenstern Adviser: Alexander Martin, Armin Fügenschuh
• Integrierte Optimierung von Schulanfangszeiten und des Nahverkehrsangebots – ein Constraint-Programming Ansatz im Vergleich zu Ganzzahliger Optimierung Adviser: Alexander Martin, Armin Fügenschuh
• Heuristic methods for site selection, installation selection and mobile assignment in UMTS  Adviser: Alexander Martin, Armin Fügenschuh
• Optimization of the School Bus Traffic in Rural Areas – Modeling and Solving a Distance Constrained, Capacitated Vehicle Routing Problem with Pickup and Delivery, Flexible Time Windows and Several Time Constraints  Adviser: Alexander Martin, Armin Fügenschuh
• Augmentierungsverfahren für Standortplanungsprobleme  Adviser: Alexander Martin, Armin Fügenschuh
• Chain-3 Constraints for an IP Model of Ken A. Dill’s HP Lattice model  Adviser: Alexander Martin, Armin Fügenschuh
• Stundenplangenerierung an einer Grundschule Adviser: Alexander Martin, Armin Fügenschuh, Agnes Dittel
• Vergleichende Untersuchung von Heuristiken für das Routing- und Wellenlängenzuordnungsproblem bei rein transparenten oprischen Telekommunikationsnetzen.  Adviser: Alexander Martin, Manfred Körkel
• Short Chain Constraints for an IP Model of Ken A. Dill’s HP-Lattice Model Adviser: Alexander Martin, Armin Fügenschuh
• Eine Rundeheuristik für Ganzzahlige Programme Adviser: Alexander Martin, Armin Fügenschuh
• Anwendung von Neuronalen netzen zur Beschleunigung von Branchen & Bound – Verfahren der Kombinatorischen Optimierung Adviser: M. Grötschel, Alexander Martin, K. Obermayer

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Data Science, Master of Science

New international master's program data science in wise23/24..

Data Science is an interdisciplinary field that uses statistical and computational methods to extract knowledge and insights from large and complex datasets. It combines elements of computer science, mathematics, and domain-specific knowledge to develop data-driven models that are used to develop applications such as speech recognition, object recognition, and automation systems in healthcare or the automotive industry.

The international master's program in Data Science at TUHH builds on the bachelor's program in Data Science and offers a scientifically oriented study program that teaches theory and methods that are required to create the digital applications of tomorrow.  

How is the program structured?

The program is organized as a two-year program (four semesters) and begins each year in October. It consists of two and a half semesters of lectures and lab courses and one and a half semesters devoted to working in a research team (project work) and writing the master's thesis. The curriculum offers a lot of freedom to set your own focus in the field of data science. The academic degree of Master of Science is awarded. The language of study is English.

The curriculum of the master's program in data science is structured as follows:

• Core qualification: Modules teaching the foundations of data science.
• Specializations: Three different specialization areas (mathematics, computer science, and applications), which allow to focus on different aspects of data science. It is possible to weight the three specialization areas differently but at least one module from each area needs to be taken.
• General qualification: Modules on non-technical topics, business & management, research methods, and a seminar.

Project work and master’s thesis: Can be done at different institutes of the TUHH and allow to work on a research-oriented project.

What are the job prospects?

The master's program in data science optimally prepares graduates for a career in research and development in an academic or industrial environment.

A data scientist:

... typically works in an environment where large amounts of data are generated and ... is responsible for their analysis, algorithmic processing and feature extraction . ... acquires knowledge in an application area and may work in an interdisciplinary team with application experts. ... works in a research-oriented manner and is thus always up to date with the latest developments in this rapidly evolving field.

Due to the high amount of computer science in the degree program, graduates are familiar with all the rules of software design, so that the career opportunities of classical computer scientists are also open to them. The degree program qualifies students for doctoral studies at a university.  

What are the requirements?

Please read the admission requirements as well as the specific requirements carefully before application.

How do I apply?

Please note the different application deadlines:

International applicants from outside EU:

If you have a non-EU nationality and do not currently have a right of residence in an EU country your application period is December 1st to March 1st. We conduct an online application process. Detailed information on the application and admission procedures can be found here .

Applicants within EU (incl. Germany):

If you have an EU nationality or you have a resident permit for a EU-country your application period starts June 1st to July 15th. We conduct an online application process. Detailed information on the application and admission procedures can be found here .

Further Information for international students

You need more information on expanses and fees, accomodation or language courses?

Please inquire with study(at)tuhh(dot)de regarding the admission requirements and application procedure.

TUHH STUDIS - Studierendenservice / Admission and Registration 21071 Hamburg Germany

Program coordinator:

Prof. Dr.-Ing. Tobias Knopp

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Wir verwenden Google für unsere Suche. Mit Klick auf „Suche aktivieren“ aktivieren Sie das Suchfeld und akzeptieren die Nutzungsbedingungen.

Hinweise zum Einsatz der Google Suche

• Data Analytics and Machine Learning Group
• TUM School of Computation, Information and Technology
• Technical University of Munich

Open Topics

We offer multiple Bachelor/Master theses, Guided Research projects and IDPs in the area of data mining/machine learning. A  non-exhaustive list of open topics is listed below.

If you are interested in a thesis or a guided research project, please send your CV and transcript of records to Prof. Stephan Günnemann via email and we will arrange a meeting to talk about the potential topics.

Graph Neural Networks for Spatial Transcriptomics

Type:  Master's Thesis

Prerequisites:

• Strong machine learning knowledge
• Proficiency with Python and deep learning frameworks (PyTorch, TensorFlow, JAX)
• Knowledge of graph neural networks (e.g., GCN, MPNN)
• Optional: Knowledge of bioinformatics and genomics

Description:

Spatial transcriptomics is a cutting-edge field at the intersection of genomics and spatial analysis, aiming to understand gene expression patterns within the context of tissue architecture. Our project focuses on leveraging graph neural networks (GNNs) to unlock the full potential of spatial transcriptomic data. Unlike traditional methods, GNNs can effectively capture the intricate spatial relationships between cells, enabling more accurate modeling and interpretation of gene expression dynamics across tissues. We seek motivated students to explore novel GNN architectures tailored for spatial transcriptomics, with a particular emphasis on addressing challenges such as spatial heterogeneity, cell-cell interactions, and spatially varying gene expression patterns.

Contact : Filippo Guerranti , Alessandro Palma

References:

• Cell clustering for spatial transcriptomics data with graph neural network
• Unsupervised spatially embedded deep representation of spatial transcriptomics
• SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
• DeepST: identifying spatial domains in spatial transcriptomics by deep learning
• Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder

GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data

Generative Models for Drug Discovery

Type:  Mater Thesis / Guided Research

• Proficiency with Python and deep learning frameworks (PyTorch or TensorFlow)
• Knowledge of graph neural networks (e.g. GCN, MPNN)
• No formal education in chemistry, physics or biology needed!

Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain which has experienced great attention through the success of generative models. These models promise a more efficient exploration of the vast chemical space and generation of novel compounds with specific properties by leveraging their learned representations, potentially leading to the discovery of molecules with unique properties that would otherwise go undiscovered. Our topics lie at the intersection of generative models like diffusion/flow matching models and graph representation learning, e.g., graph neural networks. The focus of our projects can be model development with an emphasis on downstream tasks ( e.g., diffusion guidance at inference time ) and a better understanding of the limitations of existing models.

Contact :  Johanna Sommer , Leon Hetzel

Equivariant Diffusion for Molecule Generation in 3D

Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation

Structure-based Drug Design with Equivariant Diffusion Models

Efficient Machine Learning: Pruning, Quantization, Distillation, and More - DAML x Pruna AI

Type: Master's Thesis / Guided Research / Hiwi

• Strong knowledge in machine learning
• Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch)

The efficiency of machine learning algorithms is commonly evaluated by looking at target performance, speed and memory footprint metrics. Reduce the costs associated to these metrics is of primary importance for real-world applications with limited ressources (e.g. embedded systems, real-time predictions). In this project, you will work in collaboration with the DAML research group and the Pruna AI startup on investigating solutions to improve the efficiency of machine leanring models by looking at multiple techniques like pruning, quantization, distillation, and more.

Contact: Bertrand Charpentier

• The Efficiency Misnomer
• A Gradient Flow Framework for Analyzing Network Pruning
• Distilling the Knowledge in a Neural Network
• A Survey of Quantization Methods for Efficient Neural Network Inference

Deep Generative Models

Type:  Master Thesis / Guided Research

• Strong machine learning and probability theory knowledge
• Knowledge of generative models and their basics (e.g., Normalizing Flows, Diffusion Models, VAE)
• Optional: Neural ODEs/SDEs, Optimal Transport, Measure Theory

With recent advances, such as Diffusion Models, Transformers, Normalizing Flows, Flow Matching, etc., the field of generative models has gained significant attention in the machine learning and artificial intelligence research community. However, many problems and questions remain open, and the application to complex data domains such as graphs, time series, point processes, and sets is often non-trivial. We are interested in supervising motivated students to explore and extend the capabilities of state-of-the-art generative models for various data domains.

Contact : Marcel Kollovieh , David Lüdke

• Flow Matching for Generative Modeling
• Auto-Encoding Variational Bayes
• Denoising Diffusion Probabilistic Models 
• Structured Denoising Diffusion Models in Discrete State-Spaces

Active Learning for Multi Agent 3D Object Detection 

Type: Master's Thesis  Industrial partner: BMW 

Prerequisites: 

• Strong knowledge in machine learning 
• Knowledge in Object Detection 
• Excellent programming skills 
• Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch) 

Description: 

In autonomous driving, state-of-the-art deep neural networks are used for perception tasks like for example 3D object detection. To provide promising results, these networks often require a lot of complex annotation data for training. These annotations are often costly and redundant. Active learning is used to select the most informative samples for annotation and cover a dataset with as less annotated data as possible.   

The objective is to explore active learning approaches for 3D object detection using combined uncertainty and diversity based methods.  

Contact: Sebastian Schmidt

References: 

• Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving   
• Efficient Uncertainty Estimation for Semantic Segmentation in Videos   
• KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
• Towards Open World Active Learning for 3D Object Detection   

Graph Neural Networks

Type:  Master's thesis / Bachelor's thesis / guided research

• Knowledge of graph/network theory

Graph neural networks (GNNs) have recently achieved great successes in a wide variety of applications, such as chemistry, reinforcement learning, knowledge graphs, traffic networks, or computer vision. These models leverage graph data by updating node representations based on messages passed between nodes connected by edges, or by transforming node representation using spectral graph properties. These approaches are very effective, but many theoretical aspects of these models remain unclear and there are many possible extensions to improve GNNs and go beyond the nodes' direct neighbors and simple message aggregation.

Contact: Simon Geisler

• Semi-supervised classification with graph convolutional networks
• Relational inductive biases, deep learning, and graph networks
• Diffusion Improves Graph Learning
• Weisfeiler and leman go neural: Higher-order graph neural networks
• Reliable Graph Neural Networks via Robust Aggregation

Physics-aware Graph Neural Networks

Type:  Master's thesis / guided research

• Proficiency with Python and deep learning frameworks (JAX or PyTorch)
• Knowledge of graph neural networks (e.g. GCN, MPNN, SchNet)
• Optional: Knowledge of machine learning on molecules and quantum chemistry

Deep learning models, especially graph neural networks (GNNs), have recently achieved great successes in predicting quantum mechanical properties of molecules. There is a vast amount of applications for these models, such as finding the best method of chemical synthesis or selecting candidates for drugs, construction materials, batteries, or solar cells. However, GNNs have only been proposed in recent years and there remain many open questions about how to best represent and leverage quantum mechanical properties and methods.

Contact: Nicholas Gao

• Directional Message Passing for Molecular Graphs
• Neural message passing for quantum chemistry
• Learning to Simulate Complex Physics with Graph Network
• Ab initio solution of the many-electron Schrödinger equation with deep neural networks
• Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
• Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Robustness Verification for Deep Classifiers

Type: Master's thesis / Guided research

• Strong machine learning knowledge (at least equivalent to IN2064 plus an advanced course on deep learning)
• Strong background in mathematical optimization (preferably combined with Machine Learning setting)
• Proficiency with python and deep learning frameworks (Pytorch or Tensorflow)
• (Preferred) Knowledge of training techniques to obtain classifiers that are robust against small perturbations in data

Description : Recent work shows that deep classifiers suffer under presence of adversarial examples: misclassified points that are very close to the training samples or even visually indistinguishable from them. This undesired behaviour constraints possibilities of deployment in safety critical scenarios for promising classification methods based on neural nets. Therefore, new training methods should be proposed that promote (or preferably ensure) robust behaviour of the classifier around training samples.

Contact: Aleksei Kuvshinov

References (Background):

• Intriguing properties of neural networks
• Explaining and harnessing adversarial examples
• SoK: Certified Robustness for Deep Neural Networks
• Certified Adversarial Robustness via Randomized Smoothing
• Formal guarantees on the robustness of a classifier against adversarial manipulation
• Towards deep learning models resistant to adversarial attacks
• Provable defenses against adversarial examples via the convex outer adversarial polytope
• Certified defenses against adversarial examples
• Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks

Uncertainty Estimation in Deep Learning

Type: Master's Thesis / Guided Research

• Strong knowledge in probability theory

Safe prediction is a key feature in many intelligent systems. Classically, Machine Learning models compute output predictions regardless of the underlying uncertainty of the encountered situations. In contrast, aleatoric and epistemic uncertainty bring knowledge about undecidable and uncommon situations. The uncertainty view can be a substantial help to detect and explain unsafe predictions, and therefore make ML systems more robust. The goal of this project is to improve the uncertainty estimation in ML models in various types of task.

Contact: Tom Wollschläger ,   Dominik Fuchsgruber ,   Bertrand Charpentier

• Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
• Predictive Uncertainty Estimation via Prior Networks
• Posterior Network: Uncertainty Estimation without OOD samples via Density-based Pseudo-Counts
• Evidential Deep Learning to Quantify Classification Uncertainty
• Weight Uncertainty in Neural Networks

Hierarchies in Deep Learning

Type:  Master's Thesis / Guided Research

Multi-scale structures are ubiquitous in real life datasets. As an example, phylogenetic nomenclature naturally reveals a hierarchical classification of species based on their historical evolutions. Learning multi-scale structures can help to exhibit natural and meaningful organizations in the data and also to obtain compact data representation. The goal of this project is to leverage multi-scale structures to improve speed, performances and understanding of Deep Learning models.

Contact: Marcel Kollovieh , Bertrand Charpentier

• Tree Sampling Divergence: An Information-Theoretic Metricfor Hierarchical Graph Clustering
• Hierarchical Graph Representation Learning with Differentiable Pooling
• Gradient-based Hierarchical Clustering
• Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space

BSc/MSc Thesis

Our research group offers various interesting topics for a BSc or MSc thesis, the latter both in Computer Science and Scientific Computing . These topics are typically closely related to ongoing research projects (see our Research Page and Publications ). Below, we outline the basic procedure you should follow when planning to do a thesis in our group. Please read the following carefully! You also might want to take a quick look at past topics students covered in their theses. Please also note that we currently cannot accommodate all requests for advising a thesis as in current semester  as well as in the upcoming summer semester 2024 we are already advising numerous MSc and BSc theses.

Requirements

A key requirement is that you have taken some advanced courses offered by our group. This includes Data Science for Text Analytics  or  Complex Network Analysis (ICNA) and the more recent master level class on Natural Language Processing with Transformers  (INLPT). Student should also have some background in machine learning, ideally in combination with NLP. We also strongly recommend that prior to starting a thesis (especially a BSc thesis) in our group, you do an advanced software practical to become familiar with the data and tools we use in many of our projects. Most students typically do this in the semester before they officially start their thesis. Further requirements include

• very good programming experience with Python (strongly preferred, including framework like pandas and numpy)
• solid background in statistics and linear algebra
• (optionally) experience with the machine learning frameworks such as PyTorch
• (optionally) experience with NLP frameworks such as spaCy, gensim, LangChain
• (optionally) experience with Opensearch or Elasticsearch
• knowledge using tools such as Github and Docker

It is also advantageous if you have taken some graduate courses in the areas of efficient algorithms (e.g., IEA1 ) and in particular machine learning (e.g., IML , IFML or IAI ). Being familiar with frameworks like scikit-learn , Keras or PyTorch is advantageous.

If you have only taken the undergraduate course introduction to databases (IDB) and none of the other above courses, it is unlikely that we can accommodate your request.

Make also sure that you are familiar with the examination regulations ("Prüfungsordnung") that apply to your program of study.

Getting in Contact

Prior to getting in contact with us you should, of course, read this page in its entirety. If you think your interests and expertise are a good fit for our group and research activities, send an email to Prof. Michael Gertz with the subject "Anfrage BSc Arbeit" or "Anfrage MSc Arbeit" and include the following information:

• your current transcript (as PDF). You can download this from the LSF .
• information about your field of application ("Anwendungsfach"), in particular the courses you have taken
• your programming experience and projects you worked on
• areas of interest based on the research conducted in our group
• any other information you think might strengthen your request

We will then review this information and get back to you with the scheduling of an appointment in person to discuss further details.

Thesis Expose

Once we agree on a topic for your thesis, before you officially register for a thesis, we would like to get an idea of how you approach scientific research and whether you are able to do scientific writing. For this, we require that you write an expose of your planned thesis research (see, e.g., here or here ) . This document is about 4-6 pages and has to include a description of

• the context of your project and research
• problem statement(s)
• objectives and planned approaches
• related work
• milestones towards a timely completion of the thesis

Especially for the related work, it is important that you get a good overview  early on in your thesis project; of course, your advisor will give you some starting points. Most of the time, such an expose becomes an integral part of the introductory chapter of your thesis, so there is no time and effort wasted. The expose needs to be submitted to your advisor on schedule (which you arrange with your advisor), who will then discuss the expose with you and coordinate the next steps. Occasionally we also have students give a 10-15 minute presentation of their research plan in front of the members of our group in order to get further ideas, comments, suggestions, and pointers on their thesis.

Official Registration

In agreement with your advisor, after you have submitted an expose of good quality, you plan for an official start date of the thesis. For this, please fill out the  form suitable for your program of study:

• Für Anmeldung einer Bachelorarbeit, siehe hier . 
• For officially registering your master's thesis, see here . 
• Registration form for a MSc thesis in Scientific Computing (please see Mrs. Kiesel to obtain a form).

Hand in this form to Prof. Michael Gertz who will then turn in the signed form.

Thesis Research and Advising

• Here are some hints on grammar and style we maintain locally.
• Some easy, purely syntactic  hints  on writing good research papers (from Prof. Felix Naumann )
• Dos and don'ts, Universität Heidelberg, Prof. Dr. Anette Frank
• Leitfaden zur Abfassung wissenschaftlicher Arbeiten, Ruhr-Universität Bochum, Katarina Klein
• Leitfaden zur Abfassung wissenschaftlicher Arbeiten, TU Dresden, Maria Lieber

In addition, you can find a detailed description how to write a seminar paper using our template for seminar papers. The hints in this template might also be crucial when you are writing a thesis: [ seminar template .zip ] [ report sample pdf ] [ slides english pdf ] [ slides german pdf ]

Feel also free to ask us for copies of BSc/MSc thesis students did in the past in our group.

Thesis Template

• Thesis template [.zip] ; see a sample PDF here .

Thesis Presentation

• English LaTeX-Beamer template for the presentation: template [.zip] , sample PDF
• German LaTeX-Beamer template for the presentation: template [.zip] , sample PDF

Master Data Science

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The two year Master program in Data Science is built on the Data Science B.Sc. program and can be started in either the spring or fall semester. It is a  joint project of the Faculties of Statistics, Computer Science and Mathematics . As the tasks are always and rapidly changing in this research field, the study program provides a sound statistical and mathematical theory and teaches contemporary methods and knowledge. Hence, the students are capable to do scientific research, have a scientific awareness and can trade it responsibly. The interdisciplinary ability to communicate about methods and their application, especially at the interface between statistics, computer science, mathematics and fields of application, is a key aspect of the study program.

Students who have already completed a B.Sc. program in Statistics, Mathematics, Computer Science, or related fields can be admitted for this Master program (possibly with certain  restrictions ).

All mandatory courses are  taught in English , elective courses in German may be offered.

Structure of study program

This course offers different core and elective modules. The selection of modules can be chosen from a wide range of different courses according to the individual interests of the student. Through internships and project work courses as well as the Master thesis, students have the opportunity to apply their gained knowledge. Based upon previous studies, the advanced courses cover statistical theory, statistical learning, big data, and the application of methods in chosen areas of study (case study, seminars and electives). Emphasis is placed on the development and application of efficient procedures for analyzing in particular very large amounts of data. There are eleven modules (including a six month Master thesis) during the two years of study.

Perspectives

This Master program gives students the opportunity to deepen and broaden their research skills and  methodological competence, subsequent to their B.Sc. in Data Science or related programs. They are able to develop new methods and to manage and direct large data projects. The students become experts in this field and are highly sought after by employers in many areas, particularly for the independent handling of projects with large amounts of data.

For qualified students, the program opens up the possibility for doctoral studies afterwards.

The English-language program prepares students for an international professional orientation and, on the other hand, gives foreign students the opportunity to gain a foothold in Germany.

Important files and more information

Admission to the Master program is only possible if certain requirements have been met. These and further information on enrollment can be found  here .

• Terms of Study
• Amendments of Terms of Study, from winter semester 2020/21
• Module Handbook M.Sc. Data Science
• Recommended Course of Study M.Sc. Data Science

Note: In each case, the terms of study are relevant; the recommended course of study represents only a possibility.