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Stefan Heidekrüger |
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Department of Informatics (I18)
Technical University of Munich
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| E-Mail: | stefan heidekrueger tum de . @ . |
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Room 01.10.056
Boltzmannstr. 3
85748 München, Germany
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| Phone: | +49 (0) 89 289 - 17530 | |
| Hours: | by arrangement | |
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I'm a PhD student at the DSS chair supervised by Prof. Bichler. My research focusses on computation of equilibria in incomplete information games, especially markets and auctions and using multi-agent reinforcement learning methods.
We offer topics for student projects (MSc or BSc theses, IDP, Guided Research) in this research area on a continuing basis. If you're looking for a project and are interested in (Deep and/or Multi-Agent) Reinforcement Learning, Nonlinear Optimization, Market Design and/or Algorithmic Game Theory, feel free to get in touch. (For an MSc thesis, you should already have prior experience with one or more of these.)
Short Bio
Education
- 2014 - 2016 M.Sc. Mathematics in Operations Research, Technische Universität München
- 2014 Erasmus+ student at KTH Royal Institute of Technology (Stockholm, Sweden)
- 2012 - 2013 Visiting Student at The Hong Kong University of Science and Technology
- 2010 - 2014 B.Sc. Mathematics, Technische Universität München
Working Experience
- Since 2018 Research Associate, Decision Sciences & Systems, Technische Universität München
- 2016 - 2018 Data Scientist, Business Analytics and Artificial Intelligence, Telefónica Germany
- 2013 - 2016 internships at a.hartrodt (2013) and zeb.rolfes.schierenbeck.associates (2015)
working student positions at a.hartrodt (2013-14), Telefónica Germany (2016), and SAP (2016)
student research assistant positions at TUM (2014, 2015) and HelmholtzZentrum München (2015-16)
Publications
S. Heidekrüger, P. Sutterer, and M. Bichler. Computing approximate bayes-nash equilibria through neural self-play. In Workshop on Information Technology and Systems (WITS19), Munich, Germany, 2019.
Conference Talks
- Computing approximate Bayes-Nash equilibria through Neural Self-Play (Workshop on Information Technologies and Systems, Munich, Germany, 12/2019)
- Computing Bayes-Nash Equilibria in Sealed Bid Auctions through Self Play (International Meeting on Operations Research, Dresden, Germany, 09/2019)
Teaching
Courses
- Business Analytics, Teaching Assistant (Winter Term 2018/19, 2019/20)
- Seminar on Data Mining, TA (Summer Term 2019, 2020)
- Seminar ITUB - "IT and Management Consulting", TA (Winter Term 2019/20)
Completed Student Projects
- Kevin D. Falkenstein Learning Equilibrium Strategies in Auctions via Deep Neural Networks, MSc Thesis (2018)
- Sebastian Rief Detection of anomalies in large-scale accounting data using unsupervised machine learning, MSc Thesis (2019)
- Florian Ziesche Human Interpretable Machine Learning: A Machine Learning Approach for Risk Scoring, MSc Thesis (2019)