Stefan Heidekrüger
	Department of Informatics (I18) Technical University of Munich
	E-Mail:	stefan heidekrueger   tum  de           .                    @       .
	Office:	Room 01.10.056 Boltzmannstr. 3 85748 München, Germany
	Phone:	+49 (0) 89 289 - 17530
	Hours:	by arrangement

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.

Publications 

Working Papers

Learning to Bid: Computing Bayesian Nash Equilibrium Strategies in Auctions via Neural Pseudo-gradient Ascent
Bichler, M.; Fichtl, M.; Heidekrüger, S.; Kohring, N.; and Sutterer, P
Working Paper, 2021.
A recent version was presented at the 2020 annual meeting of the NBER Working Group on Market Design: Link

Workshop Papers

Equilibrium Learning in Combinatorial Auctions: Computing Approximate Bayesian Nash Equilibria via Pseudogradient Dynamics 
S. Heidekrüger, P. Sutterer, N. Kohring, M. Fichtl, and M. Bichler
presented at the 2020 Workshop on Information Technology and Systems (WITS20), and the 2021 AAAI Workshop on Reinforcement Learning in Games (AAAI-RLG-21) Link

Multiagent Learning for Equilibrium Computation in Auction Markets,
S. Heidekrüger, P. Sutterer, N. Kohring, and M. Bichler,
AAAI Spring Symposium on Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL-21), March 2021 (forthcoming)

Computing approximate Bayes-Nash Equilibria through Neural Self-Play.
S. Heidekrüger, P. Sutterer, and M. Bichler
Workshop on Information Technology and Systems (WITS19), Munich, Germany, 2019.

Teaching

Courses

• Business Analytics, Teaching Assistant (Winter Term 2018/19, 2019/20, 20/21)
• Seminar on Data Mining, TA   (Summer Term 2019, 2020)
• Seminar ITUB - "IT and Management Consulting", TA  (Winter Term 2019/20, 20/21)

Completed Student Projects

• Daniel Schroter	Reinforcement Learning in the MIT Beer Distribution Game, BSc Thesis, Informatics (2020)
  Markus Ewert	Efficient Query Strategies in Preference Elicitation via Deep Learning, MSc Thesis, Information Systems (2020)
  Anne Christopher	Fast Solvers for Batched Constrained Optimization Problems, MSc Thesis, Mathematics in Data Science (2020)
  Lukas Feye	Confidence-Moderated Policy Advice in Multi-Agent Reinforcement Learning, BSc Thesis, Information Systems (2020)
  Florian Ziesche	Human Interpretable Machine Learning: A Machine Learning Approach for Risk Scoring, MSc Thesis, Mgmt & Technlogy (2019)
  Sebastian Rief	Detection of anomalies in large-scale accounting data using unsupervised machine learning, MSc Thesis, Mgmt & Tech. (2019)
  Kevin D. Falkenstein	Learning Equilibrium Strategies in Auctions via Deep Neural Networks, MSc Thesis, Information Systems (2018)