Philipp Geiger

Address Max-Planck-Ring 4, 72076 Tübingen, Germany
Contact E-Mail: upon request, web:, phone: upon request


Degrees Doctorate in computer science, diplom (~ MSc) in mathematics
Research Modeling via machine learning, causal inference (quasi-experiments, counterfactuals), time series analysis (demand forecasting); multi-agent decision making (game theory)
Application Implementing real-world system using Python, MySQL, in collaboration with engineers


04/2017 – present
Postdoc researcher
Max Planck Institute for Intelligent Systems, Tübingen, Germany
  • Leading research project Cafeteria Coordination, including a real-world sensor / demand forecasting / web app system for efficient usage of facilities by many agents
  • Applying machine learning (Kalman filtering, exponential smoothing, ridge regression in Python, R, MySQL), game theory (best-response dynamics, truthfulness)
  • Coordinating with software engineers, work councils, privacy officers, manufacturers
07/2015 – 10/2015
Research intern
Microsoft Research Ltd., Cambridge, United Kingdom
  • Worked on AI research project in close collaboration with software engineers


06/2013 – 03/2017
Doctorate in computer science (equivalent to PhD)
Max Planck Institute for Intelligent Systems, Tübingen, and University of Stuttgart, Germany
  • Thesis title: "Causal models for decision making via integrative inference"
  • Grade: magna cum laude
  • Supervisors: Bernhard Schölkopf, Dominik Janzing and Marc Toussaint
  • Focused on time series, quasi-experiments, counterfactuals and decision making
  • Applied Gaussian process regression and vector autoregressive processes to economic and cloud computing data (using Python, Matlab and R)
10/2006 – 12/2012
Diplom in mathematics (equivalent to MSc)
Heidelberg University and Humboldt University of Berlin, Germany
  • Thesis title: "Mutual Information and Gödel Incompleteness"
  • Grade: 1.4 (best score 1.0 of 5.0)
  • Specialization: mathematical logic, theoretical computer science; minor: philosophy


  1. Geiger, P., Zhang, K., Gong, M., Janzing, D., & Schölkopf, B. (2015). Causal inference by identification of vector autoregressive processes with hidden components. In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015).
  2. Gong, M., Zhang, K., Schoelkopf, B., Tao, D., & Geiger, P. (2015). Discovering temporal causal relations from subsampled data. In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015).
  3. Geiger, P., Janzing, D., & Schölkopf, B. (2014). Estimating causal effects by bounding confounding. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014).
  1. Geiger, P., Carata, L., & Schoelkopf, B. (2016). Causal inference for cloud computing. ArXiv Preprint ArXiv:1603.01581.
  2. Geiger, P., Hofmann, K., & Schölkopf, B. (2016). Experimental and causal view on information integration in autonomous agents. ArXiv Preprint ArXiv:1606.04250.
  1. Geiger, P. (2017). Causal models for decision making via integrative inference. PhD thesis.
  2. Geiger, P. (2012). Mutual information and Gödel incompleteness. Diploma thesis.

Supervision, teaching and reviewing

10/2016 – 03/2017
  • Student: Claudius Proissl (University of Stuttgart); research project during MSc
10/2013 – 02/2014
Teaching assistant
University of Tübingen, Germany
  • Lecture "Intelligent Systems I": a first course in machine learning
10/2011 – 04/2012
Teaching assistant
Heidelberg University, Germany
  • Lecture "Computability and Computational Complexity Theory I"
10/2014 – present
  • Conferences: NIPS 2014, ICML 2016, UAI 2016, NIPS 2016, ICML 2017, NIPS 2017
  • Journals: ACM Transactions on Intelligent Systems and Technology, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Knowledge and Data Engineering, International Journal of Data Science and Analytics


  • Machine learning (Gaussian process regression, ridge regression, Kalman filtering, exponential smoothing and vector autoregression) with Python (working knowledge), R and Matlab (basic knowledge)
  • Object-oriented programming with Python, C++ and Java (basic knowledge)
  • Web development with HTML, CSS (working knowledge), JavaScript, MySQL and web framework Django/Python (basic knowledge)
German (native), English (fluent), French (beginner)

Memberships and awards

09/2015 – 06/2017 Associate Doctoral Fellow of Max Planck ETH Center for Learning Systems
07/2005 Award for outstanding results in physics by German Physical Society (DPG)


Prof. Bernhard Schölkopf Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany
Dr. Katja Hofmann Microsoft Research Ltd., Cambridge, United Kingdom
Dr. Wolfgang Merkle Institute for Computer Science, Heidelberg University, Germany