Web page of Philipp Geiger

Overview

Welcome to my web page where I provide some of my background, interests, research publications and data/software projects.

Currently I'm an industrial researcher at Bosch Center for Artificial Intelligence. Throughout my doctoral studies and career, I've been working in research and development in the areas of machine learning and multiagent systems, combining mathematical analysis and software engineering, as well as stakeholder communication and technical sub-project supervision. I'm aiming at innovative solutions for problems important to business and society as a whole (see also further background).

Selected publications (see also all publications and further material):

1. Analyzing Closed-Loop Training Techniques for Realistic Traffic Agent Models in Autonomous Highway Driving Simulations. (2025). ICCV Workshop.
2. Fail-Safe Adversarial Generative Imitation Learning. (2022). TMLR.
3. Causal inference by identification of vector autoregressive processes with hidden components. (2015). ICML. [Slides.]

Selected publicly available software/data projects:

• AI-Based Modeling for Energy-Efficient Buildings (2025): a EU-funded competition, including a large, high-dimensional building time series dataset.

Further links: my profiles on LinkedIn, Google Scholar, OpenReview, GitHub.

News

• AI-based modeling for energy-efficient buildings:

  Energy-efficient buildings are a high-dimensional, complex modeling and optimization problem. We launched a Kaggle modeling competition on this problem: with a big dataset consisting of 1.5 years and hundreds of sensor time series, of a Bosch campus with multiple buildings, and example code available. This is part of the EU-funded ELIAS project.

  [Read more. Posted on 2025-10-02. Updated on 2026-09-05.]

• Analyzing Closed-loop Training Techniques for Realistic Traffic Agent Models in Autonomous Highway Driving Simulations:

  At Bosch Research, we conducted an extensive comparison of different approaches for (generative) imitation learning of realistic traffic agents from real-world human driving data, for driving behavior simulations, check out our preprint.

  [Posted on 2024-12-27.]

• On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows:

  In our recent ITSC workshop paper, we investigate two approaches for estimating the (tail) quantiles of driver action distributions (policies), including a version of conditional normalizing flows.

  [Posted on 2023-06-08. Updated on 2024-08-22.]

Publications

All peer-reviewed publications

1. Bitzer, M., Cimurs, R., Coors, B., Goth, J., Ziesche, S., Geiger, P., & Naumann, M. (2025). Analyzing Closed-Loop Training Techniques for Realistic Traffic Agent Models in Autonomous Highway Driving Simulations. 2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). [publication.]
2. Tee, J. Y., De Candido, O., Utschick, W., & Geiger, P. (2023). On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). [publication.]
3. Geiger, P., & Straehle, C.-N. (2022). Fail-Safe Adversarial Generative Imitation Learning. Transactions on Machine Learning Research. [publication.]
4. Geiger, P., & Straehle, C.-N. (2021). Learning game-theoretic models of multiagent trajectories using implicit layers. Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI). [publication.]
5. Etesami, J., & Geiger, P. (2020). Causal Transfer for Imitation Learning and Decision Making under Sensor­‐shift. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI). [publication.]
6. Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., & Schölkopf, B. (2019). Coordinating users of shared facilities based on data-driven assistants and game-theoretic analysis. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI). [publication, slides.]
7. Geiger, P., Hofmann, K., & Schölkopf, B. (2016). Experimental and causal view on information integration in autonomous agents. Proceedings of the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA), 21–28. [publication, slides.]
8. Geiger, P., Zhang, K., Gong, M., Janzing, D., & Schölkopf, B. (2015). Causal inference by identification of vector autoregressive processes with hidden components. Proceedings of the 32nd International Conference on Machine Learning (ICML). [publication, slides.]
9. Gong, M., Zhang, K., Schoelkopf, B., Tao, D., & Geiger, P. (2015). Discovering temporal causal relations from subsampled data. Proceedings of the 32nd International Conference on Machine Learning (ICML). [publication.]
10. Geiger, P., Janzing, D., & Schölkopf, B. (2014). Estimating causal effects by bounding confounding. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI). [publication, supplement, slides.]

Preprints

1. Geiger, P., Carata, L., & Schoelkopf, B. (2016). Causal inference for data-driven debugging and decision making in cloud computing. ArXiv Preprint ArXiv:1603.01581. [publication.]

Theses

1. Geiger, P. (2017). Causal models for decision making via integrative inference [PhD thesis]. [publication, slides.]
2. Geiger, P. (2012). Mutual information and Gödel incompleteness [Diploma thesis]. [publication.]

Notes

1. Geiger, P. (2016). Notes on socio-economic transparency mechanisms. ArXiv Preprint ArXiv:1606.04703. [publication.]

Further material, slides

1. On Mathematical Guarantees in Machine Learning for Safe Autonomous Driving. (2023). Mathematics in Sciences, Engineering, and Economics Symposium, Karlsruhe Institute of Technology. [, slides.]

Bio

Short bio:

• Since 2018, I'm an industry research scientist in research and development at the Bosch Center for Artificial Intelligence (BCAI), Renningen, Germany.
• From 2017 to 2018, I was a postdoctoral researcher at the Empirical Inference Department of the Max Planck Institute for Intelligent Systems, Tübingen, Germany.
• From 2013 to 2017 I pursued my PhD (Dr. rer. nat.) in computer science under the supervision of Dominik Janzing, Bernhard Schölkopf and Marc Toussaint at the Max Planck Institute for Intelligent Systems and the University of Stuttgart, Germany.
• In 2015, I did an internship stay at Microsoft Research Cambridge, United Kingdom.
• From 2006 till 2012, I studied mathematics, with philosophy as minor subject, at Humboldt University of Berlin and at Heidelberg University, Germany.
• In 2005, I finished high school in Karlsruhe, Germany

Recent focus: Recently I have worked on multiagent imitation learning, deep learning and foundation model fine-tuning applied to autonomous driving and its safety validation, as well as Bayesian optimization, time series analysis and agentic AI systems for modeling and control of heating, ventilcation and air conditioning (HVAC) for energy-efficient buildings. More broadly, I'm also increasingly interested in the political aspects of artificial intelligence.

Overall areas of expertise - with theoretical and/or engineering experience - and further interests:

• Methodology:
  • general mathematics (proving), statistics,
  • machine learning:
    • imitation learning and reinforcement learning ,
    • deep/representation learning ,
    • causal models, interpretable machine learning,
    • generative/probablistic models ,
    • multivariate time series analysis, anomaly detection,
  • multiagent systems, game theory,
  • dynamical systems,
  • mathematical logic (in particular Goedel incompleteness, computability theory).
• Coding: software engineering in projects, in particular data/machine learning pipelines (typically in Python, with PyTorch), benchmarking/evaluation of methods; web development.
• Applications: autononomous and highly automated driving; modeling and control for energy-efficient heating, ventilation and air conditioning (HVAC) systems; industrial design optimization for electrical machines; resource-efficient sharing economy.
• Supervision and communication: supervision of students and sub-projects, cross-functional communication (including reporting to top-level management) and stakeholder management.
• Further areas of interest (without research contributions yet): economics, politics and psychology.

Contact

Name: Philipp Geiger,
email address (at Bosch): (insert first name).w.(insert last name)@de.bosch.com.

About this web page: I used these tools to build this web page as well as my CV:

• Font Awesome,
• Jekyll (static CMS).