Web page of Philipp Geiger

Overview

Welcome on this page. My name is Philipp Geiger, and here I give an overview over my work in research and software development, and general background. Currently, I'm an industry research scientist at Bosch Center for Artificial Intelligence. Before, I did my doctorate in computer science and a postdoc at Max Planck Institute for Intelligent Systems and University of Stuttgart, including a stay at Microsoft Research Cambridge, and my diplom in mathematics with philosophy as side subject at Heidelberg University and Humboldt University of Berlin.

Generally, I conduct research and development in a broad range of topics in machine learning (core part of artificial intelligence), in particular imitation learning, reinforcement learning, deep learning, foundation models, and multiagent systems (analysis and optimization of multiple intelligent decision makers' interaction behavior). Often, this combines project-driven software engineering, experimentation, statistical/mathematical analysis, as well as supervision and stakeholder management responsibilities, and aims at innovative solutions for problems important to business and society as a whole. Applications I work on include energy-efficient building control, industrial design optimization and autonomous driving and its safety validation (see also further background).

I'm also increasingly interested in the political aspects of artificial intelligence.

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

1. Fail-Safe Adversarial Generative Imitation Learning. (2022). TMLR.
2. Learning game-theoretic models of multiagent trajectories using implicit layers. (2021). AAAI.
3. Causal inference by identification of vector autoregressive processes with hidden components. (2015). ICML. [Slides.]

Further links: my profiles on Google Scholar, DBLP, 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. This is part of the EU-funded ELIAS project.

  [Posted on 2025-10-02.]

• 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.) 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 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

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

• Methodology:
  • general mathematics (proving), statistics,
  • machine learning:
    • imitation learning (behavior cloning, generative adversarial imitation learning, inverse reinforcement learning, etc.) and reinforcement learning (model-based reinforcement learning, Bayesian optimization, etc.),
    • deep/representation learning (Transformers, graph neural nets, etc.),
    • causal models, interpretable machine learning,
    • generative/probablistic models (generative adversarial networks (GANs), normalizing flows, etc.),
    • 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); web development.
• Applications: autononomous and highly automated driving; modeling and control for energy-efficient heating, ventilation and air conditioning (HVAC) systems; industrial design optimization; 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).