Prof. Dr. Stefan Bauer
Academic Career and Research Areas
We work on developing algorithms that learn causal relationships from high-dimensional inputs, explain their decisions, and adapt quickly to new problems. All these requirements are key prerequisites for robust and transformative AI-based technologies with various downstream applications.
Stefan Bauer is an Associate Professor at TU Munich and senior PI at Helmholtz AI. He is a CIFAR Azrieli Global Scholar and prior to his appointment in Munich, he was an Assistant Professor at KTH Stockholm and a Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen. Stefan Bauer obtained his Ph.D. in Computer Science from ETH Zurich in 2018, for which he was honored with the ETH Medal for outstanding dissertations. He previously studied Mathematics at ETH Zurich and Economics and Finance at the University of London.
Awards
- CIFAR Azrieli Fellowship Learning in Machines and Brains Program (2020)
- Best paper International Conference for Machine Learning (ICML) (2019)
- ETH medal for outstanding doctoral thesis (2018)
Key Publications (all publications)
N. Pfister, S. Bauer and J. Peters. "Identifying Causal Structure in Large-Scale Kinetic Systems“. Proceedings of the National Academy of Sciences (PNAS) (2019).
AbstractB. Schölkopf, F. Locatello, S. Bauer, R. Nan Ke, N. Kalchbrenner, A. Goyal and Y. Bengio. "Towards Causal Representation Learning“. Proceedings of the IEEE - Advances in Machine Learning and Deep Neural Networks (2020).
AbstractS. Bauer*, M. Wüthrich*, F. Widmaier*, N. Funk, J. De Jesus, J. Peters, J. Watson, C. Chen, K. Srinivasan, J. Zhang, J. Zhang, M. Walter, R. Madan, C. Schaff, T. Maeda, T. Yoneda, D. Yarats, A. Allshire, E. Gordon, T. Bhattacharjee, S. Srinivasa, A. Garg, A. Buchholz, S. Stark, T. Steinbrenner, J. Akpo, S. Joshi, V. Agrawal and B. Schölkopf. "A Robot Cluster in the Cloud for Reproducible Research in Dexterous Manipulation“. NeurIPS Competition Track (2021).
AbstractP. Tigas, Y. Annadani, A. Jesson, B. Schölkopf, Y. Gal and S. Bauer. "Interventions, Where and How? Experimental Design for Causal Models at Scale“. Neural Information Processing Systems (NeurIPS) (2022).
AbstractZ. Rao, P-Y. Tung, R. Xie, Y. Wei, H. Zhang, A. Ferrari, T. Klaver, F. Körmann, P. Sukumar, A. Kwiatkowski da Silva, Y. Chen, Z. Li, D. Ponge, J. Neugebauer, O. Gutfleisch, S. Bauer and D. Raabe. "Machine learning enabled fast high-entropy alloy discovery - a case study on novel INVAR alloys“. Science (2022).
Abstract