Lampert Group
Machine Learning and Computer Vision
The Lampert group performs research on how to make artificial intelligence methods more trustworthy. It investigates questions like: Can we understand not only what modern machine learning systems are doing, but also why? Can we give guarantees for their behavior? Can we build systems that learn and one day might become intelligent without sacrificing our rights to data protection and privacy?
Computers are becoming increasingly powerful at processing data, and they have learned to perform many tasks that were thought beyond their reach, such as making successful financial investments, diagnosing cancer from medical images, and even driving cars in traffic. So why don’t we rely on them as financial advisors, oncologists, and chauffeurs? It is likely because we do not trust computers enough to let them operate important systems autonomously and outside of our control. Besides theoretical research, the group applies its results to applications in computer vision, such as image understanding, where the goal is to develop automatic systems that can analyze the contents of natural images.
Group Leader
Administrative Support
Team
Current Projects
Trustworthy machine learning | Transfer and lifelong learning | Theory of deep learning
Publications
Henzinger TA, Kresse F, Mallik K, Yu E, Zikelic D. 2025. Predictive monitoring of black-box dynamical systems. 7th Annual Learning for Dynamics & Control Conference. L4DC: Learning for Dynamics & Control, PMLR, vol. 283, 804–816. View
Kresse F, Yu E, Lampert C, Henzinger TA. 2025. Logic gate neural networks are good for verification. 2nd International Conferenceon Neuro-Symbolic Systems. NeuS: International Conferenceon Neuro-Symbolic Systems, PMLR, vol. 288, 26. View
Prach B. 2025. Robust image classification with 1-Lipschitz networks. Institute of Science and Technology Austria. View
Kalinin N, Steinberger L. 2025. Efficient estimation of a Gaussian mean with local differential privacy. Proceedings of the 28th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 258, 118–126. View
Lutsai K, Lampert C. 2024. Predicting the geolocation of tweets using transformer models on customized data. Journal of Spatial Information Science. (29), 69–99. View
ReX-Link: Christoph Lampert
Career
Since 2015 Professor, Institute of Science and Technology Austria (ISTA)
2010 – 2015 Assistant Professor, Institute of Science and Technology Austria (ISTA)
2007 – 2010 Senior Research Scientist, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
2004 – 2007 Senior Researcher, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
2003 PhD, University of Bonn, Germany
Selected Distinctions
Since 2015 Associate Editor in Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)
2012 ERC Starting Grant (consolidator phase)
2008 Best Paper Award, IEEE Conference for Computer Vision and Pattern Recognition (CVPR)
2008 Best Student Paper Award, European Conference for Computer Vision (ECCV)
2008 Main Prize, German Society for Pattern Recognition (DAGM)
