Faculty
Mario Fritz is faculty member at the CISPA Helmholtz Center for Information Security and professor at the Saarland University. Before, he was senior researcher and research group head at the Max Planck Institute for Informatics, and PostDoc at the International Computer Science Institute and UC Berkeley. He did his PhD at the TU Darmstadt. His current work is centered around Trustworthy Information Processing with a focus on the intersection of AI \& Machine Learning with Security \& Privacy. He served as Area Chair for major computer vision conferences (ECCV, ICCV), associate editor of IEEE TPAMI and is a member of the ACM Europe Technical Policy Committee Europe. He has co-authored over 100 publications, including more than 50 in top-tier journals (IJCV, TPAMI) and conferences (CVPR, ICCV, ECCV, NeurIPS, AAAI, ICLR, NDSS, USENIX Security, CCS, S\& P). He is also a leading scientist of the Helmholtz Medical Security, Privacy, and AI Research Center, where he is coordinating projects on trustworthy federated data-analytics and protecting genetic data with synthetic cohorts from deep generative models.
CVPR
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020IEEE Conference on Computer Vision and Pattern Recognition
UNSPECIFIED
NeurIPS Workshop on Computer-Assisted ProgrammingNeurIPS Workshop on Computer-Assisted Programming
CVPR
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020IEEE Conference on Computer Vision and Pattern Recognition
ArXiv
SP
2020 IEEE Symposium on Security and Privacy (S&P)IEEE Symposium on Security and Privacy (S&P)
ICLR
8th International Conference on Learning RepresentationsInternational Conference on Representation Learning (ICLR)
Neurocomputing
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Proseminar: Trustworthy Machine Learning
Machine learning has made great advances over the past year and many techniques have found their ways into applications. This leads to an increasing demand of techniques that not only perform well - but are also "trustworthy".
Lecture: High Level Computer Vision
This course will cover essential techniques for high-level computer vision. These techniques facilitate semantic interpretation of visual data, as it is required for a broad range of applications like robotics, driver assistance, multi-media retrieval, surveillance etc.
Lecture: Machine Learning in Cybersecurity
Recent advances in Machine Learning has lead to near (or beyond) human-level performance in many tasks - autonomous driving, voice assistance, playing a variety of games.