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Stuhlsatzenhaus 5
66123 Saarbrücken (Germany)


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.

CV: Letzte vier Stationen

2019 - now
Professor, Saarland University
2018 - now
Faculty, CISPA Helmholtz Center for Information Security
2011 - 2018
Senior Researcher, Max Planck Institute for Informatics
2008 - 2010
PostDoc International Computer Science Institue & UC Berkeley

Veröffentlichungen von Mario Fritz

Jahr 2020

Konferenz / Medium

ACM SIGSAC Conference on Computer and Communications Security

Konferenz / Medium

European Conference on Computer Vision (ECCV)

Konferenz / Medium

IEEE Conference on Computer Vision and Pattern Recognition

Lehre von Mario Fritz

Summer 2020

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".

Summer 2020

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.

Winter 2019/20

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.

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