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Stuhlsatzenhaus 5
66123 Saarbrücken (Germany)

Short Bio

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: Last four stations

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

Publications by Mario Fritz

Year 2019

Conference / Medium

CVPR
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Conference / Medium

CVPR
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Conference / Medium

NDSS
Network and Distributed Systems Security (NDSS) Symposium 2019Annual Network and Distributed System Security Symposium

Article

Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

Conference / Medium

WACV
IEEE Winter Conference on Applications of Computer Vision (WACV)IEEE Winter Conference on Applications of Computer Vision (WACV)

Conference / Medium

ICLR
International Conference on Learning RepresentationsInternational Conference on Representation Learning (ICLR)

Year 2018

Conference / Medium

NeurIPS
Advances in Neural Information Processing Systems 31 (NeurIPS 2018)Conference on Neural Information Processing Systems

Conference / Medium

ECCV
Proceedings of the European Conference on Computer Vision (ECCV)European Conference on Computer Vision

Conference / Medium

ECCV
Proceedings of the European Conference on Computer Vision (ECCV)European Conference on Computer Vision (ECCV)

Teaching by Mario Fritz

Winter 2021/22

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".

Trustworthiness includes:
- Interpretability of the prediction
- Robustness against changes to the input, which occur naturally or with malicious intend
- Privacy preserving machine learning (e.g. when dealing with sensitive data such as in health applications)
- Fairness
- ...

Description

As a proseminar’s primary purpose is to learn presentation skills, the seminar will feature two presentations from each student. 

In the first half of the semester, we will have presentations of two topics each week. After each presentation, fellow students and lecturers will provide feedback on how to improve the presentation. This general feedback must then be taken into account for the second half of the semester, where again each student will present.

Grading

The first presentations  will count towards 30% of the overall grade, the second presentation will count towards 70% of the overall grade. Attendance in the proseminar meetings is mandatory. At most one session can be skipped, after that you need to bring a doctor’s note to excuse your absence.

Winter 2021/22

Machine Learning in Cyber Security

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. In terms of privacy and security, this is a double-edged sword. ML techniques can be used to efficiently detect and prevent attacks (e.g., intrusion detection). However, their deployment to many real-world sensitive systems (e.g., self-driving cars, the cloud) also makes them susceptible to numerous attacks, such as introducing imperceptible perturbations in inputs and forcing ML systems behave in unintended ways.

The course explores in-depth both of these sides to Machine Learning and Cyber Security. The content addresses the following areas:

  1. ML overview
  2. ML for improving security
  3. Attacks on ML
  4. Defenses for ML
  5. ML and Privacy

While we do a brief recap in the beginning, the course requires knowledge on Machine Learning.

Date for lecture: Tuesdays noon to 2pm. 

Date for exercise: Fridays 2pm to 4pm

Due to the size of the course - the lecture will start in an online format until further notice.

The course requires prior knowledge on Machine Learning.

Once you have registered - please find internal information and schedule and links here (under construction).

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.

More information