Send email Copy Email Address

Email

Address

Stuhlsatzenhaus 5
66123 Saarbrücken (Germany)

Short Bio

Mario Fritz is a faculty member at the CISPA Helmholtz Center for Information Security, an honorary professor at Saarland University, and a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). Until 2018, he led a research group at the Max Planck Institute for Computer Science. Previously, he was a PostDoc at the International Computer Science Institute (ICSI) and UC Berkeley after receiving his PhD from TU Darmstadt and studying computer science at FAU Erlangen-Nuremberg. His research focuses on trustworthy artificial intelligence, especially at the intersection of information security and machine learning. He is Associate Editor of the journal "IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)," coordinates the Helmholtz project "Trustworthy Federated Data Analytics," and has published over 100 scientific articles - 80 of them in top conferences and journals.

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 2021

Conference / Medium

AISec
AISec '21: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security14th ACM Workshop on Artificial Intelligence and Security, co-located with the 28th ACM Conference on Computer and Communications Security

Conference / Medium

UNSPECIFIED
MTD '21: Proceedings of the 8th ACM Workshop on Moving Target DefenseMoving Target Defense Workshop in conjuncture with CCS

Conference / Medium

ICCV
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)IEEE International Conference on Computer Vision (ICCV) 2021

Conference / Medium

ICCV
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)IEEE International Conference on Computer Vision (ICCV), 2021

Conference / Medium

UNSPECIFIED
Proceedings of the 1st International Workshop on Machine Learning in Software EngineeringInternational Workshop on Machine Learning in Software Engineering in conjunction with ECML PKDD

Conference / Medium

UNSPECIFIED
Proceedings of the 1st International Workshop on Machine Learning in Software EngineeringInternational Workshop on Machine Learning in Software Engineering in conjuncture with ECML PKDD

Conference / Medium

UNSPECIFIED
SampleFix: Learning to Generate Functionally Diverse Fixes1st International Workshop on Machine Learning in Software Engineering

Conference / Medium

IJCAI
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-2130th International Joint Conference on Artificial Intelligence (IJCAI)

Article

EXCLI Journal Eperimental and Clinical Sciences

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