Tobias Andreas Fritz M.Sc.

Tobias Andreas Fritz M.Sc.

 

Research Area:

Tobias' current research area is in the domain of temporal graph neural networks and time series analysis. Here, he is especially interested in applying these for the task of fake news detection in social media networks as well as computer network analysis. Further, he is interested in Large Language models and their application in various domains.

Before starting his doctorate, he worked for one year as a software developer. He received his master's degree in mathematics from the Technical University of Munich and his bachelor's degree in mathematics with a minor in computer science from the Ludwig-Maximilians-Universität. During his studies he worked in research departments at Infineon Technologies AG and Allianz Global Investors GmbH for about 3 years.

Conferences, Workshops & Other Activities:

NOMS, Seoul, South Korea (2024)

Media4Peace Symposium, Berlin, Germany (2023)

ECML PKDD, Turin, Italy (2023)

ACM Summer School on Data Science, Athens, Greece (2023)

MILCOM, Washington, USA (2022)

Publications:

Robin Buchta, Tobias Fritz, Carsten Kleiner, Felix Heine, Gabi Dreo Rodosek. May 2024. One-Class Learning on Temporal Graphs for Attack Detection in Cyber-Physical Systems, Paper accepted for publication @ Network Operations and Management Symposium, Workshop on Analytics for Network and Service Management (NOMS AnNet) 2024.

Tobias Fritz. September 2023. Leveraging tree-structured Graphs in Graph Neural Networks for Fake News Detection, Poster @ European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML  PKDD) 2023

L. Servadei, E. Zennaro, T. Fritz, K. Devarajegowda, W. Ecker, R. Wille. November 2019.  Using Machine Learning for Predicting Area and Firmware Metrics of Hardware Designs from Abstract Specifications. In Microprocessors and Microsystems,  https://doi.org/10.1016/j.micpro.2019.102853

Seminar/Bachelor/Master Thesis Topics:

Temporal Graph Neural Networks: Computer Networks can be modeled as a graph that changes over time. The nodes are the PCs, servers, access points, … and the edges are connections/traffic between them. Graph Neural Networks can be used to detect anomalies in the network. The goal of this work is to classify existing approaches to detect attacks in computer networks and implement one aproach and evaluate its performance.

 

Please approach me if you want to propose your own idea within my area of interest.