Tobias Fritz M.Sc.

INF 3 Institut für Technische Informatik
Gebäude Carl-Wery-Str. 22, Zimmer 1614
+49 89 6004-7319

Tobias Fritz M.Sc.


Research Area:

Tobias' current research area is in the domain of graph neural networks. 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.

Conference Papers, Workshops & Other Activities:

Media4Peace Symposium, Berlin (2023)

ECML PKDD, Turin (2023)

ACM Summer School on Data Science, Athens (2023)

MILCOM, Washington (2022)


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,

Seminar/Bachelor/Master Thesis Topics:

Large Language Models for Fake News Detection: Large Language Models are at the heart of tools like ChatGPT, which have shown extrodinary performance in tasks thought to be only performed by humans before. In this thesis, different large language models will be compared with respect to architecture and performance and a small application of these models to the task of fake news detection will be implemented.

Large Language Models for Vulnerability Detection: Large Language Models can be used for code generation and code completion, for example in Microsoft Copilot. In this thesis, different large language models will be compared with respect to architecture and performance. Furthermore, the ability of these models to find security weaknesses in given pieces of code will be evaluated.

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.