- Doctoral researcher at the Chair of Communications Engineering at TUM since February 2021
- M.Sc. in Communications Engineering at TUM, 2018-2020
- B.Sc. in Electrical Engineering and Information Technology at Università degli Studi di Padova, 2015-2018
Machine Learning for Communications (WS 2122)
Theses in Progress
Directed Information Graphs
Directed Information Graphs (DIGs) provide a graphical and mathematical framework for studying information flow and causal relationships in complex systems. They find applications in various fields such as communication systems, neuroscience, and causal inference.
A DIG is typically represented as a directed graph, where nodes represent random variables or processes, and directed edges indicate the direction of information flow. In other words, the edges show which variables influence or provide information to other variables.
The student's task is to read and understand the theoretical framework, related algorithms and applications.
- Nonlinear Equalization for Optical Communications Based on Entropy-Regularized Mean Square Error. European Conference on Optical Communication (ECOC), 2022 more… BibTeX
- Nonlinear Component Equalization: A Comparison of Deep Neural Networks and Volterra Series. European Conference on Optical Communication (ECOC), 2022 more… BibTeX