I am a research scientist/postdoc at the Chair of Information Theory and Machine Learning at Technische Universität Dresden where I work at the intersection of information theory and machine learning. I did my Dr.-Ing. (PhD) at Technische Universität Berlin, advised by Gerhard Wunder. My thesis was about deterministic models for capacity approximations in interference networks and physical layer security. I received the M.Sc. degree in electrical engineering from Technische Universität Berlin in 2012 and the B.Sc. degree in electrical engineering from Hochschule Furtwangen University in 2010.

Email / Google Scholar / GitHub / LinkedIn / ORCID

Research

I am generally interested in information theory, security, fairness, machine learning, and statistics. My research spans the following topics:

• Information theory: theoretical limits of data compression and transmission
• High-dimensional statistics: estimation methods and theoretical properties in high-dimensional spaces, particularly for applications in AI
• AI for wireless communication: diffusion models and the interplay between classical codes and neural coding schemes
• Mutual information estimation and its applications in communication systems

Selected Projects and Code

• MinDiffusion: Diffusion-model material for end-to-end channel coding and the ICMLCN tutorial. [slides]

• Wireless Encoding with MI Estimation: Code for neural mutual information estimation applied to channel coding.

• Neural Gaussian Wiretap Channel: Code for deep learning-based coding over the Gaussian wiretap channel.

Recent News

• February 2026. Our collaborative paper “AI/ML-Driven 6G Network Solutions with Energy Efficiency Considerations” appeared in IEEE Access.
• January 2026. Our collaborative paper “6G PHY: Insights From 6G-ANNA Research Initiative” appeared in IEEE Open Journal of the Communications Society.
• May 2025. My paper “MinGRU-Based Encoder for Turbo Autoencoder Frameworks” with Rafael Schaefer appeared at ICMLCN 2025.
• May 2024. My colleague Muah Kim gave a tutorial about diffusion models at ICMLCN 2024 based on our work.
• January 2023. Our paper “Learning End-to-End Channel Coding with Diffusion Models” with Muah Kim and Rafael Schaefer was accepted.

Contact

Email: rick.fritschek at tu-dresden.de, rickfritschek at gmail.com