Maximilian Egger
Doctoral Researcher
Institute for Communications Engineering
Technical University of Munich
Theresienstr. 90, Building N4, Room 3416
80333 Munich
Phone: +49 89 289 29052
Email: maximilian.egger@tum.de
Recent News:
December 2024: Our papers Self-Duplicating Random Walks for Resilient Decentralized Learning on Graphs and Scalable and Reliable Over-the-Air Federated Edge Learning were presented at GLOBECOM 2024 in Cape Town, South Africa.
Our paper Maximal-Capacity Discrete Memoryless Channel Identification was published in IEEE Transactions on Information Theory, Volume 71, Issue 2.
November 2024: Our paper Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises was presented at Information Theory Workshop (ITW) 2024 in Shenzhen, China.
October 2024: Second-time re-elected as doctoral representative for the Department of Computer Engineering at Technical University of Munich.
Short Biography:
November 2024: Research Stay with Prof. Rüdiger Urbanke at École Polytechnique Fédérale de Lausanne, Switzerland.
February - May 2023: Three-Months Research Stay with Prof. Dr. Deniz Gündüz at Imperial College London, Great Britain.
Since January 2022: Doctoral Researcher at the Institute for Communications Engineering under supervision of Prof. Dr.-Ing. Antonia Wachter-Zeh
February 2022: Master of Science degree in Electrical Engineering and Information Technology from the Technical University of Munich with high distinction (final grade: 1.0)
February 2020: Bachelor of Engineering degree in Electrical Engineering from University of Applied Sciences with high distinction (final grade: 1.0)
January 2019: Successfully completed professional education as electronics technician for industrial systems with 99 of 100 reachable points
September 2015 - February 2020: Dual Studies (bachelors combined with apprenticeship and multiple temporary engineering positions) at Hilti AG, Kaufering, Germany
Research Interests:
Distributed Machine Learning
Efficiency in distributed matrix multiplication and (stochastic) gradient descent
Privacy preserving coded distributed computing and federated learning
Security in decentralized learning
Channel capacity estimation and optimization
Awards:
March 2023: DAAD Scholarship for Research Stay at Imperial College London.
November 2020: VDE Award Bavaria 2020.
September 2019: Best vocational qualification 2019 - Chamber of Industry and Commerce (IHK) of Munich and Upper Bavaria.
March 2019 - December 2021: Scholarship recipient of the "Studienstiftung des deutschen Volkes" (German Academic Scholarship Foundation).
November 2018: Participant at the gP Primus promotion program of the University of Applied Sciences in Augsburg.