Student Supervision

PhD Students

• 1. Yue Xia, April 2024.

  2. Lorenzo Zaniboni, supervised by Prof. Gerhard Kramer, September 2022.

  3. Maximilian Egger, supervised by Prof. Antonia Wachter-Zeh, January 2022.

  4. Christoph Hofmeister, supervised by Prof. Antonia Wachter-Zeh, October 2021.

  5. Luis Maßny, supervised by Prof. Antonia Wachter-Zeh, October 2021.

Master Theses

• 1. Yue Xia, "Privacy and Security in Federated learning", co-supervised with Christoph Hofmeister and Maximilian Egger, submitted in April 2024.

  2. Afonso De Sá Delgado Neto, "Secure Federated Learning with Gradient Compression", co-supervised with Dr. Mayank Bakshi and Maximilian Egger, submitted in March 2024.

  3. Cem Kaya, "Gradient compression in over-the-air aggregation", co-supervised with Christoph Hofmeister and Maximilian Egger, submitted in March 2024.

  4. Cengizhan Kaya, "The Interplay of Fairness and Privacy in Federated Learning", co-supervised with Christoph Hofmeister and Maximilian Egger, submitted in March 2024.

  5. Tim Janz, "Secure Coding for Distributed Data Storage with Sum-Rank Metric Codes", co-supervised with Prof. Frank Kschischang and Lia Liu, submitted in April 2023 .

  6. Maximilian Egger,  "Exploring while Exploiting Workers for Efficient Distributed Machine Learning", submitted in January 2022.

  7. Ana Lomashvili, "Coding for Distributed Coordinate Gradient Descent", co-supervised with Dr. Serge Kas Hanna, submitted in January 2022.

  8. Christoph Hofmeister, "Private, Secure and Flexible Distributed Machine Learning on the Cloud", co-supervised with Marvin Xhemrishi, submitted in September 2021.

Internships

• 1. Sena Ergisi, ''Coding for Privacy and Security in Federated Learning", co-supervised with Luis Maßny, ongoing.

  2. Sinan Yercan, "Codes Correcting Bursts of Deletions and Insertions", co-supervised with Lorenz Welter, September 2022 - March 2023.

  3. Danial Dehghani. "Coding for DNA storage", co-supervised with Lorenz Welter, September 2022 - February 2023.

  4. Yue Xia, "Secure Federated Learning", co-supervised with two PhD students, August-October 2022.

  5. Abhinav Vaishaya, "Coding for Blockchains", co-supervised with two PhD students, July 2022.

  6. Afonso De Sá Delgado Neto, "Secure Record Linkage via Multi-Party Computation", co-supervised with three PhD students, July-October 2022.

  7. Cengizhan Kaya, "An Implementation Framework for Federated Learning", co-supervised with two PhD students, July-October 2022.

  8. Tim Janz, "Efficient Implementation of Sum-rank Metric Codes", co-supervised with Prof. Frank Kschischang, September 2021 - February 2022.

  9. Anirudh Ramesh, "Distributed Machine Learning on the Cloud", July-December 2021.

  10. Evagoras Stylianou, "Criss-Cross Insertion and Deletion Correcting Codes", co-supervised with Lorenz Welter, August-December 2021.

  11. Leopold Thomas, "Implementation and Comparison of Approximate Gradient Codes", March-December 2021.

  12. Georg Grießing, "Adaptive Compute Strategies for Distributed Learning", April-September 2021.

  13. Ana Lomashvili, "Analysis of Criss-Cross Deletions in Arrays", co-supervised with Lorenz Welter, August-January 2021.

  14. Manav Moodi, "Implementing Different Distributed Gradient Descent Methods on Amazon EC2", May-July 2020.

  15. Sijie Li, "Network Coding with Myopic Adversaries", March-September 2020.

  16. Esben Klarlund, "Search Efficient Blockchain-Based Immutable Logging and Querying", May-September 2018.

  17. Peiwen Tian, "Implementing Staircase Codes on Amazon EC2 clusters" May-September 2016.

Bachelor Thesis

1. Amine Ben Dhiab, "Implementation of Distributed Coordinate Descent", co-supervised with Dr. Serge Kas Hanna, June-October 2022.