Rawad Bitar

Rawad Bitar is a Research Group Leader at the Institute for Communications Engineering under the TUM School of Computation, Information and Technology. He holds a habilitation from TUM with mentors including Prof. Antonia Wachter-Zeh, Prof. Deniz Gündüz, and Prof. Sidharth Jaggi. His academic journey includes a Ph.D. in ECE from Rutgers University (2020), a master’s degree from the Lebanese University (2014), and an engineering degree from the Lebanese University (2013).

Research Interests:

• Privacy and Security in Distributed Systems
• Byzantine-Resilient Federated Learning
• Private Distributed Computing
• DNA-Based Storage and Computing
• Insertion/Deletion-Correcting Codes
• Network Coding with Myopic Adversaries
• Adaptive Stochastic Gradient Descent

Article Trends: His recent publications focus on federated learning, secure coded computing, and DNA-based storage. Key themes include Byzantine resilience, gradient compression, privacy-preserving algorithms, and code design for synchronization errors. Collaborations span institutions like Stanford, EPFL, and Aalto University, with applications in cloud computing, edge networks, and synthetic biology.

Grants and Awards:

• DFG Grant for "Fundamentals of Coding and Information Theory for Edit Errors" (2025–2028)
• DFG Grant for "Private Secure and Efficient Codes for Distributed Machine Learning" (2023–2026)
• EuroTech Visiting Research Programme (2024, 2023)
• iDASH 2022: Second Place in Track 4: Secure Record Linkage
• Best Poster Award at Princeton Day of Optimization (2018)

Teaching and Supervision: Rawad co-teaches advanced courses at TUM, including Coding for Trustworthy Distributed Learning and Coding Theory for Storage and Networks. He has supervised over 15 Ph.D. and Master's students, including Sena Ergisi, Yue Xia, and Luis Maßny, often in collaboration with leading researchers like Prof. Antonia Wachter-Zeh.

Collaborations and Service: Active in workshops and conferences, Rawad co-organized events such as the 2025 Workshop on Distributed Computing, Optimization and Learning (WDCL) and the 2024 Dagstuhl Seminar on Coding for Synthetic Biology. His collaborators include Prof. Mary Wootters (Stanford) and Dr. Andreas Lenz (formerly TUM).