Senior researcher and lecturer doing a habilitation with Prof. Dr.-Ing Antonia Wachter-Zeh. I obtained a PhD from the ECE department of Rutgers University in January 2020. During my PhD, I held short term visiting positions at Aalto, Technical University of Berlin and the Chinese University of Hong Kong. In addition, I spent three years as PhD candidate at the ECE department of Illinois Institute of Technology (IIT). From August 2014 till January 2020, I was a member of the CSI lab supervised by Prof. Salim El Rouayheb.
In terms of previous studies, I received a masters degree in Information and Communication from the Lebanese University after doing the thesis at IIT in 2014. I graduated as a Computer and Communication Engineer from the Lebanese University in 2013 after doing the engineering senior project at Center of Nuclear Science and Science of Matter (CSNSM) in Paris, France and an internship at procsim-consulting company in EPFL, Lausanne, Switzerland, in 2012.
Research Interests
My research interest is centered around privacy, scalability, security and reliability of distributed systems. The applications may differ but the goal remains the same: study theoretical and fundamental limits of innovative storage and computing systems and design codes achieving those limits. My current work is motivated by the following research directions.
Federated learning
Private and secure distributed computing
Distributed storage
DNA-based storage systems
Private and secure network coding.
Awards
DFG (German Research Foundation) grant for a temporary position for a Principal Investigator "Private Secure and Efficient Codes for Distributed Machine Learning". (2023 -- 2026)
Best Poster Award on Optimization and Machine Learning in Princeton Day of Optimization, Princeton, New Jersey. (September 2018)
Service
Workshop co-Organizer
Munich Workshop on Coding and Cryptography 2022
Guest Editor
Frontier in Communications and Networks
Technical Program Committee (TPC) member
Conference on Information-Theoretic Cryptography (ITC) 2023
IEEE International Workshop on Information Theory (ITW) 2023
We consider the problem of running a distributed machine learning algorithm on the cloud. This imposes several challenges. In particular, cloud instances may have different performances/speeds. To fully leverage the performance of the instances, we want to characterize their speed and potentially use the fastest ones. To explore the speed of the instances while exploiting them (assigning computational tasks), we use the theory of multi-armed bandits (MABs).
The goal of the research intership is to start by implementing existing theoretical algorithms [1] and possibly adapting them based on the experimental observations.
[1] M. Egger, R. Bitar, A. Wachter-Zeh and D. Gündüz, Efficient Distributed Machine Learning via Combinatorial Multi-Armed Bandits, submitted to IEEE Journal on Selected Areas in Communications (JSAC), 2022.
In federated learning, a machine learning model shall be trained on private user data with the help of a central server, the so-called federator. This setting differs from other machine learning settings in that the user data shall not be shared with the federator for privacy reasons and/or to decrease the communication load of the system.
Even though only intermediate results are shared, extra care is necessary to guarantee data privacy. An additional challenge arises if the system includes malicious users that breach protocol and send corrupt computation results.
The goal of this work is to design, implement and analyze coding- and information-theoretic solutions for privacy and security in federated learning.
