Teaching:

• Object Oriented Programming, Java
• Computer Security, Cryptography
• Discrete Mathematics
• Advanced Programming, Python

Research Topics:

• Fully Homomorphic Encryption (FHE),
• Verifiable Computing,
• Post-Quantum Cryptography,
• Quantum Cryptography,
• Security of Machine Learning,
• Error correcting code

Selected publications:

• Madi, A., Sirdey, R. and Stan, O., 2020. Computing neural networks with homomorphic encryption and verifiable computing. In Applied Cryptography and Network Security Workshops: ACNS 2020 Satellite Workshops, AIBlock, AIHWS, AIoTS, Cloud S&P, SCI, SecMT, and SiMLA, Rome, Italy, October 19–22, 2020, Proceedings 18(pp. 295-317). Springer International Publishing.
• Madi, A., Stan, O., Mayoue, A., Grivet-Sébert, A., Gouy-Pailler, C. and Sirdey, R., 2021, May. A secure federated learning framework using homomorphic encryption and verifiable computing. In 2021 Reconciling Data Analytics, Automation, Privacy, and Security: A Big Data Challenge (RDAAPS)(pp. 1-8). IEEE.
• Abbass Madi, O.S., Sirdey, R. and Gouy-Pailler, C., 2022. SecTL: Secure and Verifiable Transfer Learning-based inference.

Biography

Abbass Madi received a Mathematics Diploma from the Lebanese University, Beirut, in 2015, the M.S. degree in Mathematics of information – Cryptography from the Rennes 1 university, Rennes, France, in 2018, and the Ph.D. degree in Computer Science Mathematics from Paris-Saclay university and French Alternative Energies and Atomic Energy Commission (CEA), Paris, in 2022. Currently, he is an assistant professor at the University of Sciences and Arts in Lebanon (USAL). His research interests are in Fully Homomorphic Encryption (FHE), Verifiable Computing (VC), Adaptation of the usage of VC and FHE in different Machine Learning models, Post-quantum cryptography, and multi-linear maps for cryptographical uses