My thesis focused on TFHE, a recent fully homomorphic encryption scheme able to compute a bootstrapping in record time. We introduced an optimization framework to set the degrees of freedom inherent to homomorphic computations which gives non-experts the ability to use it (more) easily. We described a plethora of new FHE algorithms which improve significantly the state of the art and limit, (if not remove) existing restrictions. Efficient open source implementations are already accessible in Concrete and in TFHE-rs.

• Specialty: data science
• Machine Learning, Markov chains, estimation theory, Reinforcement learning, data fusion, optimization and multi-agent system, operational research, probability and statistic

As an external candidate: Stochastic processes, diffusion process, functional analysis and Itô calculus

• Theory: mathematics for cryptography and industrial cryptography
• Practice: system security, network security and application security

Major in complex analysis

Undergraduated studies for Mathematics and Physics