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TERATEC 2026 Forum The essential meeting place for all digital players Simulation . HPC/HPDA . Artificial Intelligence . Quantum Computing

Tuesday October 20 Workshop 03 - 4:00 pm to 6:00 pm

Introduction
By Mathilde Mougeot, Professeure des Universités, ensIIE & ENS Paris-Saclay and Vivien Clauzon, Principal numerical scientist HPC, Michelin

Biography: Mathilde Mougeot is full professor at ensIIE, specialized in data science. She conducts her research at the Borelli Center at ENS Paris-Saclay. Driven by research topics arising from collaborations with industry, she has developed numerous research projects in partnership with industry. Since 2018, she has held the “Industrial Data and Machine Learning” industry partnership chair. She focuses on issues related to predictive modeling in a wide variety of contexts, such as high-dimensional data, model aggregation, and—in the context of data scarcity—transfer learning and hybrid models. Mathilde Mougeot is also Deputy Director of the Jacques Hadamard Mathematics Foundation and Associate Director of the Doctoral School of Mathematics at Paris-Saclay University. There, she facilitates and builds relationships to foster exchanges between students and the socio-economic community.

Biography: Vivien Clauzon is an HPC engineer who enjoys pushing both machines and ideas to their limits. His work sits at the intersection of physics, numerical simulation, and high-performance software engineering. He is currently a Principal Engineer in High Performance Computing and Product Owner of the “HPC eXperience” platform, designed to orchestrate computing workloads for Michelin R&D. Prior to that, he developed and evolved industrial numerical solvers at Michelin, covering topics such as noise, comfort, and fluid dynamics. Outside of industrial simulation (or sometimes alongside it), he created Minic, a chess engine that became well known in the chess programming community. His playground is scientific computing. His focus: writing code that is fast, scalable, and physically meaningful. The result is a career spent between GPUs, supercomputers, numerical schemes, and distributed systems, always chasing performance without losing correctness.

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