Thomas DANIEL is an accomplished AI Research Engineer and Machine Learning Researcher with extensive experience in computational mechanics, numerical simulations, and AI applications in various industries. Notably, Thomas worked at Dassault Systèmes, where techniques in geometric deep learning and object retrieval were applied to LiDAR point clouds for indoor scene reconstruction. At Safran, Thomas developed AI-assisted numerical methods that enhanced simulation speeds of turbine blades significantly. Thomas led R&D initiatives at Kinetix focused on AI-powered 3D animation and human pose estimation. Additionally, Thomas holds advanced degrees in Computational Mechanics and Applied Mathematics from École des Ponts ParisTech and has completed a PhD in Machine Learning from MINES ParisTech. Recognized with the 2022 AMIES PhD Prize for contributions to applied mathematics in industry, Thomas combines a solid academic foundation with practical expertise.
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