Summary
Raphaël Pestourie is an Assistant Professor at Georgia Tech and a computational scientist with a decade of experience translating engineering problems into large-scale optimization and inverse-design solutions. Trained as a PhD in Applied Mathematics at Harvard and seasoned by postdoctoral work at MIT, he builds fast approximate PDE solvers and scientific machine learning models that fuse data and physics for resource-efficient, end-to-end co-design. His research tackles extreme-scale challenges—such as inverse design in electromagnetism with up to a billion parameters—and blends topology/shape optimization with surrogate modeling to accelerate real-world engineering workflows. He has a multidisciplinary background spanning quantum- and nano-scale physics, quantitative research, and teaching, which lets him pivot ideas between theory, computation, and industry. Based in Atlanta, he actively recruits collaborators and students to scale these methods for hardware-software co-design and large-scale optimization. An understated strength is his track record of adapting deep physics intuition into practical computational pipelines usable by practitioners beyond academia.
10 years of coding experience
4 years of employment as a software developer
Master of Research, Nanosciences, Master of Research, Nanosciences at Université Paris-Saclay
Master of Business Administration - MBA, Management, Finance, General, Master of Business Administration - MBA, Management, Finance, General at ESSEC - ESSEC Business School
Master's Degree, Nanoscience, Master's Degree, Nanoscience at Ecole Centrale Paris
Baccalauréat Scientific, Secondary Education and Teaching, A-Levels with very high honors, Baccalauréat Scientific, Secondary Education and Teaching, A-Levels with very high honors at Academy of Versailles
Notre-Dame du Grandchamp
Doctor of Philosophy (PhD), Applied Mathematics, Doctor of Philosophy (PhD), Applied Mathematics at Harvard University
Master Thesis, Physics, Master Thesis, Physics at UC Berkeley
French, English, German, Chinese, Russian