Inverse design in nanophotonics, efficient approximate solvers in optics, deep surrogate for PDES.
Physical Applied Mathematics | Computational Science & Numerical Analysis
Originally from France, Raphaël earned a dual degree between ESSEC Business School and École Centrale Paris. He then earned a Master’s of research in Nanosciences at Université Paris Saclay. He conducted his master’s research on metamaterials at UC Berkeley in the lab of Prof. Xiang Zhang. Then, he earned a PhD in applied mathematics from Harvard University. His PhD research was about inverse design for metasurfaces, and he was co-advised between Federico Capasso and Steven Johnson at MIT. Since February 2020, he is a postdoctoral associate at MIT Mathematics. His current research leverages machine learning for modeling and inverse design for PDE-constrained problems. Raphaël is also currently a resident affiliate of Quincy House at Harvard College, where he lives among and advises undergraduate students.