Raphaël Pestourie, PhD
In my research, I formulate engineering questions as computational optimization problems and develop techniques to find optimal answers with an efficient combination of data and computing resources. To that end, I develop and optimize scientific machine-learning surrogate models and fast approximate PDE models. My research currently has two main thrusts to accelerate simulations and enable inverse design:
Large-scale inverse design in electromagnetism,
Scientific machine learning for global surrogate models and optimization.
I have applied my methodologies to the design of metasurfaces, end-to-end sensors, and thermoelectric material. I am currently working (with projects at various stages of maturity) on the design of a quantum computing device, a green communication device, a biomedical imaging device, and an autonomous pipeline between experimentation and design for molecular discovery. In the future, I plan to create new methodologies and apply them to materials science, such the design of hard and light-weight materials for transportation or ligand discovery, and bioengineering, such as the design of biosensors and drug discovery. Although these applications are very diverse and may seem disconnected, they share similar design challenges and mathematical methodology that my research may impact disruptively.
Keywords: inverse design, scientific machine learning, PDEs, electromagnetism, statistical optics, scientific computing, interpolation, large-scale optimization, Photonics, metasurfaces, end-to-end optimization, AI, active learning, Bayesian statistics, surrogate models.
I earned five masters, a PhD, and am currently pursuing postdoctoral studies:
Diplôme grande école from ESSEC
Ingénieur des Arts et Manufactures specialized in Physics from École Centrale Paris (now CentraleSupelec)
Master of research in Nanosciences from Université Paris Saclay
MBA from ESSEC
AM in Statistics from Harvard University
PhD in Applied Mathematics with a secondary field in Computational Science and Engineering from Harvard John A. Paulson School of Engineering and Applied Sciences
Postdoctoral studies in the Mathematics department at MIT (3 years).
During my PhD, I was an Arthur Sachs Fellow selected by the French Fulbright Commission, and a Jean Gaillard fellow selected by the Board of Directors of the École Centrale des Arts et Manufactures in Paris. In addition, I was awarded membership into the Harvard Graduate School Leadership Institute through the Harvard Kennedy School’s Center for Public Leadership.
I am a strong believer that research should result in innovation and commercialization. I have working experience in a quantitative trading hedge fund and in startups both as an employee and as a founder. I have published many peer-reviewed articles and am a patent inventor.
I am originally from France, and I have studied languages and cultures of other people through pursuing internships and advanced degrees in several countries. Outside work, I play music with/for my bicultural, multilingual family.
Selected videos of invited talks
Invited talk at SciMLCon on March 23rd 2022 Physics-enhanced deep surrogates trained end to end
Invited seminar at IBM on February 24th 2022 Scientific machine learning: from optics to deep surrogates
Invited seminar at MERL on February 8th 2022 Extreme optics design as a large-scale optimization problem