11/8/2023 0 Comments Harvard seas meta materials"End-to-end metasurface inverse design for single-shot multi-channel imaging," Optics Express, 2022 "Inverse design enables large-scale high-performance meta-optics reshaping virtual reality," Nat ure Comm unications, 2022 | Press: Nature blo g, Harvard SEAS News "Empowering Metasurfaces with Inverse Design: Principles and Applications," ACS Photonics, 2022 "Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport," Physical Review Research, 2022 Majumdar "Inverse-Designed Meta-Optics with Spectral-Spatial Engineered Response to Mimic Color Perception," Advanced Optical Materials, 2022 Johnson "Efficient perturbative framework for coupling of radiative and guided modes in nearly periodic surfaces," Physical Review A, 2022 Johnson "Efficient Inverse Design of Large-Area Metasurfaces for Incoherent Light" ACS Photonics, 2022 Peer-reviewed journal articles: (* these authors contributed equally) PhD thesis: "Assume Your Neighbor is Your Equal: Inverse Design in Nanophotonics" Harvard University Library website. Outside work, I play music with/for my bicultural, multilingual family. I am originally from France, and I have studied languages and cultures of other people through pursuing internships and advanced degrees in several countries. I have published many peer- reviewed articles and am a patent inventor. I have working experience in a quantitative trading hedge fund and in startups both as an employee and as a founder. ![]() I am a strong believer that research should result in innovation and commercialization. ![]() In addition, I was awarded membership into the Harvard Graduate School Leadership Institute through the Harvard Kennedy School’s Center for Public Leadership. Postdoctoral studies in the Mathematics department at MIT (3 years).ĭuring 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. Paulson School of Engineering and Applied Sciences PhD in Applied Mathematics with a secondary field in Computational Science and Engineering from Harvard John A. Master of research in Nanosciences from Université Paris Saclay Ingénieur des Arts et Manufactures specialized in Physics from École Centrale Paris (now CentraleSupelec) Keywords: inverse design, artificial intelligence, scientific machine learning, PDEs, electromagnetism, statistical optics, scientific computing, interpolation, large-scale optimization, Photonics, metasurfaces, end - to - end optimization, AI, active learning, Bayes ian statistics, surrogate models.īefore joining the faculty at Georgia Tech, I earned five masters, a PhD, and completed my postdoctoral studies: I am also a regular reviewer for these journals and for (scientific) machine learning conferences. I have published in journals like Nature Communications, npj Computational Materials, the SIAM Journal on Scientific Computing, Optics Express, ACS Photonics, Nanophotonics, Advanced Optical Materials, Physical Review Research, Physical Review A, to name a few. My PhD and postdoctoral research have been generously supported by MIT-IBM Watson AI Lab, IBM, the Simons Foundation, DARPA, the Army Research Office, arpa-e, the Institute for Soldier Nanotechnologies, and the French Fulbright Commission. My group is hiring! If you are interested in collaborating with me, please don't hesitate to reach out. ![]() Large-scale inverse design in electromagnetism (up to one billion design parameters),Ĭo-design of physical hardware and software (aka End-to-end optimization),ĪI-enhanced optimization methodologies via surrogate models, and via topology or shape optimization. These new models enable the ressource-efficient and large-scale optimization of engineering solutions in the following areas: To that end, my group develops fast approximate PDE models and scientific machine learning models that combine AI models and scientific models, end to end. We formulate engineering questions as computational optimization problems and develop techniques to find optimal answers with an efficient combination of data and computing resources. For example, we introduce models where trial and error and heuristics are the state of the art for practitioners. The goal of my group is to extend the horizon of accurate models for the optimization of engineering solutions. Welcome where Artificial Intelligence (AI) meets scientific computing for engineering applications!
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |