Assistant Professor in Scientific Machine Learning at the School of Computational Science and Engineering, Georgia Tech (2023-present)
Postdoctoral Associate, Department of Mathematics, Massachusetts Institute of Technology (2020-2023)
PhD in Applied Mathematics, Harvard University
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The goal of my group is to extend the horizon of accurate models for the optimization of engineering solutions. For example, we introduce models where trial and error and heuristics are the state of the art for practitioners. We 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, my group develops fast approximate PDE models and scientific machine learning models that combine AI (Artificial Intelligence) models and scientific models, end to end. These new models enable the resource-efficient and large-scale optimization of engineering solutions in the following areas:
Hybrid models that bridge theory-driven and data-driven models for model discovery in scienceunder fixed computing budget and scarce data,
ML-enhancement of the large-scale optimization of scalar functions via efficient representations of the feasible space,
Co-design of physical hardware and software in electromagnetism (aka End-to-end optimization) .
My graduate 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. 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. I am also a regular reviewer for these journals and for (scientific) machine learning conferences.
Keywords: ML-enhanced simulation, ML-enhanced optimization, inverse design, scientific machine learning, model discovery, large-scale optimization, fast-approximate solvers, metasurfaces, electromagnetism, metamaterials, end-to-end optimization, surrogate models.
Links
Google Scholar (link)
Twitter @rpestouriePhD (link)
My group is hiring! If you are interested in collaborating with me, please don't hesitate to reach out. I strongly support the Georgia Tech value that "We thrive on diversity."
I am very fortunate to advise the following students:
PhD & MSc student in my group at Georgia Tech
Xian Mae Hadia (PhD candidate in CSE)
Sophie Bekerman (PhD candidate in ML)
Max Huang (PhD candidate in ML)
Antonio Varagnolo (MSc Candidate)
PhD student that I mentor scientifically
Mahmoodreza Marzban (GT ECE, Prof Ali Adibi group)
Lorenzo Xavier van Muñoz (MIT Physics, NSF Graduate Research Fellow and a Dean of Science Fellow, former Mellon Mays Undergraduate Fellow at Caltech)
Dr. Wenchao Ma (MIT Chemistry, PhD student)
Past students that I advised or mentored
Dr. Joeri Lenearts (Free University of Brussels–VUB)
Dr. Constant Bourdeloux (ESPCI)
Sophie Fisher (PhD at MIT EECS)
Jerrell Cockerham from Colorado College through MIT Summer Research Program 2020 (now PhD candidate in Mathematics at Rice university)
Current collaborators: Prof. Steven G. Johnson (MIT), Dr. Giuseppe Romano (MIT), Dr. Chris Rackauckas (MIT), Dr. Payel Das (IBM), Dr. Youssef Mroueh (IBM), Prof. Federico Capasso (Harvard), Prof. Arka Majumdar (UW Seattle), Prof. Zin Lin (VT), Prof. Mathias Fink (ESPCI), Prof. Lu Lu (Yale), Prof. Boubacar Kanté (UC Berkeley), Dr. Thibault Laplace (LBNL), Prof. Holger Schmidt (UCSC), Prof. Constantine Sideris (USC), Prof. Sean Molesky (Polytechnique Montreal), Prof. Carlos Pérez Arancibia (Univ. Twente)
Fall 2023, Fall 2024: CSE 8803 "Special Topics in Scientific Machine Learning" (I created this new course at Georgia Tech)
Fall 2024: CSE 8801 "Linear algebra, Probability, and Statistics" (I created this new refresher course at Georgia Tech)
Primary PI for the Workshop on Foundation of scientific AI for optimization of complex systems (GT, Jan. 16th, 2024)
This one-day workshop will convene experts in computational mathematics, statistics, data science, and application domain scientists across science and engineering to address the foundational topics of scientific AI for the prediction and optimization of complex systems (such as electromagnetic, corrosion science). The agenda emphasizes transcending conventional forward simulations and predictions to realize the optimization of complex systems at a large scale by harnessing the power of scientific AI.
Primary PI for the Student-focused Scientific Machine Learning Symposia (virtual, November 2023, November 2024)
I co-organized a mini-symposium with a Georgia Tech graduate students about applications of scientific machine learning in engineering: weather prediction, neutron star simulation, physics-informed robots, efficient DFT methods, drug discovery, epidemiology, heart sciences, permafrost study, aeroacoustics, to name a few. All the invited speakers and contributed talks were given by graduate students. This event truly showcased the Georgia Tech value that "We thrive on diversity".
Co-convener for SIAM MDS MS on Data-Driven Scientific Machine Learning for the Optimization of Complex Systems (Atlanta, October 21-25, 2024)
Reviewer for: NeurIPS, ICLR, AISTATS; Nature Communications, Scientific Reports, Advanced Materials, Advanced Science, Advanced Optical Materials, Optics Express, Optics Letter, Applied Optics, ACS Photonics, Nanophotonics, IEEE Access, IEEE Transactions on Antennas and Propagation.
Before joining the faculty at Georgia Tech, I earned five masters, a PhD, and completed my 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.
PhD thesis: "Assume Your Neighbor is Your Equal: Inverse Design in Nanophotonics" Harvard University Library website.
Peer-reviewed journal articles: (* these authors contributed equally)
[17] W. Ma, R. Pestourie, et al. "Multiplicative resonant enhancement of chemical detection" Physical Review Applied, 2024 [DOI]
[16] R. Pestourie, et al. "Physics-enhanced deep surrogates for partial differential equations," Nature Machine Intelligence, 2023 [DOI] | Press: IEEE Spectrum, MIT News
[15] R. Pestourie, W. Yao, B. Kanté, and S.G. Johnson "Efficient Inverse Design of Large-Area Metasurfaces for Incoherent Light" ACS Photonics, 2022 [DOI]
[14] S. Fisher, R. Pestourie, and S.G. Johnson "Efficient perturbative framework for coupling of radiative and guided modes in nearly periodic surfaces," Physical Review A, 2022 [DOI] | Editors' Suggestion
[13] C. Munley, W. Ma, J. E. Fröch, Q. A. A. Tanguy, E. Bayati, K. F. Böhringer, Z. Lin, R. Pestourie, S.G. Johnson, and A. Majumdar "Inverse-Designed Meta-Optics with Spectral-Spatial Engineered Response to Mimic Color Perception," Advanced Optical Materials, 2022 [DOI]
[12] L. Lu, R. Pestourie, et al. "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 [DOI]
[11] Z. Li, R. Pestourie, et al. "Empowering Metasurfaces with Inverse Design: Principles and Applications," ACS Photonics, 2022 [DOI]
[10] Z. Li*, R. Pestourie*, et al. "Inverse design enables large-scale high-performance meta-optics reshaping virtual reality," Nature Communications, 2022 [DOI] | Press: Nature blog, Harvard SEAS News
[9] Z. Lin, R. Pestourie, et al. "End-to-end metasurface inverse design for single-shot multi-channel imaging," Optics Express, 2022 [DOI]
[8] E. Bayati*, R. Pestourie*, et al. "Inverse Designed Extended Depth of Focus Meta-Optics for Broadband Imaging in the Visible," Nanophotonics, 2021 [DOI]
[7] L. Lu, R. Pestourie, et al. "Physics-informed neural networks with hard constraints for inverse design," SIAM Journal on Scientific Computing, 2021 [DOI]
[6] Z. Lin, C. Roques-Carmes, R. Pestourie, et al.. "End-to-end nanophotonic inverse design for imaging and polarimetry," Nanophotonics, 2020 [DOI]
[5] R. Pestourie et al. "Active learning of deep surrogates for PDEs: Application to metasurface design," npj Computational Materials, 2020 [DOI] | Press: IBM blog | Code: [UQ360]
[4] E. Bayati*, R. Pestourie*, et al., “Inverse designed metalenses with extended depth of focus,” ACS Photonics, 2020 [DOI]
[3] Z. Lin, V. Liu, R. Pestourie, et al., “Topology optimization of freeform large-area metasurfaces,” Optics Express, 2019 [DOI]
[2] R. Pestourie, et al., “Inverse design of large-area metasurfaces,” Optics Express, 2018 [DOI]| Press: MIT News
[1] C. Pérez-Arancibia, R. Pestourie, and S.G. Johnson, “Sideways adiabaticity: Beyond ray optics for slowly varying metasurfaces,” Optics Express, 2018 [DOI]
Preprints under review:
[2] W. Ma, R. Pestourie, SG Johnson "Time-reversal-symmetry bounds on electromagnetic fields", 2025 [arXiv]
[1] R. Pestourie "Fast approximate solvers for metamaterials design in electromagnetism", 2025 [arXiv]
Patents:
[2] R. Pestourie, Y. Mroueh, C.V. Rackauckas, P. Das, and S.G. Johnson, 2022. Physics-enhanced deep surrogate. US Patent 17/982996
[1] R. Pestourie, Y. Mroueh, P. Das, S. G. Johnson "Active learning of data models for scaled optimization" US Patent 17/405318
Peer-reviewed conference proceedings:
[8] R. Pestourie, et al. "Towards optimal spatiotemporal wavefront shaping for the cocktail party problem with inverse design of an acoustic reconfigurable metasurface in disordered media" Metamaterials 2024 [arXiv]
[7] J. Budhu, R. Pestourie "A New Homogenization-Free Boundary Condition Towards Aperiodic Metasurface Design Using Full-Wave Surrogate Models of Printed Circuits," Metamaterials 2024 [arXiv]
[6] R. Pestourie, Z. Li, Y. Mroueh, P. Das, F. Capasso, and S. G. Johnson "Surrogate models and machine learning for large-scale meta-optics inverse design," 2022 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO) (NEMO 2022) (July 2022)
[5] R. Pestourie, Y. Mroueh, C. V. Rackauckas, P. Das, S. G. Johnson "Data-Efficient Training with Physics-Enhanced Deep Surrogates," AAAI 2022 ADAM workshop (March 2022). [PDF]
[4] R. Pestourie, Z. Li, E. Bayati, J.-S. Park, Y.-W. Huang, S. Colburn, Z. Lin, A. Majumdar, F. Capasso, and S.G. Johnson "Extreme optics: inverse design and experimental realizations of ultra-large-area complex meta-optics," 15th International Congress on Artificial Materials for Novel Wave Phenomena - Metamaterials 2021
[3] R. Pestourie and S. G. Johnson "Opening the black box for data efficiency and inverse design in photonics," International Society for Optics and Photonics - Metamaterials, Metadevices, and Metasystems 2021
[2] R. Pestourie and S. G. Johnson "Complex design of metasurfaces," OSA Optical Design and Fabrication 2021 (Flat Optics, Freeform, IODC, OFT) (June 2021)
[1] R. Pestourie, G. Chomette, Y. Mroueh, P. Das, R. Radovitzky, and S. G. Johnson, "Active learning of deep surrogates for PDEs," ICLR 2021 SimDL Workshop (May 2021). [PDF]
Preprints:
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Invited talk at Crunch seminar at Brown on May 24th 2024 Physics-enhanced deep surrogated for partial differential equations
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
Invited seminar at IEEE Photonics Society (Boston) on May 13th 2021 Inverse design of complex meta-optics
Please follow my Twitter account for the latest news: @rpestouriePhD
November 2021: I was invited to present at OPTICSMEET2021 on Saturday November 6th. My virtual presentation will be about a new paradigm for surrogate-based inverse design in nanophotonics, leveraging AI to go beyond the locally periodic approximation.
November 2021: Surrogate-based inverse design meets end-to-end optimization. We discovered spontaneous multiplexing when combining large-scale metasurface design and Tikhonov regularization for multi-channel imaging (spectral, polarization and depth), end-to-end. Check it out on arXiv.
September 2021: Excited to talk about "Extreme Optics: Inverse Design and Experimental Realizations of Ultra-Large-Area Complex Meta-Optics" at the 15th International Congress on Artificial Materials for Novel Wave Phenomena - Metamaterials 2021
September 2021: Our paper Inverse Designed Extended Depth of Focus Meta-Optics for Broadband Imaging in the Visible was accepted in the journal Nanophotonics.
August 2021: Our paper "Physics-informed neural networks with hard constraints for inverse design" was accepted in SIAM Journal on Scientific Computing.
August 2021: Excited to talk at SPIE Optics and Photonics in the session on Deep Learning in Photonics in San Diego, CA!
July 2021: I just pushed the supporting code of my active learning article in npj Computational Materials. It is part of the IBM open source project on uncertainty quantification called UQ360.
June 2021: Excited to talk at the OSA Optical Design and Fabrication Congress about complex design of metasurfaces.
May 2021: Our work on surrogate models for PDEs was features on IBM blog AI boosts the discovery of metamaterials vital for next-gen gadgets.
May 2021: I will be presenting on "Inverse Design of Complex Meta-Optics" on May 13th at the Boston Chapter of the IEEE Photonic Society. Thank you for the invitation!
May 2021: We put on arXiv our latest work on lenses with Extended Depth of Field. Check it out! Inverse Designed Extended Depth of Focus Meta-Optics for Broadband Imaging in the Visible.
April 2021: We put on arXiv the fruit of a multiple year collaboration culminating in the larger metasurface in the visible to date (cm diameter)! Check it out Inverse design enables large-scale high-performance meta-optics reshaping virtual reality.
April 2021: Our paper "Active learning of deep surrogates for PDEs", where we extend our work previous in active learning to mechanical elasticity equations, was accepted at ICLR 2021 Workshop on Deep Learning for Simulation! I am looking forward to sharing this work with the community on May 7th!
March 2021: Excited to give a seminar "Efficient inverse design for extreme applications in optics" in the Instituto de Ingeniería Matemática y Computacional at Pontificia Universidad Católica de Chile!
February 2021: Our paper "Physics-informed neural networks (PINN) with hard constraints for inverse design" is now available on arXiv. It presents PINN used in inverse design, especially enforcing the PDE constraint via an augmented Lagrangian method. The advantage of this approach is that the resulting designs are smoother.
December 2020: Our paper "End-to-end nanophotonic inverse design for imaging and polarimetry." is available ahead of print in Nanophotonics .
December 2020: Excited to have been invited to present my research "Inverse design and deep learning for optical metasurfaces" for the groups of Prof. Boubacar Kanté and Prof. Eli Yablonovitch at UC Berkeley.
October 2020: npj Computation Materials published my collaboration with MIT-IBM Watson AI lab Active learning of deep surrogates for PDEs: application to metasurface design on October 29, 2020.
October 2020: I presented the poster "Active learning of deep surrogates for PDEs: Application to metasurface design" at the AI for Materials: From Discovery to Production symposium organised by the New York Academy of Sciences, on October 6, 2020.
August 2020: I just put on arXiv this fantastic work on active learning for PDE surrogate models done in collaboration with MIT-IBM lab. Using our active-learning algorithm, we can find the training points that make the biggest difference with respect to model accuracy improvement, thus reducing the need for data by more than an order of magnitude! The surrogate model is 100x faster than solving the PDE directly. Active learning of deep surrogates for PDEs: Application to metasurface design
June 2020: We just pushed an exciting ground-breaking article on arXiv about new usage of large-scale optimization for inverse design in nanophotonics "End-to-End Inverse Design for Inverse Scattering via Freeform Metastructures"
March 2020: ACS Photonics published my collaboration with Elyas Bayati and Arka Majumdar from UW of an inverse-designed lens with extended depth of field in 2D. Inverse designed metalenses with extended depth of focus
March 2020: L'Essentiel du Sup published an interview of me about the impact that multidisciplinarity has played in my student career as a dual degree student at ESSEC and École Centrale Paris (CentraleSupelec). Oser l'hybridation, de la théorie à la pratique
February 2020: I published a repository called fdfd_local_field on GitHub with julia code for embarassingly parallel simulations of Maxwell's equation in two dimensions. Github/rpestourie
January 2020: the 3rd Physics Informed Machine Learning Workshop in Santa Fe accepted my abstract "Active neural networks for electromagnetic surrogate models" about my current collaborative work with IBM Research. PIML 2020
December 2019: I defended my PhD in Applied Mathematics from Harvard John A. Paulson School of Engineering and Applied Sciences: "Assume Your Neighbor is Your Equal: Inverse Design in Nanophotonics" (available at Harvard University Library systems).
October 2019: Elyas Bayati and I pushed the first experimental application designed by my optimization framework-–a 2D lens with extended depth of field on arXiv. Inverse designed metalenses with extended depth of focus
May 2019: I presented an extension of my large-scale optimization framework to three dimensional applications at 2019 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization. NEMO 2019
May 2019: I was interviewed by Jennifer Chu from MIT News Office to vulgarize my research. Mathematical technique quickly tunes next-generation lenses
May 2019: Optics Express published the extension of my optimization framework to topology optimization. Topology optimization of freeform large-area metasurfaces
March 2019: I defended my PhD secondary field in Computational Science and Engineering: "Hybrid Maxwell’s equations solver and inverse design tool for metasurfaces".
January 2019: I was invited to present my paper "Inverse design of large-area metasurfaces" at the Workshop on Numerical Analysis of Partial Differential Equations in Concepción, Chile. WONAPDE 2019
December 2018: Optics Express published my seminal paper about large-scale optimization of metasurfaces based on the local periodic approximation. Inverse design of large-area metasurfaces
November 2018: Optics Express published a study that my colleague Carlos Pérez-Arancibia conducted with me and Prof Steven G. Johnson about a locally periodic approximation for continuous surfaces. Sideways adiabaticity: beyond ray optics for slowly varying metasurfaces