Raphaël Pestourie, MBA, PhD
I am a Postdoctoral Associate of Prof. Steven G. Johnson's group in the Mathematics Department of the Massachusetts Institute of Technology (MIT) and my research boils down to one word: HEDGing.
Over centuries, humans have been great at developing informative abstract knowledge using the scientific method. Unfortunately, when it comes to using that knowledge on specific engineering problems by formulating intuitions or educated guesses, the number of parameters and goals that our working memory can take into account simultaneously is limited to about four... (Cowan (2010))
Enter HEDGing: High-dimensional Educated Digital Guessing! HEDGing is a computing paradigm that I developed over the past decade. My research scales the number of parameters and goals to tackle problems with millions to billions of independent components, by teaching knowledge to computer programs and using their educated digital guesses.
For example, in artificial intelligence, I taught physics and uncertainty quantification to machine learning algorithms so that their predictions not only benefit from existing data, but also from existing human knowledge. Applications include optics, thermo-electrics, mechanics, and fluidics.
In optics in particular, I incorporated Maxwell's equations and relevant field knowledge such as common approximations into a computer program, and in return, the program could design billions of parameters simultaneously taking into account multiple complex goals.
HEDGing is a very exciting computing paradigm which takes advantage of the high-dimensional and parallel properties of computer memory and human knowledge, developed over centuries of scientific method, to achieve the best of both worlds. I am now looking into expanding it to other areas such as green technologies and bio-medical applications.
HEDGing is at the interface between numerical methods, (Bayesian and scientific) machine learning techniques, and end-to-end large-scale optimization. My work is multidisciplinary and experiment-driven on a wide range of applications. I thrive working on collaborative, interdisciplinary problems where research outcomes are larger than the mere sum of the contributions from each discipline. My philosophy is to develop tools through impactful applications rather than focusing on methods development alone.
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
Like my scientific ML models, I am highly educated. I earned five masters, a PhD, and a postdoc:
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 hedge fund (a not so different kind of hedging) and in startups both as an employee and as a founder. I have published many peer-reviewed articles (my Google Scholar page) and am a patent inventor.
I come from France and am an advocate of bilingualism. I love learning languages and have lived in three different continents. Outside work, I play music with/for my kids, and build stuff digitally or in real-life.
Contact me at rpestour ατ mit δøτ edu