BioB.S. Northwestern University, 2015Ph.D. Rensselaer Polytechnic Institute, 2020Post-Doc Massachusetts Institute of Technology, 2020-2022
"We combine molecular simulations and artificial intelligence to design new molecules and materials."
Dr. Bilodeau received her B.S. and M.S. from Northwestern University and her Ph.D. from Rensselaer Polytechnic Institute, both in Chemical and Biological Engineering. During her Ph.D., she received the Lawrence Livermore Advanced Simulations and Computation Graduate Fellowship, through which she carried out research at Lawrence Livermore National Laboratory. Dr. Bilodeau is currently a postdoctoral researcher studying machine learning for molecular design in Klavs Jensen’s group in the Chemical Engineering department at MIT. She has joined the University of Virginia as an assistant professor in Chemical Engineering and will begin her position in January 2023.
Molecular simulations are powerful tools that can predict important physical properties while simultaneously yielding a full molecular picture of the system. In the past decade, the advent of GPU computing has resulted in dramatic improvements in the computational speed of molecular simulations, making it possible to study larger, more complex systems. At the same time, the field of deep learning has experienced a renaissance, with neural networks being successfully employed for reading text, classifying images, and even folding proteins. This convergence of increased hardware and algorithmic capabilities has set the stage for combining the wealth of data arising from rapid molecular simulations with the deep learning tools to mine this data.
In the Bilodeau group, we explore the intersection between molecular simulations, statistical physics, and artificial intelligence to develop tools to discover and design of new molecules, surfaces, and proteins with optimized properties. Our core expertise lies in molecular dynamics simulations of soft matter systems and artificial intelligence for molecular property prediction and generation. This interdisciplinary toolset allows us to solve important problems in applications ranging from designing biotherapeutics to developing novel separation materials.