ABOUT

I am a research scientist at Meta FAIR. I work at the intersection of machine learning, computational chemistry, and materials science. My research aims to leverage the multi-scale and multi-modal nature of physical systems, as well as their symmetry properties, to develop powerful and scalable learning models and algorithms. My research combines generative modeling of atomistic structures and learned simulation techniques to accelerate the design of new materials and molecules with desired properties. I completed my PhD at MIT CSAIL, advised by Tommi Jaakkola.

NEWS

• 24/09 A recipe for charge density prediction, which proposes a state of the art charge density prediction model, is accepted to NeurIPS 2024. Code is available.
• 24/06 I graduated from MIT and joined the FAIR Chemistry team at Meta as a research scientist, continuing research on machine learning for chemistry and materials science.