I am a senior researcher at
working on machine learning for accelerating computational chemistry.
On the ML side, I develop generative models and architectures that can deal with special data structures and constraints (e.g. functional input, symmetry, etc).
On the application front, I am interested in molecular dynamics simulation, quantum chemistry, and enhanced sampling methods.
If you are interested in collaboration or doing an internship with me, feel free to contact me to have a chat.
I am also a final year PhD candidate at Mila,
advised by Aaron Courville.
My PhD work is on probability flows in deep learning, including flow-based models, latent variable models, and diffusion models.
I have also worked on designing efficient algorithms for approximate inference and Bayesian deep learning.
See my Google Scholar page
for my research.