I am a postdoc with Yoshua Bengio studying the causal discovery of cell dynamics at Mila in Montreal. This work is in collaboration with Fabian Theis through the newly formed Helmholtz International Lab, a German-Canadian collaboration.
I completed my PhD in the computer science department at Yale University in 2021 where I was advised by Smita Krishnaswamy. My dissertation can be found here. My research interests are in generative modeling, deep learning, and optimal transport. I’m working on applying ideas from generative modeling, causal discovery, optimal transport, and graph signal processing to understand how cells develop and respond to changing conditions.
I grew up in Seattle, Washington, USA, and graduated from Tufts University in 2017 with a BS and MS in computer science. Outside of work, I love sailing and running. I am the 2019 junior North American champion in the 505 class, and I recently ran my first 50 mile race the Vermont 50!
PhD in Computer Science, 2021
MPhil in Computer Science, 2020
MS in Computer Science, 2017
BS in Computer Science, 2017
Trellis is a tree-based earth mover’s distance method for understanding estimating treatment effects from single cell data. In this work we apply it to colorectal cancer PDOS and investigate the chemoprotection induced by cancer-associated fibroblasts.
We present FoldFlow a novel flow matching model for protein design. We present theory and practical tricks for flow models over SE(3)^N. Empirically, we validate these models on protein backbone generation of up to 300 amino acids leading to high-quality designable, diverse, and novel samples.
We present methods for learning simple flows over R^d with optimal transport conditional flow matching (OT-CFM). Training with this objective leads to imprved results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schrodinger bridge inference.
A fast diffusion-based earth mover’s distance