Hierarchical Refinement: Optimal Transport to Infinity and Beyond
ICML 2025 (Oral)
PhD Student, Computer Science · Princeton University
ph3641 [at] princeton [dot] edu · Office: 35 Olden Street
My interests are primarily in optimal transport, including algorithmic approaches for discrete optimal transport (algorithms for primal and low-rank optimal transport), the theory of optimal transport based continuous optimization (the JKO Scheme and Wasserstein gradient flow), and computational approaches for optimization in Wasserstein space (flow-based models, score-matching). I also adapt these techniques for quantitative and computational biology, where I develop methods to infer large-scale maps and developmental trajectories in temporal single-cell and spatial transcriptomics with the goal of understanding the dynamics of cell differentiation and growth in space and time.
ICML 2025 (Oral)
RECOMB 2025
NeurIPS 2024
Cell Systems 2025 · RECOMB 2024
ASHG 2022 (Poster)
A selection of recent talks. Slides are linked when available.
As an undergraduate, I worked at Foundation Medicine, supervised by Justin Newberg, Garrett Frampton, and Megan Montesion. I contributed to two projects accepted to AACR 2020:
I was previously a teaching assistant for Organic Chemistry at Columbia University with Professor Talha Siddiqui. I also teach Computer Science at the King Summer Institute, where I've taught an introductory Java course.
Outside of my academic interests, I am an avid hiker and an amateur birder, botanist, and photographer.