Riemannian Metric Learning for Alignment of Spatial Multiomics
ISMB 2026
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.
ISMB 2026
RECOMB 2026
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.