- Hierarchical Refinement: Optimal Transport to Infinity and Beyond, ICML 2025 (Oral)
- Learning Latent Trajectories in Developmental Time Series with Hidden-Markov Optimal Transport, RECOMB 2025
- Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling, NeurIPS 2024
- DeST-OT: Alignment of Spatiotemporal Transcriptomics Data, Cell Systems 2025 and RECOMB 2024
- Pooled RNA-IP approach to investigate variant effects on RBP binding and splicing, ASHG 2022
Preprints:
- System Identification for Continuous-time Linear Dynamical Systems, ArXiV
Research Experience
Worked at Foundation Medicine as an undergraduate, supervised by Justin Newberg, Garrett Frampton, and Megan Montesion. Contributed to two projects accepted to AACR 2020. Previously a teaching assistant for Organic Chemistry at Columbia with Professor Talha Siddiqui. Also teaches Computer Science at the King Summer Institute, where he has taught an introductory Java course.
Education
PhD in Computer Science at Princeton University, advised by Ben Raphael; Bachelor's degree in Computer Science from Columbia University with a concentration in Chemistry. Worked under the guidance of David Knowles at Columbia University and the New York Genome Center.
Background
Research interests lie at the intersection of pure machine learning and computational biology. Specifically interested in the computational and mathematical aspects of optimal transport, including low-rank and neural optimal transport, as well as flow-based generative models. Focus is on developing and adapting these tools to quantitatively infer developmental trajectories in single-cell and spatial transcriptomics, and to understand the dynamics of cell differentiation in space and time.
Miscellany
Outside of academic interests, enjoys hiking, birding, botany, and photography.