Transport Clustering: Solving Low-Rank Optimal Transport via Clustering
ICML 2026
Research Interests
My interests are primarily in optimal transport to infer least-cost maps between distributions, including algorithmic approaches for discrete optimal transport (algorithms for primal and low-rank optimal transport), the theory of optimal transport based distributional optimization (the JKO Scheme and Wasserstein gradient flow), and optimizing for unnormalized distributions (score-matching, Fokker-Planck, and diffusion models).
I adapt these mathematical techniques for computational applications to high-throughput spatiotemporal biological data recorded via dissociated marginal distributions (snapshots). I develop methods which infer couplings between such data marginals via optimal transport, inferring maps for spatiotemporal dynamics and growth-rates, latent Markovian cell-type transitions, and alignments across multiomics modalities with techniques from geometric deep learning and differential geometry.Publications
Riemannian Metric Learning for Alignment of Spatial Multiomics
ISMB 2026
Multimodal spatial alignment and morphology mapping with MOSAICField
RECOMB 2026
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 · RECOMB 2024
Pooled RNA-IP approach to investigate variant effects on RBP binding and splicing
ASHG 2022 (Poster)
Preprints
Talks
A selection of recent talks. Slides are linked when available.
Optimal Transport Modeling of Cellular Differentiation: From Low-Rank Structure to Temporal Dynamics
Hierarchical Refinement: Optimal Transport to Infinity and Beyond
Hidden-Markov Optimal Transport for Developmental Time Series
Teaching and Service
Teaching Assistant: Organic Chemistry (CHEM UN2443, Columbia 2021)
Instructor: Introduction to Computer Science (King Summer Institute 2023)
Reviewer: NeurIPS 2026, Transactions on Pattern Analysis and Machine Intelligence 2026, ICML 2026, NeurIPS 2025, ISMB/ECCB 2025, RECOMB 2025, RECOMB 2024.
Graduate mentorship: Princeton Pre-Application PhD Program, First Year Mentorship Program.
Relevant Coursework
Partial Differential Equations (MAT522), Information Theory (COS 585), Theoretical Machine Learning (COS 511), Dynamical Systems (APC 571), Deep Learning Theory (ORF 543), Statistical Mechanics (CHEM3079), Machine Learning and Pattern Recognition (ECE 535), Advanced Algorithm Design (COS 521), Ordinary Differential Equations (MATHUN2030), Measure-Theoretic Probability (MAT 385), Stochastic Calculus (ORF 527; AUD), Advanced Organic Chemistry (CHEM GU4147), Biochemistry (BCHM 4501).
Other
Outside of my academic interests, I am an avid hiker and an amateur birder, botanist, and photographer.