Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …

Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however,
crucial spatial information is often lost. We present SpaOTsc, a method relying on structured …

We propose and study a method called FLOT that estimates scene flow on point clouds. We
start the design of FLOT by noticing that scene flow estimation on point clouds reduces to …

Cross-domain alignment between two sets of entities (eg, objects in an image, words in a
sentence) is fundamental to both computer vision and natural language processing. Existing …

We propose a new perspective on designing powerful Graph Neural Networks (GNNs). In a
nutshell, this enables a general solution to inject structural properties of graphs into a …

Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a
tissue slice while recording the two-dimensional (2D) coordinates of each spot. We …

We propose a scalable Gromov-Wasserstein learning (S-GWL) method and establish a
novel and theoretically-supported paradigm for large-scale graph analysis. The proposed …

Understanding generalization and robustness of machine learning models fundamentally
relies on assuming an appropriate metric on the data space. Identifying such a metric is …

Spatially resolved transcriptomics has been dramatically transforming biological and
medical research in various fields. It enables transcriptome profiling at single‐cell, multi …

Comparison of graph structure is a ubiquitous task in data analysis and machine learning,
with diverse applications in fields such as neuroscience, cyber security, social network …