Cross-Modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport

Name: Cross-Modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport
Start: 2024-09-01T00:00:00Z
Location: Seattle, WA, USA

Romain Lopez.

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Abstract

We introduce a labeled Gromov-Wasserstein optimal transport framework for matching and predicting cellular responses across different experimental modalities. The work received the Best Paper Award at the ICML 2024 Workshop on AI for Science and was selected for an oral presentation at MLCB 2024.

Date

Sep 1, 2024

Event

Machine Learning in Computational Biology (MLCB) 2024 — Oral Presentation

Location

Seattle, WA, USA

Seattle, WA, United States

optimal transport perturbation prediction single-cell genomics contributed talk

Romain Lopez

Assistant Professor of Computer Science and Biology

My research interests lie at the intersection of statistics, computation and modeling of biological data. A significant part of my research is driven by building more statistically accurate and faster machine learning software for analyzing genomics data.