Uploaded July 2026 | Updated September 2026, 2 weeks ago
Models, Inference, and Algorithms | April 22, 2026
Broad Institute of MIT and Harvard
Meeting: Resolving Tissue Maps: Statistical and Deep Learning Methods for Integrative Spatial Omics Across Samples, Sections, and Modalities
Ying Ma
Assistant Professor of Biostatistics, Brown University
Abstract:
Spatial omics technologies have opened new frontiers for understanding the molecular organization of tissues, yet major challenges remain in scalability, cross-sample alignment, and integration across resolutions and modalities. In this talk, I will present a series of data integration methods developed in my lab to address these challenges across samples, platforms, and molecular signals. I will introduce statistical methods that learn biologically informed spatial domains, and deep learning methods that jointly learn spatial correspondence and shared representations across tissue sections, as well as uncertainty-aware contrastive models for integrating spatial multi-omics data across diverse modalities. These approaches enable robust alignment despite distortion and batch effects, preserve modality-specific biological signals, and scale to ultra–high-resolution datasets. Together, these methods uncover refined spatial domains, modality-specific regulatory programs, and regions of biological or technical heterogeneity. By combining scalability, generalizability, and interpretability, our goal is to build reliable computational foundations for spatial omics that accelerate biological discovery.
About MIA:
The Models, Inference & Algorithms (MIA) Initiative at the Broad Institute supports learning and collaboration across the interface of biology and medicine with mathematics, statistics, machine learning, and computer science. Our weekly meetings are open and pedagogical, emphasizing lucid exposition of computational ideas over rapid-fire communication of results.
MIA is hosted by the Eric and Wendy Schmidt Center at the Broad Institute.
Relevant Links:
MIA Website: broadinstitute.org/mia
MIA YouTube Playlist: broad.io/MIAPlaylist
Copyright Broad Institute, 2026. All rights reserved.
Models, Inference, and Algorithms | April 22, 2026
Broad Institute of MIT and Harvard
Meeting: Resolving Tissue Maps: Statistical and Deep Learning Methods for Integrative Spatial Omics Across Samples, Sections, and Modalities
Ying Ma
Assistant Professor of Biostatistics, Brown University
Abstract:
Spatial omics technologies have opened new frontiers for understanding the molecular organization of tissues, yet major challenges remain in scalability, cross-sample alignment, and integration across resolutions and modalities. In this talk, I will present a series of data integration methods developed in my lab to address these challenges across samples, platforms, and molecular signals. I will introduce statistical methods that learn biologically informed spatial domains, and deep learning methods that jointly learn spatial correspondence and shared representations across tissue sections, as well as uncertainty-aware contrastive models for integrating spatial multi-omics data across diverse modalities. These approaches enable robust alignment despite distortion and batch effects, preserve modality-specific biological signals, and scale to ultra–high-resolution datasets. Together, these methods uncover refined spatial domains, modality-specific regulatory programs, and regions of biological or technical heterogeneity. By combining scalability, generalizability, and interpretability, our goal is to build reliable computational foundations for spatial omics that accelerate biological discovery.
About MIA:
The Models, Inference & Algorithms (MIA) Initiative at the Broad Institute supports learning and collaboration across the interface of biology and medicine with mathematics, statistics, machine learning, and computer science. Our weekly meetings are open and pedagogical, emphasizing lucid exposition of computational ideas over rapid-fire communication of results.
MIA is hosted by the Eric and Wendy Schmidt Center at the Broad Institute.
Relevant Links:
MIA Website: broadinstitute.org/mia
MIA YouTube Playlist: broad.io/MIAPlaylist
Copyright Broad Institute, 2026. All rights reserved.
