Speaker: Prof. Arvind Rao

Title: Machine Learning Approaches To The Interpretation Of the Tumor Microenvironment Using Spatial Immuno-profiling & Spatial Transcriptomics

Abstract: Spatial profiling technologies like hyper-plex immunostaining in tissue, spatial transcriptomics, etc. have the potential to enable a multi-factorial, multi-modal characterization of the tissue microenvironment. Scalable, quantitative methods to analyze and interpret spatial patterns of protein staining and gene expression are required to understand cell-cell relationships in the context of local variations in tissue structure. Objective scoring methods inspired by recent advances in statistics and machine learning can serve to aid the interpretation of these datasets, as well as their integration with other, companion data like genomics. In this talk, we will discuss elements of spatial profiling from multiple studies as well as paradigms from statistics and machine learning in the context of these problems. This talk will also discuss the use of AI/ML and spatial analytics of the tumor microenvironment to derive spatial biomarkers of immunotherapy.
About the Speaker: Dr. Arvind Rao is an Associate Professor in the Department of Computational Medicine and Bioinformatics at the University of Michigan. His group uses image analysis and machine learning methods to link image-derived phenotypes with genetic data, across biological scale (i.e. single cell, tissue, and radiology data). Such methods have found application in radiogenomics, drug repurposing based on phenotypic screens, and spatial profiling in tissue, as well as in spatial transcriptomics. Dr. Arvind received his Ph.D. in Electrical Engineering and Bioinformatics from the University of Michigan, specializing in transcriptional genomics, and was a Lane Postdoctoral Fellow at Carnegie Mellon University, specializing in bioimage informatics.