School of CSE Seminar Series: Sriram Sankararaman

Speaker: Sriram Sankararaman, professor at UCLA
Date and Time: November 7, 2:00-3:00 p.m.
Location: TBD
Host: Yunan Luo

Title: Understanding the Genetic Basis of Complex Traits and Common Diseases from Biobank-Scale Data: Statistical and Computational Challenges

Abstract: The quest to understand the interplay between evolution, genes and traits has been revolutionized by the collection of rich phenotypic and genetic data across millions of individuals in diverse populations. However analyses of these Biobank-scale datasets present substantial statistical and computational challenges. 

I will describe how we bring together statistical and computational insights to design accurate and highly scalable algorithms for a suite of problems that arise in the analysis of Biobank data: highly scalable randomized inference algorithms to dissect the genetic architecture of complex traits and deep-learning based phenotype imputation to deal with complex patterns of missingness. By applying these methods to about half a million individuals from the UK Biobank, we obtain novel insights how genetic effects are distributed across the genome, the relative contributions of additive, dominance and gene-environment interaction effects to trait variation, and new genes that confer risk for hard-to-measure diseases.

Bio: Sankararaman is a professor in the Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA. His research interests lie at the interface of computer science, statistics, and biology. His lab develops machine learning algorithms to analyze genomic and biomedical data with the broad goal of understanding the interplay between evolution, genomes, and traits. His work has led to methods for identifying disease genes in diverse human populations, discovered  interbreeding between archaic and modern humans, established guidelines for sharing genetic data while maintaining privacy, and characterized the genetic architecture of human complex traits.

He received a B.Tech. in Computer Science from the Indian Institute of Technology, Madras, a Ph.D. in Computer Science from UC Berkeley and was a postdoctoral fellow in Harvard Medical School before joining UCLA. He is a recipient of a NSF Career Award, a NIH Pathway to Independence Award, fellowships from Microsoft Research, the Sloan Foundation, the Okawa
Foundation and the Simons Institute , and multiple teaching awards.