EE Systems Seminar

Abstract: Advances in DNA sequencing are generating large quantities of genomic data that could allow us to answer fundamental questions in biology and medicine. One class of genomic studies attempt to correlate variation in traits (such as diseases) with variation in genes across large numbers of individuals. Such studies, however, leads to a number of statistical as well as computational challenges.

I will describe some of the important challenges. The first arises from the hidden genetic structure of human populations that can lead to the inference of spurious associations between genetic variants and disease. I will describe latent variable models that can infer this hidden structure and show how these inferences lead to novel insights into the genetics of diseases and into human history. A second challenge is the problem of preserving the privacy of individuals that participate in these studies. I will talk about a technique for breaching privacy as well as our attempts to characterize the statistical limits of such attacks that allow us to determine how such data can be safely released.

Bio: Sriram Sankararaman is an assistant professor in the Departments of Computer Science and Human Genetics at UCLA. His research interests lie at the interface of computer science, statistics, and biology. He is interested in developing statistical machine learning algorithms to make sense of large-scale genomic data and in using these tools to understand the interplay between evolution, our genomes, and traits. He received a B.Tech. in Computer Science from IIT Madras, a Ph.D. in Computer Science from UC Berkeley and was a post-doctoral fellow in the Department of Genetics, Harvard Medical School before joining UCLA. He is the recipient of a Alfred P. Sloan fellowship (2017), NIH Pathway to Independence Award (2014), a Simons Research fellowship (2014), and a Harvard Science of the Human Past fellowship (2012).

Host: Siddharth Jain