Abstract: 

Biology and medicine are deluged with data so that techniques from machine learning and statistics will increasingly play a key role in extracting insights from the vast quantities of data being generated. I will provide an overview of the modeling and inferential challenges that arise in these domains.

In the first part of my talk, I will focus on machine learning problems arising in the field of genomics. The cost of genome sequencing has decreased by over 100,000 fold over the last decade. Availability of genetic variation data from millions of individuals has opened up the possibility of using genetic information to identifying the cause of diseases, developing effective drugs, predicting disease risk and personalizing treatment. While genome-wide association studies offer a powerful paradigm to discovering disease-causing genes, the hidden genetic structure of human populations can confound these studies. I will describe statistical models that can infer this hidden structure and show how these inferences lead to novel insights into the genetic basis of diseases.

In the second part of my talk, I will discuss how the availability of large-scale electronic medical records is opening up the possibility of using machine learning in clinical settings. These electronic medical records are designed to capture a wide range of data associated with a patient including demographic information, laboratory tests, images, medications and clinical notes. Using electronic records from around 60,000 surgeries over five years in the UCLA hospital, I will describe efforts to use machine learning algorithms to predict mortality after surgery. Our results reveal that these algorithms can accurately predict mortality from information available prior to surgery indicating that automated predictive systems have great potential to augment clinical care.

Bio: 

Sriram Sankararaman is an assistant professor in the Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA where he leads the machine learning and genomic lab. His research interests lie at the interface of computer science, statistics and biology and is interested in developing statistical machine learning algorithms to make sense of large-scale biomedical 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 the Indian Institute of Technology, Madras, a Ph.D. in Computer Science from UC Berkeley and was a post-doctoral fellow in Harvard Medical School before joining UCLA. He is a recipient of the Alfred P. Sloan Foundation fellowship (2017), Okawa Foundation grant (2017), the UCLA Hellman fellowship (2017), the NIH Pathway to Independence Award (2014), a Simons Research fellowship (2014), and a Harvard Science of the Human Past fellowship (2012) as well as the Northrop-Grumman Excellence in Teaching Award at UCLA (2019).