In the first part of my talk, I will focus on machine learning problems arising in the field of genomics. The orders-of-magnitude cost reduction in genome sequencing, and 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. 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.
Sriram is am an assistant professor at UCLA in the Computer Science Department, the Department of Human Genetics, and the Department of Computational Medicine. Previously, he was a post-doctoral fellow at Harvard Medical School and the Broad Institute. Sriram received a Ph.D in Computer Science at UC Berkeley, a B.Tech in Computer Science from IIT, Madras. Sriram is broadly interested in problems at the intersection of computer science, statistics, and biomedicine.
