Not What but Why: Machine Learning for Understanding Genomics
Machine learning and artificial intelligence are changing the nature of biological research, especially genomics. Artificial intelligence applications are opening up our understanding of ourselves and disease, and we must strive to create tools that can work as partners in research, not simply as black boxes. Barbara Engelhardt is an assistant professor in the Computer Science Department at Princeton University since 2014.
She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service.
Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution, an NIH NHGRI K99/R00 Pathway to Independence Award, and the Sloan Faculty Fellowship. Professor Engelhardt is currently a PI on the Genotype-Tissue Expression (GTEx) Consortium.
Her research interests involve statistical models and methods for analysis of high-dimensional data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human diseases. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.