Barbara Engelhardt
Research
Interests: Machine learning, Bayesian statistics, statistical genetics, computational biology, quantitative genetics.
Alfred P. Sloan Research Fellowship, 2016
Short Bio
Barbara E. Engelhardt, an associate professor, joined the Princeton Computer Science Department in 2014 from Duke University, where she had been an assistant professor in Biostatistics and Bioinformatics and Statistical Sciences. 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, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution. As a faculty member, she received the NIH NHGRI K99/R00 Pathway to Independence Award, a Sloan Faculty Fellowship, and an NSF CAREER Award. Professor Engelhardt’s research interests involve developing statistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human disease.
Selected Publications
- "A statin-dependent QTL for GATM expression is associated with statin-induced myopathy." Mangravite LM*, Engelhardt BE*, Medina MW, Mecham BH, Howie B, Shim H, Naidoo D, Smith JD, Rieder MJ, Nickerson DA, Stephens M*, Krauss RM*. (2013), Nature 502: 377-380.
- "Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs.” Brown CD, Mangravite LM, Engelhardt BE. (2013), PLoS Genetics 9(8): e1003649.
- "Genome-wide association study of d-amphetamine response in healthy human volunteers identifies putative associations, including cadherin 13 (CDH13)." Hart AB*, Engelhardt BE*, Wardel MC, Sokoloff G, Stephens M, de Wit H, Palmer AA. (2012), PLoS ONE 7(8):e42646.
- "Analysis of population structure: a unifying framework and novel methods based on sparse factor analysis." Engelhardt BE, Stephens M. (2010), PLoS Genetics 6(9):e1001117.
