American computer scientist
Barbara Elizabeth Engelhardt is an American computer scientist and specialist in bioinformatics. Working as a Professor at Stanford University, her work has focused on latent variable models, exploratory data analysis for genomic data, and QTLs.[1] In 2021, she was awarded the Overton Prize by the International Society for Computational Biology.
Career and research
Engelhardt worked as a postdoctoral researcher at the University of Chicago in the Department of Human Genetics with Matthew Stephens from 2008 to 2011.[4] She joined Duke University in 2011 as an assistant professor in the Biostatistics and Bioinformatics Department. She joined Princeton University as an assistant professor in 2014 and received a promotion to Associate Professor with tenure in 2017.[5] In August 2022, she moved to California, she now holds the position of Professor at Stanford University and Gladstone Institute of Data Science and Biotechnology. [6][7]
After graduating from Stanford, Engelhardt worked at the Jet Propulsion Laboratory in the Artificial Intelligence group for two years, working on planning and scheduling for autonomous spacecraft.[8] As a graduate student at Berkeley, she developed statistical models for protein function annotation and statistical frameworks for reasoning about ontologies.[9][10] During her postdoctoral research, she developed sparse factor analysis models for population structure[11] and Bayesian models for association testing.[12]
In her faculty position, the bulk of Engelhardt's research focused on developing latent variable models and exploratory data analysis for genomic data,[13] and also on statistical models for association testing in expression QTLs.[14] As a member of the Genotype Tissue Expression (GTEx) Consortium, her group was responsible for the trans-eQTL discovery and analysis in the GTEx v6[15] and v8 data.[16]
Post tenure, Engelhardt's research in these latent variable models has expanded to include single cell sequencing, with a particular focus on spatial transcriptomics.[17] She also has work on Bayesian experimental design using contextual multi-armed bandits, and has adapted this work to the novel species problem in order to inform single cell data collection for atlas building.[18] Her work has also expanded into machine learning for electronic healthcare records.[19][20]
Engelhardt's work has been featured in Quanta Magazine. In 2017, she gave a TEDx talk entitled: 'Not What but Why: Machine Learning for Understanding Genomics.'
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Honors and awards
Engelhardt's research has been funded by the National Institutes of Health through two R01 grants and a number of other mechanisms. Engelhardt has been recognized by several awards including an Alfred P. Sloan Fellowship in Computational Biology,[22] a National Science Foundation CAREER Award,[23] two Chan Zuckerberg Initiative grants for the Human Cell Atlas,[24] and a Fast Grant for her recent work on COVID-19.[25] In 2021, she was awarded the Overton Prize by the International Society for Computational Biology.[26]
Engelhardt's postdoctoral work was partly funded through an NIH NHGRI K99 grant,[27] and her PhD was partly funded through an NSF Graduate Research Fellowship and the Google Anita Borg Scholarship in 2005.[28] She received SMBE's Walter M. Fitch Prize in 2004.[29]
Service and leadership
Engelhardt served on the Board of Directors (2014–2017) and the Senior Advisory Council (2017–present) for Women in Machine Learning.[30] She is the Diversity & Inclusion Co-chair at the International Conference on Machine Learning (ICML, 2018–2022).[31] In 2019, she was a member of the NIH Advisory Committee to the Director, Working Group on Artificial Intelligence[32]
