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Barbara Engelhardt

Barbara Engelhardt https://www.cs.princeton.edu/~bee/ https://www.cs.princeton.edu/people/profile/beeis an Associate Professor in the Department of Computer Science at Princeton University https://en.wikipedia.org/wiki/Princeton_University. Her research involves the development of statistical and machine learning models for the analysis of biomedical data.

Academic Background

Dr. Engelhardt received her B.S. in Symbolic Systems and M.S. in Computer Science (working with Daphne Koller) from Stanford University. She received her Ph.D. in 2008 from the University of California, Berkeley in the Electrical Engineering and Computer Science Department supervised by Prof. Michael I Jordan. She did a postdoc at University of Chicago in the Department of Human Genetics with Prof Matthew Stephens from 2008-2011. She joined the Duke University faculty in 2011 as an Assistant Professor in the Biostatistics and Bioinformatics Department. She moved to Princeton as an Assistant Professor in 2014 and received a promotion to Associate Professor with tenure in 2017.

Research and Work

After graduating from Stanford, Engelhardt worked at Jet Propulsion Laboratory in the Artificial Intelligence group for two years, working on planning and scheduling for autonomous spacecraft. As a graduate student at Berkeley, she developed statistical models for protein function annotation and statistical frameworks for reasoning about ontologies.

She worked from 2007-2008 at 23andMe, and then began postdoctoral research at University of Chicago. There, she developed sparse factor analysis models for population structure and Bayesian models for association testing.

In her faculty position, the bulk of Engelhardt's research focused on developing latent variable models and exploratory data analysis for genomic data, and also on statistical models for association testing in expression QTLs. 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 and v8 data. She is involved in the Fragile Families Child Wellbeing genomic data analysis.

Post tenure, Engelhardt's research in these latent variable models has expanded to include single cell sequencing, with a particular focus on spatial transcriptomics. 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.

Her work has also expanded into machine learning for electronic healthcare records. She has developed a Gaussian process-based framework for modeling patient data and response to interventions, and treatment policies for different patient objectives (e.g., removing mechanical ventilation, administration of lab tests, electrolyte repletion) using off-policy reinforcement learning methods. With Professor Finale Doshi-Velez, she has developed an RL method that identifies policies that may be evaluated by the data in hand. With Princeton undergraduate Grace Guan, she developed methods for scheduling sick-child visits and clinicians in a pediatric outpatient clinic.

Dr. Engelhardt's work has been featured in Quanta Magazine. In 2017, she gave a TEDx talk titled: 'Not What but Why: Machine Learning for Understanding Genomics.'

https://www.quantamagazine.org/barbara-engelhardts-statistical-search-for-genomic-truths-20180227/

https://www.youtube.com/watch?v=uC3SfnbCXmw

Honors and Awards

Engelhardt’s research has been funded by the NIH through two R01s and a number of other mechanisms. Dr. Engelhardt has been recognized by several awards including an Alfred P. Sloan Fellowship in Computational Biology, an NSF CAREER Award, two Chan-Zuckerberg Initiative grants for the Human Cell Atlas, and a FastGrant for her recent work on Covid-19. https://www.cs.princeton.edu/news/prof-barbara-engelhardt-recipient-alfred-p-sloan-foundation-research-fellowship

https://www.cs.princeton.edu/news/barbara-engelhardt-receives-nsf-career-award

https://chanzuckerberg.com/grants-ventures/grants/

https://fastgrants.org/ Engelhardt's postdoctoral work was partly funded through an NIH NHGRI K99 grant, and her PhD was partly funded through an NSF Graduate Research Fellowship and the Google Anita Borg Scholarship in 2005. She received SMBE's Walter M. Fitch Prize in 2004. https://www.genome.gov/27545993/2012-news-feature-nhgri-supports-seven-young-investigators-on-research-career-paths

https://www.research.gov/grfp/AwardeeList.do http://googlepress.blogspot.com/2005/04/2005-google-anita-borg-memorial_08.html https://www.smbe.org/smbe/AWARDS/TheWalterMFitchAward.aspx

Service

Professor Engelhardt served on the Board of Directors (2014-2017) and the Senior Advisory Council (2017-present) for Women in Machine Learning. She is the Diversity & Inclusion Co-Chair at the International Conference on Machine Learning (ICML, 2018-2022). In 2019, she was a member of the NIH Advisory Committee to the Director, Working Group on Artificial Intelligence. https://wimlworkshop.org/senior-advisory-council/ https://icml.cc/ https://acd.od.nih.gov/working-groups/ai.html

Personal Life

Dr. Engelhardt was born and raised in New York City, and attended Trinity School. She is a single mother to four kids. link to video: https://www.youtube.com/watch?v=uC3SfnbCXmw