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Barbara Engelhardt
Born
Barbara Elizabeth Engelhardt
Alma materStanford University (BS, MS) University of California, Berkeley (PhD)
AwardsOverton Prize (2021)
Scientific career
FieldsStatistical genetics
Bayesian statistics
Machine learning
Statistical inference
Genomics[1]
InstitutionsPrinceton University
Chicago University
Jet Propulsion Laboratory
ThesisPredicting protein molecular function (2007)
Doctoral advisorMichael I. Jordan[2]
Websitewww.cs.princeton.edu/~bee/ Edit this at Wikidata

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.

Education

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Engelhardt received a Bachelor of Science in Symbolic Systems and a Master of Science in Computer Science from Stanford University. She received a PhD in 2008 from the University of California, Berkeley supervised by Michael I. Jordan.[3] 

Career and research

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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.' [21]

Honors and awards

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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

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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]

References

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  1. ^ a b Barbara Engelhardt publications indexed by Google Scholar Edit this at Wikidata
  2. ^ Barbara Engelhardt at the Mathematics Genealogy Project Edit this at Wikidata
  3. ^ "Michael I. Jordan's Home Page". people.eecs.berkeley.edu. Retrieved 2021-01-11.
  4. ^ "Stephens Lab". stephenslab.uchicago.edu. Retrieved 2021-01-11.
  5. ^ "Eleven Women Faculty Members Who Have Been Assigned New Duties". Women In Academia Report. 2018-03-08. Retrieved 2021-01-11.
  6. ^ "Barbara Elizabeth Engelhardt's Profile | Stanford Profiles". profiles.stanford.edu. Retrieved 2022-08-27.
  7. ^ "barbara.engelhardt@gladstone.ucsf.edu". gladstone.org. Retrieved 2022-08-27.
  8. ^ "3cs | AIG". sensorwebs.jpl.nasa.gov. Retrieved 2021-01-11.
  9. ^ Engelhardt, Barbara E.; Jordan, Michael I.; Muratore, Kathryn E.; Brenner, Steven E. (2005-10-07). "Protein Molecular Function Prediction by Bayesian Phylogenomics". PLOS Computational Biology. 1 (5): e45. Bibcode:2005PLSCB...1...45E. doi:10.1371/journal.pcbi.0010045. ISSN 1553-7358. PMC 1246806. PMID 16217548.
  10. ^ Engelhardt, Barbara E.; Jordan, Michael I.; Srouji, John R.; Brenner, Steven E. (2011-11-01). "Genome-scale phylogenetic function annotation of large and diverse protein families". Genome Research. 21 (11): 1969–1980. doi:10.1101/gr.104687.109. ISSN 1088-9051. PMC 3205580. PMID 21784873.
  11. ^ Engelhardt, Barbara E.; Stephens, Matthew (2010-09-16). "Analysis of Population Structure: A Unifying Framework and Novel Methods Based on Sparse Factor Analysis". PLOS Genetics. 6 (9): e1001117. doi:10.1371/journal.pgen.1001117. ISSN 1553-7404. PMC 2940725. PMID 20862358.
  12. ^ Mangravite, Lara M.; Engelhardt, Barbara E.; Medina, Marisa W.; Smith, Joshua D.; Brown, Christopher D.; Chasman, Daniel I.; Mecham, Brigham H.; Howie, Bryan; Shim, Heejung; Naidoo, Devesh; Feng, QiPing (October 2013). "A statin-dependent QTL for GATM expression is associated with statin-induced myopathy". Nature. 502 (7471): 377–380. Bibcode:2013Natur.502..377M. doi:10.1038/nature12508. ISSN 1476-4687. PMC 3933266. PMID 23995691.
  13. ^ Gao, Chuan; McDowell, Ian C.; Zhao, Shiwen; Brown, Christopher D.; Engelhardt, Barbara E. (2016-07-28). Zhou, Xianghong Jasmine (ed.). "Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering". PLOS Computational Biology. 12 (7): e1004791. Bibcode:2016PLSCB..12E4791G. doi:10.1371/journal.pcbi.1004791. ISSN 1553-7358. PMC 4965098. PMID 27467526.
  14. ^ Dumitrascu, Bianca; Darnell, Gregory; Ayroles, Julien; Engelhardt, Barbara E (2019-01-15). Hancock, John (ed.). "Statistical tests for detecting variance effects in quantitative trait studies". Bioinformatics. 35 (2): 200–210. doi:10.1093/bioinformatics/bty565. ISSN 1367-4803. PMC 6330007. PMID 29982387.
  15. ^ Aguet, François; Brown, Andrew A.; Castel, Stephane E.; Davis, Joe R.; He, Yuan; Jo, Brian; Mohammadi, Pejman; Park, YoSon; Parsana, Princy; Segrè, Ayellet V.; Strober, Benjamin J. (October 2017). "Genetic effects on gene expression across human tissues". Nature. 550 (7675): 204–213. Bibcode:2017Natur.550..204A. doi:10.1038/nature24277. ISSN 1476-4687. PMC 5776756. PMID 29022597.
  16. ^ The GTEx Consortium (2020-09-11). "The GTEx Consortium atlas of genetic regulatory effects across human tissues". Science. 369 (6509): 1318–1330. Bibcode:2020Sci...369.1318.. doi:10.1126/science.aaz1776. ISSN 0036-8075. PMC 7737656. PMID 32913098.
  17. ^ Verma, Archit; Engelhardt, Barbara E. (2020-07-21). "A robust nonlinear low-dimensional manifold for single cell RNA-seq data". BMC Bioinformatics. 21 (1): 324. doi:10.1186/s12859-020-03625-z. ISSN 1471-2105. PMC 7374962. PMID 32693778.
  18. ^ Camerlenghi, Federico; Dumitrascu, Bianca; Ferrari, Federico; Engelhardt, Barbara E.; Favaro, Stefano (December 2020). "Nonparametric Bayesian multiarmed bandits for single-cell experiment design". Annals of Applied Statistics. 14 (4): 2003–2019. arXiv:1910.05355. doi:10.1214/20-AOAS1370. ISSN 1932-6157. S2CID 204509422.
  19. ^ Cheng, Li-Fang; Dumitrascu, Bianca; Darnell, Gregory; Chivers, Corey; Draugelis, Michael; Li, Kai; Engelhardt, Barbara E. (2020-07-08). "Sparse multi-output Gaussian processes for online medical time series prediction". BMC Medical Informatics and Decision Making. 20 (1): 152. doi:10.1186/s12911-020-1069-4. ISSN 1472-6947. PMC 7341595. PMID 32641134.
  20. ^ Cheng, Li-Fang; Prasad, Niranjani; Engelhardt, Barbara E. (2019). "An Optimal Policy for Patient Laboratory Tests in Intensive Care Units". Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 24: 320–331. arXiv:1808.04679. ISSN 2335-6936. PMC 6417830. PMID 30864333.
  21. ^ "A Statistical Search for Genomic Truths". 27 February 2018.
  22. ^ "Prof. Barbara Engelhardt recipient of an Alfred P. Sloan Foundation Research Fellowship | Computer Science Department at Princeton University". www.cs.princeton.edu. Retrieved 2021-01-11.
  23. ^ "Barbara Engelhardt wins CAREER award for research with high-dimensional genomic data | Computer Science Department at Princeton University". www.cs.princeton.edu. Retrieved 2021-01-11.
  24. ^ "Grants". Chan Zuckerberg Initiative. Retrieved 2021-01-11.
  25. ^ "Fast Grants". fastgrants.org. Retrieved 2021-01-11.
  26. ^ "Overton Prize". www.iscb.org.
  27. ^ "NHGRI supports seven young investigators on research career paths". Genome.gov. Retrieved 2021-01-11.
  28. ^ "2005 Google Anita Borg Memorial Scholarship Winners Announced – News announcements – News from Google – Google". googlepress.blogspot.com. Retrieved 2021-01-11.
  29. ^ The Society for Molecular Biology & Evolution. "The Walter M. Fitch Award". www.smbe.org. Archived from the original on 2020-08-12. Retrieved 2021-01-11.
  30. ^ "Senior Advisory Council". Archived from the original on 2021-01-13. Retrieved 2021-01-11.
  31. ^ "2021 Conference". icml.cc. Retrieved 2021-01-11.
  32. ^ "ACD Working Group on Artificial Intelligence". NIH Advisory Committee to the Director. Retrieved 2021-01-11.
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