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Barbara Engelhardt

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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.

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

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  
  2. ^ Barbara Engelhardt at the Mathematics Genealogy Project  
  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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