Barbara Engelhardt: Difference between revisions
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{{Short description|
{{Infobox scientist
'''Barbara Engelhardt''' is an American [[computer scientist]] and specialist in [[bioinformatics]]. Working a a Professor at [[Princeton University]], her work has focused on [[Latent variable model|latent variable models]], exploratory data analysis for [[genomics|genomic data]], and [[quantitative trait locus|QTLs]]. In 2021, she was awarded the [[Overton Prize]] by the [[International Society for Computational Biology]].▼
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| birth_name = Barbara Elizabeth Engelhardt
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| fields = [[Statistical genetics]]<br>[[Bayesian statistics]]<br>[[Machine learning]]<br>[[Statistical inference]]<br>[[Genomics]]<ref name=gs/>
| workplaces = [[Princeton University]]<br>[[Chicago University]]<br>[[Jet Propulsion Laboratory]]
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| education =
| alma_mater = [[Stanford University]] (BS, MS)
[[University of California, Berkeley]] (PhD)
| thesis_title = Predicting protein molecular function
| thesis_url = https://search.library.berkeley.edu/permalink/01UCS_BER/s4lks2/cdi_keris_ddod_11544041
| thesis_year = 2007
| doctoral_advisor = [[Michael I. Jordan]]<ref name=mathgene>{{MathGenealogy}}</ref>
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| awards = [[Overton Prize]] (2021)
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▲'''Barbara Elizabeth Engelhardt''' is an American [[computer scientist]] and specialist in [[bioinformatics]]. Working
==
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]]
==
Engelhardt worked as a [[postdoctoral researcher]] at the [[University of Chicago]] in the Department of Human Genetics with [[Matthew Stephens (statistician)|Matthew Stephens]] from 2008 to 2011.<ref>{{Cite web|title=Stephens Lab|url=https://stephenslab.uchicago.edu/people.html|access-date=2021-01-11|website=stephenslab.uchicago.edu}}</ref> 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.<ref>{{Cite web|date=2018-03-08|title=Eleven Women Faculty Members Who Have Been Assigned New Duties|url=https://www.wiareport.com/2018/03/eleven-women-faculty-members-assigned-new-duties/|access-date=2021-01-11|website=Women In Academia Report}}</ref> In August 2022, she moved to California, she now holds the position of Professor at [[Stanford University]] and [https://gladstone.org/science/data-science-and-biotechnology-institute Gladstone Institute of Data Science and Biotechnology]. <ref>{{Cite web |title=Barbara Elizabeth Engelhardt's Profile {{!}} Stanford Profiles |url=https://profiles.stanford.edu/barbara-engelhardt |access-date=2022-08-27 |website=profiles.stanford.edu}}</ref><ref>{{Cite web |[email protected] |url=https://gladstone.org/people/barbara-engelhardt |access-date=2022-08-27 |website=gladstone.org |language=en}}</ref>
Post tenure, Engelhardt's research in these latent variable models has expanded to include single cell sequencing, with a particular focus on spatial transcriptomics.<ref>{{Cite journal|last1=Verma|first1=Archit|last2=Engelhardt|first2=Barbara E.|date=2020-07-21|title=A robust nonlinear low-dimensional manifold for single cell RNA-seq data|journal=BMC Bioinformatics|volume=21|issue=1|pages=324|doi=10.1186/s12859-020-03625-z|issn=1471-2105|pmc=7374962|pmid=32693778 |doi-access=free }}</ref> 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.<ref>{{Cite journal|last1=Camerlenghi|first1=Federico|last2=Dumitrascu|first2=Bianca|last3=Ferrari|first3=Federico|last4=Engelhardt|first4=Barbara E.|last5=Favaro|first5=Stefano|date=December 2020|title=Nonparametric Bayesian multiarmed bandits for single-cell experiment design|url=https://projecteuclid.org/euclid.aoas/1608346909|journal=Annals of Applied Statistics|language=EN|volume=14|issue=4|pages=2003–2019|doi=10.1214/20-AOAS1370|arxiv=1910.05355|s2cid=204509422|issn=1932-6157}}</ref> Her work has also expanded into machine learning for electronic healthcare records.<ref>{{Cite journal|last1=Cheng|first1=Li-Fang|last2=Dumitrascu|first2=Bianca|last3=Darnell|first3=Gregory|last4=Chivers|first4=Corey|last5=Draugelis|first5=Michael|last6=Li|first6=Kai|last7=Engelhardt|first7=Barbara E.|date=2020-07-08|title=Sparse multi-output Gaussian processes for online medical time series prediction|journal=BMC Medical Informatics and Decision Making|volume=20|issue=1|pages=152|doi=10.1186/s12911-020-1069-4|issn=1472-6947|pmc=7341595|pmid=32641134 |doi-access=free }}</ref><ref>{{Cite journal|last1=Cheng|first1=Li-Fang|last2=Prasad|first2=Niranjani|last3=Engelhardt|first3=Barbara E.|date=2019|title=An Optimal Policy for Patient Laboratory Tests in Intensive Care Units|journal=Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|volume=24|pages=320–331|issn=2335-6936|pmc=6417830|pmid=30864333|arxiv=1808.04679}}</ref>
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.'
<ref>{{Cite web|url=https://www.quantamagazine.org/barbara-engelhardts-statistical-search-for-genomic-truths-20180227/|title = A Statistical Search for Genomic Truths|date = 27 February 2018}}</ref>
=== Honors and awards ===
Engelhardt's postdoctoral work was partly funded through an NIH NHGRI K99 grant,<ref>{{Cite web|title=NHGRI supports seven young investigators on research career paths|url=https://www.genome.gov/27545993/2012-news-feature-nhgri-supports-seven-young-investigators-on-research-career-paths|access-date=2021-01-11|website=Genome.gov|language=en}}</ref>
=== Service and leadership===
Engelhardt served on the Board of Directors (2014–2017) and the Senior Advisory Council (2017–present) for Women in Machine Learning.<ref>{{Cite web|title=Senior Advisory Council|url=https://wimlworkshop.org/senior-advisory-council/|access-date=2021-01-11|language=en-US|archive-date=2021-01-13|archive-url=https://web.archive.org/web/20210113135141/https://wimlworkshop.org/senior-advisory-council/|url-status=dead}}</ref>
==References==
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[[Category:21st-century American women scientists]]
[[Category:Sloan Research Fellows]]
[[Category:Overton Prize winners]]
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Latest revision as of 20:25, 29 December 2023
Barbara Engelhardt | |
---|---|
Born | Barbara Elizabeth Engelhardt |
Alma mater | Stanford University (BS, MS) University of California, Berkeley (PhD) |
Awards | Overton Prize (2021) |
Scientific career | |
Fields | Statistical genetics Bayesian statistics Machine learning Statistical inference Genomics[1] |
Institutions | Princeton University Chicago University Jet Propulsion Laboratory |
Thesis | Predicting protein molecular function (2007) |
Doctoral advisor | Michael I. Jordan[2] |
Website | www |
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
[edit]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
[edit]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
[edit]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
[edit]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
[edit]- ^ a b Barbara Engelhardt publications indexed by Google Scholar
- ^ Barbara Engelhardt at the Mathematics Genealogy Project
- ^ "Michael I. Jordan's Home Page". people.eecs.berkeley.edu. Retrieved 2021-01-11.
- ^ "Stephens Lab". stephenslab.uchicago.edu. Retrieved 2021-01-11.
- ^ "Eleven Women Faculty Members Who Have Been Assigned New Duties". Women In Academia Report. 2018-03-08. Retrieved 2021-01-11.
- ^ "Barbara Elizabeth Engelhardt's Profile | Stanford Profiles". profiles.stanford.edu. Retrieved 2022-08-27.
- ^ "[email protected]". gladstone.org. Retrieved 2022-08-27.
- ^ "3cs | AIG". sensorwebs.jpl.nasa.gov. Retrieved 2021-01-11.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ "A Statistical Search for Genomic Truths". 27 February 2018.
- ^ "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.
- ^ "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.
- ^ "Grants". Chan Zuckerberg Initiative. Retrieved 2021-01-11.
- ^ "Fast Grants". fastgrants.org. Retrieved 2021-01-11.
- ^ "Overton Prize". www.iscb.org.
- ^ "NHGRI supports seven young investigators on research career paths". Genome.gov. Retrieved 2021-01-11.
- ^ "2005 Google Anita Borg Memorial Scholarship Winners Announced – News announcements – News from Google – Google". googlepress.blogspot.com. Retrieved 2021-01-11.
- ^ 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.
- ^ "Senior Advisory Council". Archived from the original on 2021-01-13. Retrieved 2021-01-11.
- ^ "2021 Conference". icml.cc. Retrieved 2021-01-11.
- ^ "ACD Working Group on Artificial Intelligence". NIH Advisory Committee to the Director. Retrieved 2021-01-11.