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== Research and work ==
== 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<ref>{{Cite web|title=3cs {{!}} AIG|url=https://sensorwebs.jpl.nasa.gov/public/projects/3cs/|access-date=2021-01-11|website=sensorwebs.jpl.nasa.gov|language=en}}</ref>. As a graduate student at Berkeley, she developed statistical models for protein function annotation and statistical frameworks for reasoning about ontologies<ref>{{Cite journal|last1=Engelhardt|first1=Barbara E.|last2=Jordan|first2=Michael I.|last3=Muratore|first3=Kathryn E.|last4=Brenner|first4=Steven E.|date=2005-10-07|title=Protein Molecular Function Prediction by Bayesian Phylogenomics|journal=PLOS Computational Biology|language=en|volume=1|issue=5|pages=e45|doi=10.1371/journal.pcbi.0010045|issn=1553-7358|pmc=1246806|pmid=16217548|bibcode=2005PLSCB...1...45E}}</ref><ref>{{Cite journal|last1=Engelhardt|first1=Barbara E.|last2=Jordan|first2=Michael I.|last3=Srouji|first3=John R.|last4=Brenner|first4=Steven E.|date=2011-11-01|title=Genome-scale phylogenetic function annotation of large and diverse protein families|url=http://genome.cshlp.org/content/21/11/1969|journal=Genome Research|language=en|volume=21|issue=11|pages=1969–1980|doi=10.1101/gr.104687.109|issn=1088-9051|pmid=21784873|pmc=3205580}}</ref>.
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<ref>{{Cite web|title=3cs {{!}} AIG|url=https://sensorwebs.jpl.nasa.gov/public/projects/3cs/|access-date=2021-01-11|website=sensorwebs.jpl.nasa.gov|language=en}}</ref>. As a graduate student at Berkeley, she developed statistical models for [[protein function]] annotation and statistical frameworks for reasoning about [[ontologies]]<ref>{{Cite journal|last1=Engelhardt|first1=Barbara E.|last2=Jordan|first2=Michael I.|last3=Muratore|first3=Kathryn E.|last4=Brenner|first4=Steven E.|date=2005-10-07|title=Protein Molecular Function Prediction by Bayesian Phylogenomics|journal=PLOS Computational Biology|language=en|volume=1|issue=5|pages=e45|doi=10.1371/journal.pcbi.0010045|issn=1553-7358|pmc=1246806|pmid=16217548|bibcode=2005PLSCB...1...45E}}</ref><ref>{{Cite journal|last1=Engelhardt|first1=Barbara E.|last2=Jordan|first2=Michael I.|last3=Srouji|first3=John R.|last4=Brenner|first4=Steven E.|date=2011-11-01|title=Genome-scale phylogenetic function annotation of large and diverse protein families|url=http://genome.cshlp.org/content/21/11/1969|journal=Genome Research|language=en|volume=21|issue=11|pages=1969–1980|doi=10.1101/gr.104687.109|issn=1088-9051|pmid=21784873|pmc=3205580}}</ref>. During her postdoctoral research, she developed sparse factor analysis models for population structure<ref>{{Cite journal|last1=Engelhardt|first1=Barbara E.|last2=Stephens|first2=Matthew|date=2010-09-16|title=Analysis of Population Structure: A Unifying Framework and Novel Methods Based on Sparse Factor Analysis|journal=PLOS Genetics|language=en|volume=6|issue=9|pages=e1001117|doi=10.1371/journal.pgen.1001117|issn=1553-7404|pmc=2940725|pmid=20862358}}</ref> and Bayesian models for association testing<ref>{{Cite journal|last1=Mangravite|first1=Lara M.|last2=Engelhardt|first2=Barbara E.|last3=Medina|first3=Marisa W.|last4=Smith|first4=Joshua D.|last5=Brown|first5=Christopher D.|last6=Chasman|first6=Daniel I.|last7=Mecham|first7=Brigham H.|last8=Howie|first8=Bryan|last9=Shim|first9=Heejung|last10=Naidoo|first10=Devesh|last11=Feng|first11=QiPing|date=October 2013|title=A statin-dependent QTL for GATM expression is associated with statin-induced myopathy|journal=Nature|language=en|volume=502|issue=7471|pages=377–380|doi=10.1038/nature12508|pmid=23995691|pmc=3933266|bibcode=2013Natur.502..377M|issn=1476-4687}}</ref>.


During her postdoc, with Matthew Stephens, she developed sparse factor analysis models for population structure<ref>{{Cite journal|last1=Engelhardt|first1=Barbara E.|last2=Stephens|first2=Matthew|date=2010-09-16|title=Analysis of Population Structure: A Unifying Framework and Novel Methods Based on Sparse Factor Analysis|journal=PLOS Genetics|language=en|volume=6|issue=9|pages=e1001117|doi=10.1371/journal.pgen.1001117|issn=1553-7404|pmc=2940725|pmid=20862358}}</ref> and Bayesian models for association testing<ref>{{Cite journal|last1=Mangravite|first1=Lara M.|last2=Engelhardt|first2=Barbara E.|last3=Medina|first3=Marisa W.|last4=Smith|first4=Joshua D.|last5=Brown|first5=Christopher D.|last6=Chasman|first6=Daniel I.|last7=Mecham|first7=Brigham H.|last8=Howie|first8=Bryan|last9=Shim|first9=Heejung|last10=Naidoo|first10=Devesh|last11=Feng|first11=QiPing|date=October 2013|title=A statin-dependent QTL for GATM expression is associated with statin-induced myopathy|journal=Nature|language=en|volume=502|issue=7471|pages=377–380|doi=10.1038/nature12508|pmid=23995691|pmc=3933266|bibcode=2013Natur.502..377M|issn=1476-4687}}</ref>.
In her faculty position, the bulk of Engelhardt's research focused on developing latent variable models and exploratory data analysis for genomic data,<ref>{{Cite journal|last1=Gao|first1=Chuan|last2=McDowell|first2=Ian C.|last3=Zhao|first3=Shiwen|last4=Brown|first4=Christopher D.|last5=Engelhardt|first5=Barbara E.|date=2016-07-28|editor-last=Zhou|editor-first=Xianghong Jasmine|title=Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering|journal=PLOS Computational Biology|language=en|volume=12|issue=7|pages=e1004791|doi=10.1371/journal.pcbi.1004791|issn=1553-7358|pmc=4965098|pmid=27467526|bibcode=2016PLSCB..12E4791G}}</ref> and also on statistical models for association testing in expression [[Quantitative trait locus|QTLs]].<ref>{{Cite journal|last1=Dumitrascu|first1=Bianca|last2=Darnell|first2=Gregory|last3=Ayroles|first3=Julien|last4=Engelhardt|first4=Barbara E|date=2019-01-15|editor-last=Hancock|editor-first=John|title=Statistical tests for detecting variance effects in quantitative trait studies|url=https://academic.oup.com/bioinformatics/article/35/2/200/5050024|journal=Bioinformatics|language=en|volume=35|issue=2|pages=200–210|doi=10.1093/bioinformatics/bty565|issn=1367-4803|pmc=6330007|pmid=29982387}}</ref> 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<ref>{{Cite journal|last1=Aguet|first1=François|last2=Brown|first2=Andrew A.|last3=Castel|first3=Stephane E.|last4=Davis|first4=Joe R.|last5=He|first5=Yuan|last6=Jo|first6=Brian|last7=Mohammadi|first7=Pejman|last8=Park|first8=YoSon|last9=Parsana|first9=Princy|last10=Segrè|first10=Ayellet V.|last11=Strober|first11=Benjamin J.|date=October 2017|title=Genetic effects on gene expression across human tissues|journal=Nature|language=en|volume=550|issue=7675|pages=204–213|doi=10.1038/nature24277|pmid=29022597|pmc=5776756|bibcode=2017Natur.550..204A|issn=1476-4687}}</ref> and v8 data<ref>{{Cite journal|last=The GTEx Consortium|date=2020-09-11|title=The GTEx Consortium atlas of genetic regulatory effects across human tissues|journal=Science|language=en|volume=369|issue=6509|pages=1318–1330|doi=10.1126/science.aaz1776|issn=0036-8075|pmc=7737656|pmid=32913098|bibcode=2020Sci...369.1318.}}</ref>.


In her faculty position, the bulk of Engelhardt's research focused on developing latent variable models and exploratory data analysis for genomic data,<ref>{{Cite journal|last1=Gao|first1=Chuan|last2=McDowell|first2=Ian C.|last3=Zhao|first3=Shiwen|last4=Brown|first4=Christopher D.|last5=Engelhardt|first5=Barbara E.|date=2016-07-28|editor-last=Zhou|editor-first=Xianghong Jasmine|title=Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering|journal=PLOS Computational Biology|language=en|volume=12|issue=7|pages=e1004791|doi=10.1371/journal.pcbi.1004791|issn=1553-7358|pmc=4965098|pmid=27467526|bibcode=2016PLSCB..12E4791G}}</ref> and also on statistical models for association testing in expression QTLs.<ref>{{Cite journal|last1=Dumitrascu|first1=Bianca|last2=Darnell|first2=Gregory|last3=Ayroles|first3=Julien|last4=Engelhardt|first4=Barbara E|date=2019-01-15|editor-last=Hancock|editor-first=John|title=Statistical tests for detecting variance effects in quantitative trait studies|url=https://academic.oup.com/bioinformatics/article/35/2/200/5050024|journal=Bioinformatics|language=en|volume=35|issue=2|pages=200–210|doi=10.1093/bioinformatics/bty565|issn=1367-4803|pmc=6330007|pmid=29982387}}</ref> 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<ref>{{Cite journal|last1=Aguet|first1=François|last2=Brown|first2=Andrew A.|last3=Castel|first3=Stephane E.|last4=Davis|first4=Joe R.|last5=He|first5=Yuan|last6=Jo|first6=Brian|last7=Mohammadi|first7=Pejman|last8=Park|first8=YoSon|last9=Parsana|first9=Princy|last10=Segrè|first10=Ayellet V.|last11=Strober|first11=Benjamin J.|date=October 2017|title=Genetic effects on gene expression across human tissues|journal=Nature|language=en|volume=550|issue=7675|pages=204–213|doi=10.1038/nature24277|pmid=29022597|pmc=5776756|bibcode=2017Natur.550..204A|issn=1476-4687}}</ref> and v8 data<ref>{{Cite journal|last=The GTEx Consortium|date=2020-09-11|title=The GTEx Consortium atlas of genetic regulatory effects across human tissues|journal=Science|language=en|volume=369|issue=6509|pages=1318–1330|doi=10.1126/science.aaz1776|issn=0036-8075|pmc=7737656|pmid=32913098|bibcode=2020Sci...369.1318.}}</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|url=https://doi.org/10.1186/s12859-020-03625-z|journal=BMC Bioinformatics|volume=21|issue=1|pages=324|doi=10.1186/s12859-020-03625-z|issn=1471-2105|pmc=7374962|pmid=32693778}}</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|url=https://doi.org/10.1186/s12911-020-1069-4|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}}</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.'
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|url=https://doi.org/10.1186/s12859-020-03625-z|journal=BMC Bioinformatics|volume=21|issue=1|pages=324|doi=10.1186/s12859-020-03625-z|issn=1471-2105|pmc=7374962|pmid=32693778}}</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|url=https://doi.org/10.1186/s12911-020-1069-4|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}}</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 titled: '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>
<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>


Revision as of 10:34, 16 August 2021

Barbara Engelhardt is a Professor in the Department of Computer Science at Princeton University.[1] Her research involves the development of statistical and machine learning models for the analysis of biomedical data.


Academic background

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.[2]  She worked as a postdoctoral researcher at the University of Chicago in the Department of Human Genetics with Matthew Stephens from 2008-2011.[3]  She joined Duke University in 2011 as an Assistant Professor in the Biostatistics and Bioinformatics Department. She moved to Princeton University as an Assistant Professor in 2014 and received a promotion to Associate Professor with tenure in 2017.[4] As of 2021, she holds the rank of Professor.[5]

Research and work

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[6]. As a graduate student at Berkeley, she developed statistical models for protein function annotation and statistical frameworks for reasoning about ontologies[7][8]. During her postdoctoral research, she developed sparse factor analysis models for population structure[9] and Bayesian models for association testing[10].

In her faculty position, the bulk of Engelhardt's research focused on developing latent variable models and exploratory data analysis for genomic data,[11] and also on statistical models for association testing in expression QTLs.[12] 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[13] and v8 data[14].

Post tenure, Engelhardt's research in these latent variable models has expanded to include single cell sequencing, with a particular focus on spatial transcriptomics[15].  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[16]. Her work has also expanded into machine learning for electronic healthcare records.[17][18]

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.' [19]

Honors and awards

Engelhardt’s research has been funded by the NIH through two R01s and a number of other mechanisms. Engelhardt  has been recognized by several awards including an Alfred P. Sloan Fellowship in Computational Biology[20], an NSF CAREER Award[21], two Chan-Zuckerberg Initiative grants for the Human Cell Atlas[22], and a FastGrant for her recent work on Covid-19[23]. She is the winner of the 2021 ISCB Overton Prize[24].

Engelhardt's postdoctoral work was partly funded through an NIH NHGRI K99 grant[25], and her PhD was partly funded through an NSF Graduate Research Fellowship and the Google Anita Borg Scholarship in 2005[26]. She received SMBE's Walter M. Fitch Prize in 2004[27].

Service

Engelhardt served on the Board of Directors (2014-2017) and the Senior Advisory Council (2017-present) for Women in Machine Learning[28]. She is the Diversity & Inclusion Co-Chair at the International Conference on Machine Learning (ICML, 2018-2022)[29]. In 2019, she was a member of the NIH Advisory Committee to the Director, Working Group on Artificial Intelligence[30].

References

  1. ^ "Princeton BEEHIVE". beehive.cs.princeton.edu. Retrieved 2021-01-11.
  2. ^ "Michael I. Jordan's Home Page". people.eecs.berkeley.edu. Retrieved 2021-01-11.
  3. ^ "Stephens Lab". stephenslab.uchicago.edu. Retrieved 2021-01-11.
  4. ^ "Eleven Women Faculty Members Who Have Been Assigned New Duties". Women In Academia Report. 2018-03-08. Retrieved 2021-01-11.
  5. ^ "Barbara Engelhardt". Computer Science Department at Princeton. Retrieved 2021-08-16.
  6. ^ "3cs | AIG". sensorwebs.jpl.nasa.gov. Retrieved 2021-01-11.
  7. ^ 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  8. ^ 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.
  9. ^ 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  10. ^ 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.
  11. ^ 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  12. ^ 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.
  13. ^ 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.
  14. ^ 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.
  15. ^ 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  16. ^ 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.
  17. ^ 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  18. ^ 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.
  19. ^ "A Statistical Search for Genomic Truths". 27 February 2018.
  20. ^ "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.
  21. ^ "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.
  22. ^ "Grants". Chan Zuckerberg Initiative. Retrieved 2021-01-11.
  23. ^ "Fast Grants". fastgrants.org. Retrieved 2021-01-11.
  24. ^ "Overton Prize". www.iscb.org.
  25. ^ "NHGRI supports seven young investigators on research career paths". Genome.gov. Retrieved 2021-01-11.
  26. ^ "2005 Google Anita Borg Memorial Scholarship Winners Announced – News announcements – News from Google – Google". googlepress.blogspot.com. Retrieved 2021-01-11.
  27. ^ The Society for Molecular Biology & Evolution. "The Walter M. Fitch Award". www.smbe.org. Retrieved 2021-01-11.
  28. ^ "Senior Advisory Council". Retrieved 2021-01-11.
  29. ^ "2021 Conference". icml.cc. Retrieved 2021-01-11.
  30. ^ "ACD Working Group on Artificial Intelligence". NIH Advisory Committee to the Director. Retrieved 2021-01-11.
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