Publications

I have published a number of papers with talented undergraduate students.

Preprints

  • Jones, A., Townes, W. F., Li, D., & Engelhardt, B. E.. (2022). Alignment of spatial genomics and histology data using deep Gaussian processes. .
    [BibTeX] [Download PDF]
    @unpublished{Jones2022,
    archivePrefix = {bioRxiv},
    year = {2022},
    author = {Jones, Andrew and Townes, F William and Li, Didong and Engelhardt, Barbara E.},
    eprint = {475692},
    journal = {{Annals of Applied Statistics (AOAS; accepted)}},
    title = {{Alignment of spatial genomics and histology data using deep Gaussian processes}},
    url = {https://www.biorxiv.org/content/10.1101/2022.01.10.475692v1}
    }

  • Gewirtz, A. D., Townes, W. F., & Engelhardt, B. E.. (2021). Telescoping bimodal latent Dirichlet allocation to identify expression QTLs across tissues. Cold spring harbor laboratory.
    [BibTeX] [Download PDF]
    @unpublished{gewirtz2021,
    title={{Telescoping bimodal latent Dirichlet allocation to identify expression QTLs across tissues}},
    author={Gewirtz, Ariel DH and Townes, F William and Engelhardt, Barbara E},
    journal={bioRxiv},
    eprint = {466156},
    year={2021},
    publisher={Cold Spring Harbor Laboratory},
    url = {https://www.biorxiv.org/content/10.1101/2021.10.27.466156v2}
    }

  • Dumitrascu, B., Feng, K., & Engelhardt, B. E.. GT-TS: Experimental design for maximizing cell type discovery in single-cell data. .
    [BibTeX] [Download PDF]
    @unpublished{Dumitrascu2018b,
    archivePrefix = {bioRxiv},
    arxivId = {386540},
    author = {Dumitrascu, Bianca and Feng, Karen and Engelhardt, Barbara E.},
    eprint = {386540},
    journal = {bioRxiv: 386540},
    month = may,
    pages = {1--14},
    title = {{GT-TS: Experimental design for maximizing cell type discovery in single-cell data}},
    url = {https://www.biorxiv.org/content/early/2018/08/07/386540}
    }

  • Chaney, A. J. B., Verma, A., Lee, Y., & Engelhardt, B. E.. Nonparametric deconvolution models. .
    [BibTeX] [Download PDF]
    @unpublished{Chaney2020,
    archivePrefix = {arXiv},
    arxivId = {2003.07718},
    author = {Chaney, Allison J.B. and Verma, Archit and Lee, Young-suk and Engelhardt, Barbara E.},
    eprint = {2003.07718},
    journal = {arXiv preprint arXiv:2003.07718},
    title = {{Nonparametric deconvolution models}},
    url = {arxiv.org/abs/2003.07718}
    }

  • Gao, C., Brown, C. D., & Engelhardt, B. E.. A latent factor model with a mixture of sparse and dense factors to model gene expression data affected by technical and biological covariates. .
    [BibTeX] [Download PDF]
    @unpublished{Gao2014a,
    archivePrefix = {arXiv},
    arxivId = {arXiv:1310.4792v1},
    author = {Gao, Chuan and Brown, Christopher D and Engelhardt, Barbara E},
    eprint = {arXiv:1310.4792v1},
    journal = {arXiv preprint arXiv:1310.4792},
    pages = {1--28},
    url = {http://arxiv.org/abs/1310.4792v1},
    title = {A latent factor model with a mixture of sparse and dense factors to model gene expression data affected by technical and biological covariates}
    }

  • Verma, A., & Engelhardt, B. E.. A Bayesian nonparametric semi-supervised model for integration of multiple single-cell experiments. .
    [BibTeX] [Download PDF]
    @unpublished{Verma2020,
    archivePrefix = {bioRxiv},
    author = {Verma, Archit and Engelhardt, Barbara E.},
    eprint = {906313},
    journal = {bioRxiv:906313},
    title = {{A Bayesian nonparametric semi-supervised model for integration of multiple single-cell experiments}},
    url = {https://www.biorxiv.org/content/10.1101/2020.01.14.906313v2.full}
    }

  • Grabski, I. N., {De Vito}, R., & Engelhardt, B. E.. Bayesian ordinal quantile regression with a partially collapsed Gibbs sampler. .
    [BibTeX] [Download PDF]
    @unpublished{Grabski2019,
    archivePrefix = {arXiv},
    arxivId = {1911.07099},
    author = {Grabski, Isabella N. and {De Vito}, Roberta and Engelhardt, Barbara E.},
    eprint = {1911.07099},
    journal = {arXiv:1911.07099},
    title = {{Bayesian ordinal quantile regression with a partially collapsed Gibbs sampler}},
    url = {https://arxiv.org/abs/1911.07099}
    }

  • Mandyam, A., Jones, A., Laudanski, K., & Engelhardt, B. E.. Nested policy reinforcement learning. .
    [BibTeX] [Download PDF]
    @unpublished{Mandyam2021,
    archivePrefix = {arXiv},
    arxivId = {},
    author = {Mandyam, Aishwarya and Jones, Andrew and Laudanski, Krzysztof and Engelhardt, Barbara E.},
    eprint = {arXiv:2110.02879},
    journal = {2110.02879},
    title = {{Nested policy reinforcement learning}},
    url = {https://arxiv.org/abs/2110.02879}
    }

  • Li*, D., Jones*, A., & Engelhardt, B. E.. Probabilistic contrastive principal component analysis. .
    [BibTeX] [Download PDF]
    @unpublished{Li2020,
    archivePrefix = {arXiv},
    arxivId = {},
    author = {Li*, Didong and Jones*, Andrew and Engelhardt, Barbara E.},
    eprint = {2012.07977},
    journal = {arXiv:2012.07977},
    title = {{Probabilistic contrastive principal component analysis}},
    url = {https://arxiv.org/abs/2012.07977}
    }

  • Townes, W. F., & Engelhardt, B. E.. Nonnegative spatial factorization. .
    [BibTeX] [Download PDF]
    @unpublished{Townes2021,
    archivePrefix = {arXiv},
    arxivId = {},
    author = {Townes, F. William and Engelhardt, Barbara E.},
    eprint = {arXiv:2110.06122},
    journal = {2110.06122},
    title = {{Nonnegative spatial factorization}},
    url = {https://arxiv.org/abs/2110.06122}
    }

  • {De Vito}, R., Grabski, I. N., Aguiar, D., Schneper, L. M., Verma, A., {Castillo Fernandez}, J., Mitchell, C., Bell, J., McLanahan, S., Notterman, D. A., & Engelhardt, B. E.. Differentially methylated regions and methylation QTLs for teen depression and early puberty in the Fragile Families Child Wellbeing Study. .
    [BibTeX] [Download PDF]
    @unpublished{DeVito2021,
    archivePrefix = {bioRxiv},
    arxivId = {2021.05.20.444959},
    author = {{De Vito}, Roberta and Grabski, Isabella N. and Aguiar, Derek and Schneper, Lisa M. and Verma, Archit and {Castillo Fernandez}, Juan and Mitchell, Colter and Bell, Jordana and McLanahan, Sara and Notterman, Daniel A and Engelhardt, Barbara E.},
    eprint = {2021.05.20.444959},
    journal = {bioRxiv:2021.05.20.444959},
    title = {{Differentially methylated regions and methylation QTLs for teen depression and early puberty in the Fragile Families Child Wellbeing Study}},
    url = {https://www.biorxiv.org/content/10.1101/2021.05.20.444959v1.full.pdf}
    }

Publications

  • Martinet, G., Strzalkowski, A., & Engelhardt, B. E.. (2022). Variance minimization in the Wasserstein space for invariant causal prediction. Artificial Intelligence and Statistics (AISTATS; accepted).
    [BibTeX] [Download PDF]
    @article{Martinet2022,
    author = {Martinet, Guillaume and Strzalkowski, Alexander and Engelhardt, Barbara E.},
    eprint = {arXiv:2110.07064},
    journal = {{Artificial Intelligence and Statistics (AISTATS; accepted)}},
    year = {2022},
    title = {{Variance minimization in the Wasserstein space for invariant causal prediction}},
    url = {https://arxiv.org/abs/2110.07064}
    }

  • Jones, A., Townes, W. F., Li, D., & Engelhardt, B. E.. (2022). Contrastive latent variable modeling with application to case-control sequencing experiments. Annals of Applied Statistics (AOAS; accepted).
    [BibTeX] [Download PDF]
    @article{Jones2022a,
    archivePrefix = {arXiv},
    year = {2022},
    author = {Jones, Andrew and Townes, F William and Li, Didong and Engelhardt, Barbara E.},
    eprint = {2102.06731},
    journal = {{Annals of Applied Statistics (AOAS; accepted)}},
    title = {{Contrastive latent variable modeling with application to case-control sequencing experiments}},
    url = {https://arxiv.org/abs/2102.06731}
    }

  • Cui, S., Yoo, E. C., Li, D., Laudanski, K., & Engelhardt, B. E.. (2022). Hierarchical Gaussian processes and mixtures of experts to model Covid-19 patient trajectories. Proceedings of the Pacific Symposium on Biocomputing (PSB).
    [BibTeX] [Download PDF]
    @article{Cui2022,
    author = {Cui, Sunny and Yoo, Elizabeth C. and Li, Didong and Laudanski, Krzysztof and Engelhardt, Barbara E.},
    journal = {{Proceedings of the Pacific Symposium on Biocomputing (PSB)}},
    year = {2022},
    title = {{Hierarchical Gaussian processes and mixtures of experts to model Covid-19 patient trajectories}},
    url = {http://psb.stanford.edu/psb-online/proceedings/psb22/cui.pdf}
    }

  • Ash*, J., Darnell*, G., Munro*, D., & Engelhardt, B. E.. (2021). Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology. Nature Communications, 12(1609).
    [BibTeX] [Download PDF]
    @article{Ash2021,
    archivePrefix = {bioRxiv},
    author = {Ash*, Jordan and Darnell*, Gregory and Munro*, Daniel and Engelhardt, Barbara E.},
    eprint = {458711},
    journal = {{Nature Communications}},
    year = {2021},
    volume={12},
    number={1609},
    title = {{Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology}},
    url = {https://www.nature.com/articles/s41467-021-21727-x}
    }

  • Dumitrascu, B., Villar, S., Mixon, D. G., & Engelhardt, B. E.. (2021). Optimal marker gene selection for cell type discrimination in single cell analyses. Nature Communications, 12(1186).
    [BibTeX] [Download PDF]
    @article{Dumitrascu2021,
    author = {Dumitrascu, Bianca and Villar, Soledad and Mixon, Dustin G. and Engelhardt, Barbara E.},
    eprint = {599654},
    year = {2021},
    volume={12},
    number={1186},
    journal = {{Nature Communications}},
    title = {{Optimal marker gene selection for cell type discrimination in single cell analyses}},
    url = {https://www.nature.com/articles/s41467-021-21453-4}
    }

  • Lu, J., Dumitrascu, B., McDowell, I. C., Barrera, A., K., H. L., Leichter, S. M., Reddy, T. E., & Engelhardt, B. E.. (2021). Causal network inference from gene transcriptional time series response to glucocorticoids. PLoS Computational Biology, 17(1), e1008223.
    [BibTeX] [Download PDF]
    @article{Lu2021,
    archivePrefix = {bioRxiv},
    arxivId = {587170},
    author = {Lu, Jonathan and Dumitrascu, Bianca and McDowell, Ian C. and Barrera, Alejandro and Hong Linda K. and Leichter, Sarah M. and Reddy, Timothy E. and Engelhardt, Barbara E.},
    eprint = {587170},
    journal = {{PLoS Computational Biology}},
    year = {2021},
    volume={17},
    number= {1},
    pages={e1008223},
    title = {{Causal network inference from gene transcriptional time series response to glucocorticoids}},
    url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008223}
    }

  • Gundersen, G. W., Cai, D., Zhou, C., Engelhardt, B. E., & Adams, R. P.. (2021). Active multi-fidelity Bayesian online changepoint detection. Uncertainty in Artifical Intelligence (UAI).
    [BibTeX] [Download PDF]
    @article{Gundersen2021b,
    archivePrefix = {arXiv},
    author = {Gundersen, Gregory W. and Cai, Diana and Zhou, Chuteng and Engelhardt, Barbara E. and Adams, Ryan P.},
    year = {2021},
    journal = {{Uncertainty in Artifical Intelligence (UAI)}},
    title = {{Active multi-fidelity Bayesian online changepoint detection}},
    url = {https://www.auai.org/uai2021/pdf/uai2021.718.pdf}
    }

  • Wu, A., Nastase, S. A., Baldassano, C. A., Turk-Browne, N. B., Norman, K. A., Engelhardt, B. E., & Pillow, J. W.. (2021). Brain kernel: a new spatial covariance function for fMRI data. NeuroImage, 15(245), 118580.
    [BibTeX] [Download PDF]
    @article{Wu2021,
    archivePrefix = {bioRiv},
    author = {Wu, Anqi and Nastase, Samuel A. and Baldassano, Christopher A. and Turk-Browne, Nicholas B. and Norman, Kenneth A. and Engelhardt, Barbara E. and Pillow, Jonathan W.},
    volume={15},
    number={245},
    pages={118580},
    year = {2021},
    journal = {{NeuroImage}},
    title = {{Brain kernel: a new spatial covariance function for fMRI data}},
    url = {https://pubmed.ncbi.nlm.nih.gov/34740792/}
    }

  • Verma, A., Jena, S., Isakov, D. R., Aoki, K., Toettcher, J. E., & Engelhardt, B. E.. (2021). A self-exciting point process to study multi-cellular spatial signaling patterns. Proceedings of the National Academy of Sciences (PNAS), 118, e2026123118.
    [BibTeX] [Download PDF]
    @article{Verma2021,
    author = {Verma, Archit and Jena, Siddhartha and Isakov, Danielle R. and Aoki, Kazuhiro and Toettcher, Jared E. and Engelhardt, Barbara E.},
    journal = {{Proceedings of the National Academy of Sciences (PNAS)}},
    title = {{A self-exciting point process to study multi-cellular spatial signaling patterns}},
    year = {2021},
    volume = {118},
    issue = {32},
    pages = {e2026123118},
    url = {https://www.pnas.org/content/118/32/e2026123118/tab-article-info}
    }

  • Mandyam, A., Yoo, E. C., Soules, J., Laudanski, K., & Engelhardt, B. E.. (2021). COP-E-CAT: Cleaning and organization pipeline for EHR computational and analytic tasks. Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB).
    [BibTeX] [Download PDF]
    @article{Mandyam2021,
    author = {Mandyam, Aishwarya and Yoo, Elizabeth C. and Soules, Jeff and Laudanski, Krzysztof and Engelhardt, Barbara E.},
    journal = {{Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB)}},
    year = {2021},
    title = {{COP-E-CAT: Cleaning and organization pipeline for EHR computational and analytic tasks}},
    url = {https://dl.acm.org/doi/10.1145/3459930.3469536}
    }

  • Gundersen, G. W., Zhang, M. M., & Engelhardt, B. E.. (2021). Latent variable modeling with random features. Artificial Intelligence and Statistics (AISTATS).
    [BibTeX] [Download PDF]
    @article{Gundersen2021,
    archivePrefix = {arXiv},
    author = {Gundersen, Gregory W. and Zhang, Michael Minyi and Engelhardt, Barbara E.},
    year = {2021},
    eprint = {2006.11145},
    journal = {{Artificial Intelligence and Statistics (AISTATS)}},
    title = {{Latent variable modeling with random features}},
    url = {https://arxiv.org/abs/2006.11145}
    }

  • Prasad, N., Engelhardt, B. E., & Doshi-Velez, F.. (2020). Defining admissible rewards for high confidence policy evaluation. ACM Conference on Health, Inference, and Learning (CHIL).
    [BibTeX] [Download PDF]
    @article{Prasad2020,
    archivePrefix = {arXiv},
    arxivId = {1905.13167},
    author = {Prasad, Niranjani and Engelhardt, Barbara E. and Doshi-Velez, Finale},
    eprint = {1905.13167},
    journal = {{ACM Conference on Health, Inference, and Learning (CHIL)}},
    year = {2020},
    url = {https://dl.acm.org/doi/abs/10.1145/336850},
    title = {{Defining admissible rewards for high confidence policy evaluation}}
    }

  • Cheng, L., Dumitrascu, B., Zhang, M. M., Chivers, C., Draugelis, M. E., Li, K., & Engelhardt, B. E.. (2020). Patient-specific effects of medication using latent force models with Gaussian processes. Artificial Intelligence and Statistics (AISTATS).
    [BibTeX] [Download PDF]
    @article{Cheng2020b,
    archivePrefix = {arXiv},
    arxivId = {1906.00226},
    author = {Cheng, Li-Fang and Dumitrascu, Bianca and Zhang, Michael Minyi and Chivers, Corey and Draugelis, Michael E. and Li, Kai and Engelhardt, Barbara E.},
    eprint = {1906.00226},
    year = {2020},
    journal = {{Artificial Intelligence and Statistics (AISTATS)}},
    title = {{Patient-specific effects of medication using latent force models with Gaussian processes}},
    url = {http://proceedings.mlr.press/v108/cheng20c/cheng20c.pdf}
    }

  • Salganik, M., & others. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. Proceedings of the National Academy of Sciences (PNAS).
    [BibTeX] [Download PDF]
    @article{Salganik2020,
    author = {Salganik, Matthew and others},
    title = {Measuring the predictability of life outcomes with a scientific mass collaboration},
    year = {2020},
    journal = {{Proceedings of the National Academy of Sciences (PNAS)}},
    url = {https://www.pnas.org/content/117/15/8398}
    }

  • Cheng, L., Darnell, G., Chivers, C., Draugelis, M. E., Li, K., & Engelhardt, B. E.. (2020). Sparse multi-output Gaussian processes for medical time series prediction. BMC Medical Informatics and Decision Making, 20(152).
    [BibTeX] [Download PDF]
    @article{Cheng2020,
    archivePrefix = {arXiv},
    arxivId = {1703.09112},
    author = {Cheng, Li-Fang and Darnell, Gregory and Chivers, Corey and Draugelis, Michael E. and Li, Kai and Engelhardt, Barbara E.},
    eprint = {1703.09112},
    journal = {{BMC Medical Informatics and Decision Making}},
    year = {2020},
    volume={20},
    number={152},
    title = {{Sparse multi-output Gaussian processes for medical time series prediction}},
    url = {https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-1069-4}
    }

  • Camerlenghi*, F., Dumitrascu*, B., Ferrari, F., Engelhardt, B. E., & Favaro, S.. (2020). Nonparametric Bayesian multi-armed bandits for single cell experiment design. Annals of Applied Statistics (AOAS).
    [BibTeX] [Download PDF]
    @article{Dumitrascu2019b,
    archivePrefix = {arXiv},
    arxivId = {1910.05355},
    author = {Camerlenghi*, Federico and Dumitrascu*, Bianca and Ferrari, Federico and Engelhardt, Barbara E. and Favaro, Stefano},
    eprint = {1910.05355},
    year = {2020},
    journal = {{Annals of Applied Statistics (AOAS)}},
    title = {{Nonparametric Bayesian multi-armed bandits for single cell experiment design}},
    url = {https://projecteuclid.org/euclid.aoas/1608346909}
    }

  • Oliva, M., GTEx Consortium, & others. (2020). The impact of sex on gene expression across human tissues. Science, 369. doi:10.1126/science.aba3066
    [BibTeX] [Download PDF]
    @article{Oliva2020,
    author = {Oliva, Meritxell and {GTEx Consortium} and others},
    journal = {{Science}},
    year = {2020},
    volume = {369},
    issue = {6509},
    doi = {10.1126/science.aba3066},
    title = {{The impact of sex on gene expression across human tissues}},
    url = {https://science.sciencemag.org/content/369/6509/eaba3066}
    }

  • GTEx Consortium. (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science, 369, 1318–1330. doi:10.1126/science.aaz1776
    [BibTeX] [Download PDF]
    @article{GTEx2020,
    author = {{GTEx Consortium}},
    journal = {{Science}},
    year = {2020},
    volume = {369},
    issue = {6509},
    pages = {1318--1330},
    doi = {10.1126/science.aaz1776},
    title = {{The GTEx Consortium atlas of genetic regulatory effects across human tissues}},
    url = {https://science.sciencemag.org/content/369/6509/1318}
    }

  • Gill, D., Arvanitis, M., Carter, P., Cordero, A. H. I., Jo, B., Karhunen, V., Larsson, S. C., Li, X., Lockhart, S. M., Mason, A., Pashos, E., Saha, A., Tan, V. Y., Zuber, V., Bossé, Y., Fahle, S., Hao, K., Jiang, T., Joubert, P., Lunt†, A. C., Ouwehand, W. H., Roberts, D. J., Timens, W., van den Berge, M., Watkins, N. A., Battle, A., Butterworth, A. S., Danesh, J., Angelantonio, E. D., Engelhardt, B. E., Peters, J. E., Sin, D. D., & Burgess, S.. (2020). ACE inhibition and cardiometabolic risk factors, lung ACE2 and TMPRSS2 gene expression, and plasma ACE2 levels: a Mendelian randomization study. Royal Society of Open Science, 7, 200958.
    [BibTeX] [Download PDF]
    @article{Gill2020,
    author = {Dipender Gill and Marios Arvanitis and Paul Carter and Ana I. Hernández Cordero and Brian Jo and Ville Karhunen and Susanna C. Larsson and Xuan Li and Sam M. Lockhart and Amy Mason and Evanthia Pashos and Ashis Saha and Vanessa Y. Tan and Verena Zuber and Yohan Bossé and Sarah Fahle and Ke Hao and Tao Jiang and Philippe Joubert and Alan C. Lunt† and Willem Hendrik Ouwehand and David J. Roberts and Wim Timens and Maarten van den Berge and Nicholas A. Watkins and Alexis Battle and Adam S. Butterworth and John Danesh and Emanuele Di Angelantonio and Barbara E. Engelhardt and James E. Peters and Don D. Sin and Stephen Burgess},
    year = {2020},
    journal = {{Royal Society of Open Science}},
    volume = {7},
    pages = {200958},
    title = {{ACE inhibition and cardiometabolic risk factors, lung ACE2 and TMPRSS2 gene expression, and plasma ACE2 levels: a Mendelian randomization study}},
    url = {https://royalsocietypublishing.org/doi/10.1098/rsos.200958}
    }

  • Verma, A., & Engelhardt, B. E.. (2020). A robust nonlinear low-dimensional manifold for single cell RNA-seq data. BMC Bioinformatics, 21(324).
    [BibTeX] [Download PDF]
    @article{Verma2020,
    archivePrefix = {bioRxiv},
    author = {Verma, Archit and Engelhardt, Barbara E.},
    eprint = {443044},
    year = {2020},
    journal = {{BMC Bioinformatics}},
    volume = {21},
    number = {324},
    title = {{A robust nonlinear low-dimensional manifold for single cell RNA-seq data}},
    url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03625-z}
    }

  • Elyanow, R., Dumitrascu, B., Engelhardt, B. E., & Raphael, B. J.. (2020). netNMF-sc: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis. Genome Research, 20(2), 195–204.
    [BibTeX] [Download PDF]
    @article{Elyanow2020,
    author = {Elyanow, Rebecca and Dumitrascu, Bianca and Engelhardt, Barbara E. and Raphael, Benjamin J.},
    title = {{netNMF-sc: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis}},
    year = {2020},
    volume = {20},
    number = {2},
    pages = {195--204},
    journal = {Genome {R}esearch},
    url = {https://genome.cshlp.org/content/early/2020/01/28/gr.251603.119}
    }

  • Cheng, L., Prasad, N., & Engelhardt, B. E.. (2019). An optimal policy for patient laboratory tests in intensive care units. Proceedings of the Pacific Symposium on Biocomputing (PSB).
    [BibTeX] [Download PDF]
    @article{Cheng2019,
    archivePrefix = {arXiv},
    arxivId = {1808.04679},
    author = {Cheng, Li-Fang and Prasad, Niranjani and Engelhardt, Barbara E.},
    eprint = {1808.04679},
    journal = {{Proceedings of the Pacific Symposium on Biocomputing (PSB)}},
    year = {2019},
    title = {{An optimal policy for patient laboratory tests in intensive care units}},
    url = {https://psb.stanford.edu/psb-online/proceedings/psb19/cheng_l.pdf}
    }

  • Guan, G., & Engelhardt, B. E.. (2019). Predicting sick patient volume in a pediatric outpatient setting using time series analysis. Proceedings of Machine Learning for Health Care (MLHC).
    [BibTeX] [Download PDF]
    @article{Guan2019,
    author = {Guan, Grace and Engelhardt, Barbara E.},
    title = {{Predicting sick patient volume in a pediatric outpatient setting using time series analysis}},
    year = {2019},
    journal = {{Proceedings of Machine Learning for Health Care (MLHC)}},
    url = {https://static1.squarespace.com/static/59d5ac1780bd5ef9c396eda6/t/5d473093f352bf0001304366/1564946582724/Guan.pdf}
    }

  • Elyanow, R., Dumitrascu, B., Engelhardt, B. E., & Raphael, B. J.. (2019). netNMF-sc: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis. Proceedings of the 23rd International Conference on Research in Computational Molecular Biology (RECOMB).
    [BibTeX] [Download PDF]
    @article{Elyanow2019,
    author = {Elyanow, Rebecca and Dumitrascu, Bianca and Engelhardt, Barbara E. and Raphael, Benjamin J.},
    title = {{netNMF-sc: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis}},
    year = {2019},
    journal = {{Proceedings of the 23rd International Conference on Research in Computational Molecular Biology (RECOMB)}},
    url = {https://www.biorxiv.org/content/10.1101/544346}
    }

  • Gundersen, G., Dumitrascu, B., & Engelhardt, B. E.. (2019). End-to-end training of deep probabilistic CCA on paired biomedical observations. in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI).
    [BibTeX] [Download PDF]
    @article{Gundersen2019,
    author = {Gundersen, Gregory and Dumitrascu, Bianca and Engelhardt, Barbara E.},
    title = {{End-to-end training of deep probabilistic CCA on paired biomedical observations}},
    journal = {{in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI)}},
    year = 2019,
    url = {http://proceedings.mlr.press/v115/gundersen20a.html}
    }

  • McDowell, I. C., Manandhar, D., Vockley, C. M., Schmid, A., Reddy*, T. E., & Engelhardt*, B. E.. (2018). Clustering gene expression time series data using an infinite Gaussian process mixture model. PLoS Computational Biology, 14(1), e1005896.
    [BibTeX] [Download PDF]
    @article{McDowell2018,
    archivePrefix = {bioRiv},
    arxivId = {131151},
    author = {McDowell, Ian C. and Manandhar, Dinesh and Vockley, Christopher M. and Schmid, Amy and Reddy*, Timothy E. and Engelhardt*, Barbara E.},
    pages = {e1005896},
    volume={14},
    number={1},
    year = {2018},
    journal = {{PLoS Computational Biology}},
    title = {{Clustering gene expression time series data using an infinite Gaussian process mixture model}},
    url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005896}
    }

  • Dumitrascu, B., Darnell, G., Ayroles, J., & Engelhardt, B. E.. (2018). A Bayesian test to identify variance effects. Bioinformatics, bty565.
    [BibTeX] [Download PDF]
    @article{Dumitrascu2018c,
    author = {Dumitrascu, Bianca and Darnell, Gregory and Ayroles, Julien and Engelhardt, Barbara E},
    journal = {Bioinformatics},
    pages = {bty565},
    year={2018},
    title = {{A Bayesian test to identify variance effects}},
    url = {https://doi.org/10.1093/bioinformatics/bty565}
    }

  • McDowell, I. C., Barrero, A., D’Ippolito, A. M., Vockley, C. M., Hong, L. K., Leichter, S. M., Bartelt, L. C., Majoros, W. H., Song, L., Safi, A., Koçak, D. D., Gersbach, C. A., Hartemink, A. J., Crawford, G. E., Engelhardt, B. E., & Reddy, T. E.. (2018). Glucocorticoid receptor recruits to enhancers and drives activation by motif-directed binding. Genome Research.
    [BibTeX] [Download PDF]
    @article{McDowell2018b,
    author = {McDowell, Ian C. and Barrero, Alejandro and D'Ippolito, Anthony M. and Vockley, Christopher M. and Hong, Linda K. and Leichter, Sarah M. and Bartelt, Luke C. and Majoros, William H. and Song, Lingyun and Safi, Alexias and Koçak, D. Dewran and Gersbach, Charles A. and Hartemink, Alexander J. and Crawford, Gregory E. and Engelhardt, Barbara E. and Reddy, Timothy E.},
    year = {2018},
    journal = {{Genome Research}},
    title = {{Glucocorticoid receptor recruits to enhancers and drives activation by motif-directed binding}},
    url = {https://genome.cshlp.org/content/early/2018/08/10/gr.233346.117}
    }

  • Aguiar, D., Cheng, L., Dumitrascu, B., Mordelet, F., Pai, A. A., & Engelhardt, B. E.. (2018). BIISQ: Bayesian nonparametric discovery of Isoforms and Individual Specific Quantification. Nature Communications, 9(1681).
    [BibTeX] [Download PDF]
    @article{Aguiar2018,
    arxivId = {1703.08260},
    author = {Aguiar, Derek and Cheng, Li-Fang and Dumitrascu, Bianca and Mordelet, Fantine and Pai, Athma A. and Engelhardt, Barbara E.},
    eprint = {1703.08260},
    journal = {{Nature Communications}},
    year = {2018},
    month = mar,
    volume={9},
    number = {1681},
    title = {{BIISQ: Bayesian nonparametric discovery of Isoforms and Individual Specific Quantification}},
    url = {https://www.nature.com/articles/s41467-018-03402-w}
    }

  • Chaney, A. J. B., Stewart, B. M., & Engelhardt, B. E.. (2018). How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. 12th ACM Conference on Recommender Systems.
    [BibTeX] [Download PDF]
    @article{Chaney2018,
    archivePrefix = {arXiv},
    arxivId = {1710.11214},
    author = {Chaney, Allison J.B. and Stewart, Brandon M. and Engelhardt, Barbara E.},
    eprint = {1710.11214},
    year={2018},
    journal = {{12th ACM Conference on Recommender Systems}},
    title = {{How algorithmic confounding in recommendation systems increases homogeneity and decreases utility}},
    url = {http://arxiv.org/abs/1710.11214}
    }

  • Dumitrascu*, B., Feng*, K., & Engelhardt, B. E.. (2018). PG-TS: Improved Thompson sampling for logistic contextual bandits. Proceedings of Neural Information Processing Systems (NeurIPS).
    [BibTeX] [Download PDF]
    @article{Dumitrascu2018,
    archivePrefix = {arXiv},
    arxivId = {1805.07458},
    author = {Dumitrascu*, Bianca and Feng*, Karen and Engelhardt, Barbara E.},
    eprint = {1805.07458},
    journal = {{Proceedings of Neural Information Processing Systems (NeurIPS)}},
    year = {2018},
    title = {{PG-TS: Improved Thompson sampling for logistic contextual bandits}},
    url = {http://papers.nips.cc/paper/7713-pg-ts-improved-thompson-sampling-for-logistic-contextual-bandits}
    }

  • Cohen, J. D., Daw, N., Engelhardt, B., Hasson, U., Li, K., Niv, Y., Norman, K. A., Pillow, J., Ramadge, P. J., Turk-Browne, N. B., & Willke, T. L.. (2017). Computational approaches to fMRI analysis. Nature Neuroscience, 20(3), 304–313.
    [BibTeX] [Download PDF]
    @article{Cohen2017,
    title={{Computational approaches to fMRI analysis}},
    author={Cohen, Jonathan D and Daw, Nathaniel and Engelhardt, Barbara and Hasson, Uri and Li, Kai and Niv, Yael and Norman, Kenneth A and Pillow, Jonathan and Ramadge, Peter J and Turk-Browne, Nicholas B and Willke, Theodore L},
    journal={{Nature Neuroscience}},
    volume={20},
    number={3},
    pages={304--313},
    year={2017},
    url = {https://www.nature.com/neuro/journal/v20/n3/full/nn.4499.html},
    publisher={Nature Research}
    }

  • Srivastava, S., Engelhardt, B. E., & Dunson, D. B.. (2017). Expandable factor analysis. Biometrika, asx030.
    [BibTeX] [Download PDF]
    @article{Srivastava2017,
    author = {Srivastava, Sanvesh and Engelhardt, Barbara E and Dunson, David B},
    pages = {asx030},
    year = {2017},
    url = {https://academic.oup.com/biomet/article-abstract/doi/10.1093/biomet/asx030/3869470/Expandable-factor-analysis?redirectedFrom=fulltext},
    journal = {Biometrika},
    title = {{Expandable factor analysis}}
    }

  • GTEx Consortium, Battle*, A., Brown*, C. D., Engelhardt*, B. E., & Montgomery*, S. M.. (2017). Genetic effects on gene expression across human tissues. Nature, 550, 204–213.
    [BibTeX] [Download PDF]
    @article{GTEx2017,
    title={{Genetic effects on gene expression across human tissues}},
    author = {{GTEx Consortium} and Battle*, Alexis and Brown*, Christopher D. and Engelhardt*, Barbara E. and Montgomery*, Stephen M.},
    year = {2017},
    volume={550},
    pages={204--213},
    journal = {Nature},
    url={http://www.nature.com/articles/nature24277}
    }

  • Zhao, S., Engelhardt, B. E., Mukherjee, S., & Dunson, D. B.. (2017). Fast moment estimation for generalized latent Dirichlet models. Journal of the American Statistical Association (JASA).
    [BibTeX] [Download PDF]
    @article{Zhao2017,
    author = {Zhao, Shiwen and Engelhardt, Barbara E and Mukherjee, Sayan and Dunson, David B},
    journal = {{Journal of the American Statistical Association (JASA)}},
    year = {2017},
    title = {{Fast moment estimation for generalized latent Dirichlet models}},
    url = {http://www.tandfonline.com/doi/full/10.1080/01621459.2017.1341839}
    }

  • Darnell, G., Georgiev, S., Mukherjee, S., & Engelhardt, B. E.. (2017). Adaptive randomized dimension reduction on massive data. Journal of Machine Learning Research (JMLR), 18(140), 1-30.
    [BibTeX] [Download PDF]
    @article{Darnell2017,
    archivePrefix = {arXiv},
    arxivId = {1504.03183},
    author = {Darnell, Gregory and Georgiev, Stoyan and Mukherjee, Sayan and Engelhardt, Barbara E},
    eprint = {1504.03183},
    journal = {{Journal of Machine Learning Research (JMLR)}},
    volume={18},
    number={140},
    year = {2017},
    pages = {1-30},
    title = {{Adaptive randomized dimension reduction on massive data}},
    url = {http://www.jmlr.org/papers/v18/15-143.html}
    }

  • Saha, A., Kim*, Y., Gewirtz*, A. D. H., Jo, B., Gao, C., McDowell, I. C., GTEx Consortium, Engelhardt*, B. E., & Battle*, A.. (2017). Co-expression networks reveal the tissue-specific regulation of transcription and splicing. Genome Research, 27(11), 1843–1858.
    [BibTeX] [Download PDF]
    @article{Saha2017,
    title={{Co-expression networks reveal the tissue-specific regulation of transcription and splicing}},
    author = {Saha, Ashis and Kim*, Yungil and Gewirtz*, Ariel D. H. and Jo, Brian and Gao, Chuan and McDowell, Ian C. and {GTEx Consortium} and Engelhardt*, Barbara E. and Battle*, Alexis},
    year={2017},
    volume= {27},
    number={11},
    pages={1843--1858},
    journal = {{Genome Research}},
    url={http://genome.cshlp.org/content/early/2017/10/06/gr.216721.116.abstract}
    }

  • Prasad, N., Cheng, L., Chivers, C., Draugelis, M., & Engelhardt, B. E.. (2017). A reinforcement learning approach to weaning of mechanical ventilation in intensive care units. Proceedings of Uncertainty in Artificial Intelligence (UAI), 1–9.
    [BibTeX] [Download PDF]
    @article{Prasad2017,
    archivePrefix = {arXiv},
    arxivId = {1704.06300},
    author = {Prasad, Niranjani and Cheng, Li-Fang and Chivers, Corey and Draugelis, Michael and Engelhardt, Barbara E},
    eprint = {1704.06300},
    journal = {{Proceedings of Uncertainty in Artificial Intelligence (UAI)}},
    year = 2017,
    pages = {1--9},
    title = {A reinforcement learning approach to weaning of mechanical ventilation in intensive care units},
    url = {http://auai.org/uai2017/proceedings/papers/209.pdf}
    }

  • Jerfel, G., Basbug, M. E., & Engelhardt, B. E.. (2017). Dynamic collaborative filtering with compound Poisson factorization. Proceedings of Artificial Intelligence and Statistics (AISTATS), 54, 738-747.
    [BibTeX] [Download PDF]
    @article{Jerfel2017,
    archivePrefix = {arXiv},
    arxivId = {1608.04839},
    author = {Jerfel, Ghassen and Basbug, Mehmet E and Engelhardt, Barbara E},
    eprint = {1608.04839},
    journal = {{Proceedings of Artificial Intelligence and Statistics (AISTATS)}},
    month = aug,
    year = {2017},
    volume = {54},
    pages = {738-747},
    title = {{Dynamic collaborative filtering with compound Poisson factorization}},
    url = {http://proceedings.mlr.press/v54/jerfel17a.html}
    }

  • Basbug, M. E., & Engelhardt, B. E.. (2016). Hierarchical compound Poisson factorization. Proceedings of the International Conference on Machine Learning (ICML), 1795–1803.
    [BibTeX] [Download PDF]
    @article{Basbug2016,
    archivePrefix = {arXiv},
    arxivId = {1604.03853},
    author = {Basbug, Mehmet E and Engelhardt, Barbara E},
    eprint = {1604.03853},
    journal = {{Proceedings of the International Conference on Machine Learning (ICML)}},
    year = {2016},
    month = jul,
    pages = {1795–1803},
    title = {{Hierarchical compound Poisson factorization}},
    url = {http://arxiv.org/abs/1604.03853}
    }

  • Gao, C., Zhao, S., McDowell, I. C., Brown, C. D., & Engelhardt, B. E.. (2016). Context-specific and differential gene co-expression networks via Bayesian biclustering models. PLoS Computational Biology, 12, e1004791.
    [BibTeX] [Download PDF]
    @article{Gao2016,
    archivePrefix = {arXiv},
    arxivId = {arXiv:1411.1997v1},
    author = {Gao, Chuan and Zhao, Shiwen and McDowell, Ian C and Brown, Christopher D and Engelhardt, Barbara E},
    eprint = {arXiv:1411.1997v1},
    journal = {{PLoS Computational Biology}},
    title = {{Context-specific and differential gene co-expression networks via Bayesian biclustering models}},
    year={2016},
    volume={12},
    issue={7},
    pages={e1004791},
    url = {http://dx.doi.org/10.1371/journal.pcbi.1004791}
    }

  • Tonner, P. D., Darnell, C. D., Engelhardt, B. E., & Schmid, A.. (2016). Detecting differential growth of microbial populations with Gaussian process regression. Genome Research, 27, 320-333.
    [BibTeX] [Download PDF]
    @article{Tonner2016,
    title={{Detecting differential growth of microbial populations with Gaussian process regression}},
    author = {Tonner, Peter D and Darnell, Cynthia D and Engelhardt, Barbara E and Schmid, Amy},
    url={http://genome.cshlp.org/content/early/2017/01/23/gr.210286.116},
    journal = {{Genome Research}},
    volume = {27},
    pages={320-333},
    year={2016}
    }

  • Zhao, S., Gao, C., Mukherjee, S., & Engelhardt, B. E.. (2016). Bayesian group latent factor analysis with structured sparse priors. Journal of Machine Learning Research (JMLR), (in press).
    [BibTeX] [Download PDF]
    @article{Zhao2016,
    archivePrefix = {arXiv},
    arxivId = {arXiv:1411.2698v1},
    author = {Zhao, Shiwen and Gao, Chuan and Mukherjee, Sayan and Engelhardt, Barbara E},
    eprint = {arXiv:1411.2698v1},
    journal = {{Journal of Machine Learning Research (JMLR)}},
    pages={(in press)},
    year = {2016},
    title = {{Bayesian group latent factor analysis with structured sparse priors}},
    url = {http://arxiv.org/abs/1411.2698}
    }

  • Engelhardt, B. E., & Brown, C. D.. (2015). Diving deeper to predict noncoding sequence function. Nature Methods (News & Views; not peer-reviewed), 12(10), 925–926.
    [BibTeX] [Download PDF]
    @article{Engelhardt2015,
    title={Diving deeper to predict noncoding sequence function},
    author={Engelhardt, Barbara E and Brown, Christopher D},
    journal={{Nature Methods (News \& Views; not peer-reviewed)}},
    volume={12},
    number={10},
    pages={925--926},
    year={2015},
    url= {http://www.nature.com/nmeth/journal/v12/n10/full/nmeth.3604.html}
    doi= {10.1038/nmeth.3604},
    publisher={Nature Publishing Group}
    }

  • van Den Berg, S. M., de Moor, M. H., Verweij, K. J., Krueger, R. F., Luciano, M., Vasquez, A. A., Matteson, L. K., Derringer, J., Esko, T., Amin, N., & others. (2015). Meta-analysis of genome-wide association studies for extraversion: Findings from the Genetics of Personality Consortium. Behavior Genetics, 1–13. doi:10.1007/s10519-015-9735-5
    [BibTeX] [Download PDF]
    @article{VanDenBerg2015,
    title={{Meta-analysis of genome-wide association studies for extraversion: Findings from the Genetics of Personality Consortium}},
    author={van Den Berg, Stephanie M and de Moor, Marleen HM and Verweij, Karin JH and Krueger, Robert F and Luciano, Michelle and Vasquez, Alejandro Arias and Matteson, Lindsay K and Derringer, Jaime and Esko, T{\~o}nu and Amin, Najaf and others},
    journal = {{Behavior Genetics}},
    pages = {1--13},
    year = {2015},
    publisher = {Springer},
    url = {http://www.cs.princeton.edu/~bee/pubs/vandenBerg-BehavGenet-2015.pdf},
    doi = {10.1007/s10519-015-9735-5},
    PubMedID = {26362575}
    }

  • Zhang, W., Spector, T., Deloukas, P., Bell, J., & Engelhardt, B. E.. (2015). Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements. Genome Biology, 16(1), 14. doi:10.1186/s13059-015-0581-9
    [BibTeX] [Download PDF]
    @article{Zhang2015,
    author = {Zhang, Weiwei and Spector, Tim and Deloukas, Panos and Bell, Jordana and Engelhardt, Barbara E},
    title = {{Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements}},
    journal = {{Genome Biology}},
    volume = {16},
    year = {2015},
    number = {1},
    pages = {14},
    url = {http://www.cs.princeton.edu/~bee/pubs/zhang_gb_2015.pdf},
    doi = {10.1186/s13059-015-0581-9},
    PubMedID = {25616342},
    issn = {1465-6906},
    }

  • Mimno, D., Blei, D. M., & Engelhardt, B. E.. (2015). Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure. Proceedings of the National Academy of Sciences (PNAS), 112(26), E3341–50. doi:10.1073/pnas.1412301112
    [BibTeX] [Download PDF]
    @article{Mimno2015,
    author = {Mimno, David and Blei, David M and Engelhardt, Barbara E},
    pages = {E3341--50},
    volume = {112},
    number = {26},
    year = {2015},
    url = {http://www.cs.princeton.edu/~bee/pubs/PNAS-2015-Mimno.pdf},
    doi = {10.1073/pnas.1412301112},
    PubMedID = {26071445},
    journal = {{Proceedings of the National Academy of Sciences (PNAS)}},
    title = {Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure}
    }

  • Genetics of Personality Consortium. (2015). Meta-analysis of genome-wide association studies for neuroticism, and the polygenic association with major depressive disorder. JAMA Psychiatry, 72(7), 642-650. doi:10.1001/jamapsychiatry.2015.0554
    [BibTeX] [Download PDF]
    @article{deMoor2015,
    author = {{Genetics of Personality Consortium}},
    title = {{Meta-analysis of genome-wide association studies for neuroticism, and the polygenic association with major depressive disorder}},
    journal = {{JAMA Psychiatry}},
    year = {2015},
    volume = {72},
    number = {7},
    pages = {642-650},
    url = {http://www.cs.princeton.edu/~bee/pubs/DeMoor-JAMA-2015.pdf},
    doi = {10.1001/jamapsychiatry.2015.0554},
    PubMedID = {25993607}
    }

  • Hart, A. B., Gamazon, E. R., Engelhardt, B. E., Sklar, P., Kähler, A. K., Hultman, C. M., Sullivan, P. F., Neale, B. M., Faraone, S. V., de Wit, H., Cox, N. J., & a Palmer, A.. (2014). Genetic variation associated with euphorigenic effects of d-amphetamine is associated with diminished risk for schizophrenia and attention deficit hyperactivity disorder. Proceedings of the National Academy of Sciences (PNAS), 111(16), 5968–73. doi:10.1073/pnas.1318810111
    [BibTeX] [Download PDF]
    @article{Hart2014,
    author = {Hart, Amy B and Gamazon, Eric R and Engelhardt, Barbara E and Sklar, Pamela and K\"{a}hler, Anna K and Hultman, Christina M and Sullivan, Patrick F and Neale, Benjamin M and Faraone, Stephen V and de Wit, Harriet and Cox, Nancy J and Palmer, Abraham a},
    doi = {10.1073/pnas.1318810111},
    url = {http://www.cs.princeton.edu/~bee/pubs/Hart-PNAS-2014.pdf},
    issn = {1091-6490},
    journal = {{Proceedings of the National Academy of Sciences (PNAS)}},
    month = apr,
    number = {16},
    pages = {5968--73},
    pmid = {24711425},
    title = {{Genetic variation associated with euphorigenic effects of d-amphetamine is associated with diminished risk for schizophrenia and attention deficit hyperactivity disorder}},
    volume = {111},
    year = {2014}
    }

  • Mangravite*, L. M., Engelhardt*, B. E., Medina, M. W., Smith, J. D., Brown, C. D., Chasman, D. I., Mecham, B. H., Howie, B., Shim, H., Naidoo, D., Feng, Q., Rieder, M. J., Chen, Y. I., Rotter, J. I., Ridker, P. M., Hopewell, J. C., Parish, S., Armitage, J., Collins, R., Wilke, R. A., Nickerson, D. A., Stephens, M., & Krauss, R. M.. (2013). A statin-dependent QTL for GATM expression is associated with statin-induced myopathy. Nature, 502(7471), 377–80. doi:10.1038/nature12508
    [BibTeX] [Download PDF]
    @article{Mangravite2013,
    author = {Mangravite*, Lara M and Engelhardt*, Barbara E and Medina, Marisa W and Smith, Joshua D and Brown, Christopher D and Chasman, Daniel I and Mecham, Brigham H and Howie, Bryan and Shim, Heejung and Naidoo, Devesh and Feng, QiPing and Rieder, Mark J and Chen, Yii-Der I and Rotter, Jerome I and Ridker, Paul M and Hopewell, Jemma C and Parish, Sarah and Armitage, Jane and Collins, Rory and Wilke, Russell A and Nickerson, Deborah A and Stephens, Matthew and Krauss, Ronald M},
    doi = {10.1038/nature12508},
    url = {http://www.cs.princeton.edu/~bee/pubs/Mangravite-Nature-2013.pdf},
    issn = {1476-4687},
    journal = {Nature},
    month = oct,
    number = {7471},
    pages = {377--80},
    pmid = {23995691},
    title = {{A statin-dependent QTL for GATM expression is associated with statin-induced myopathy}},
    url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3933266\&tool=pmcentrez\&rendertype=abstract},
    volume = {502},
    year = {2013}
    }

  • Mordelet, F., Horton, J., Hartemink, A. J., Engelhardt, B. E., & Gordân, R.. (2013). Stability selection for regression-based models of transcription factor-DNA binding specificity. Bioinformatics, 29(13), i117–25. doi:10.1093/bioinformatics/btt221
    [BibTeX] [Download PDF]
    @article{Mordelet2013,
    author = {Mordelet, Fantine and Horton, John and Hartemink, Alexander J and Engelhardt, Barbara E and Gord\^{a}n, Raluca},
    doi = {10.1093/bioinformatics/btt221},
    url = {http://www.cs.princeton.edu/~bee/pubs/Mordelet-Bioinformatics-2013.pdf},
    issn = {1367-4811},
    journal = {Bioinformatics},
    month = jul,
    number = {13},
    pages = {i117--25},
    pmid = {23812975},
    title = {{Stability selection for regression-based models of transcription factor-DNA binding specificity}},
    volume = {29},
    year = {2013}
    }

  • Brown, C. D., Mangravite, L. M., & Engelhardt, B. E.. (2013). Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs. PLoS Genetics, 9(8), e1003649. doi:10.1371/journal.pgen.1003649
    [BibTeX] [Download PDF]
    @article{Brown2013,
    author = {Brown, Christopher D and Mangravite, Lara M and Engelhardt, Barbara E},
    doi = {10.1371/journal.pgen.1003649},
    url = {http://www.cs.princeton.edu/~bee/pubs/Brown-plosgen-2013.pdf},
    issn = {1553-7404},
    journal = {{PLoS Genetics}},
    month = jan,
    number = {8},
    pages = {e1003649},
    pmid = {23935528},
    title = {{Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs}},
    volume = {9},
    year = {2013}
    }

  • Muratore, K. E., Engelhardt, B. E., Srouji, J. R., Jordan, M. I., Brenner, S. E., & Kirsch, J. F.. (2013). Molecular function prediction for a family exhibiting evolutionary tendencies toward substrate specificity swapping: Recurrence of tyrosine aminotransferase activity in the I$\alpha$ subfamily. Proteins: Structure, Function, and Bioinformatics, 81(9), 1593–1609.
    [BibTeX] [Download PDF]
    @article{Muratore2013,
    title={{Molecular function prediction for a family exhibiting evolutionary tendencies toward substrate specificity swapping: Recurrence of tyrosine aminotransferase activity in the I$\alpha$ subfamily}},
    author={Muratore, Kathryn E and Engelhardt, Barbara E and Srouji, John R and Jordan, Michael I and Brenner, Steven E and Kirsch, Jack F},
    journal={{Proteins: Structure, Function, and Bioinformatics}},
    volume={81},
    number={9},
    pages={1593--1609},
    year={2013},
    url={http://onlinelibrary.wiley.com/doi/10.1002/prot.24318/full},
    publisher={Wiley Online Library}
    }

  • Hart*, A. B., Engelhardt*, B. E., Wardle, M. C., Sokoloff, G., Stephens, M., de Wit, H., & Palmer, A.. (2012). Genome-wide association study of d-amphetamine response in healthy volunteers identifies putative associations, including cadherin 13 (CDH13). PLoS One, 7(8), e42646. doi:10.1371/journal.pone.0042646
    [BibTeX]
    @article{Hart2012,
    author = {Hart*, Amy B and Engelhardt*, Barbara E and Wardle, Margaret C and Sokoloff, Greta and Stephens, Matthew and de Wit, Harriet and Palmer, Abraham},
    doi = {10.1371/journal.pone.0042646},
    journal = {{PLoS One}},
    month = jan,
    number = {8},
    pages = {e42646},
    pmid = {22952603},
    title = {{Genome-wide association study of d-amphetamine response in healthy volunteers identifies putative associations, including cadherin 13 (CDH13)}},
    volume = {7},
    year = {2012}
    }

  • Engelhardt, B. E., Jordan, M. I., Srouji, J. R., & Brenner, S. E.. (2011). Genome-scale phylogenetic function annotation of large and diverse protein families. Genome Research, 21(11), 1969–80. doi:10.1101/gr.104687.109
    [BibTeX] [Download PDF]
    @article{Engelhardt2011,
    author = {Engelhardt, Barbara E and Jordan, Michael I and Srouji, John R and Brenner, Steven E},
    doi = {10.1101/gr.104687.109},
    url = {http://www.cs.princeton.edu/~bee/pubs/engelhardt-genomeresearch-2011.pdf},
    issn = {1549-5469},
    journal = {{Genome Research}},
    month = nov,
    number = {11},
    pages = {1969--80},
    pmid = {21784873},
    title = {{Genome-scale phylogenetic function annotation of large and diverse protein families}},
    volume = {21},
    year = {2011}
    }

  • Engelhardt, B. E., & Stephens, M.. (2010). 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
    [BibTeX] [Download PDF]
    @article{Engelhardt2010,
    author = {Engelhardt, Barbara E and Stephens, Matthew},
    doi = {10.1371/journal.pgen.1001117},
    url = {http://www.cs.princeton.edu/~bee/pubs/engelhardt-stephens-plosgen-2010.pdf},
    issn = {1553-7404},
    journal = {{PLoS Genetics}},
    keywords = {Africa,Computer Simulation,Databases, Genetic,Europe,Factor Analysis, Statistical,Genetics, Population,Genetics, Population: methods,Genotype,Humans,India,Models, Genetic,Population Dynamics,Principal Component Analysis},
    month = sep,
    number = {9},
    pages = {e1001117},
    pmid = {20862358},
    title = {{Analysis of population structure: A unifying framework and novel methods based on sparse factor analysis}},
    volume = {6},
    year = {2010}
    }

  • Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J., Stephens, M., Gilad, Y., & Pritchard, J. K.. (2010). Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature, 464(7289), 768–772. doi:10.1038/nature08872
    [BibTeX] [Download PDF]
    @article{Pickrell2010,
    title = {Understanding mechanisms underlying human gene expression variation with {RNA} sequencing},
    volume = {464},
    number = {7289},
    doi={10.1038/nature08872},
    url = {http://www.cs.princeton.edu/~bee/pubs/pickrell-2014-nature.pdf},
    author = {Pickrell, Joseph K and Marioni, John C and Pai, Athma A and Degner, Jacob F and Engelhardt, Barbara E and Nkadori, Everlyne and Veyrieras, Jean-Baptiste and Stephens, Matthew and Gilad, Yoav and Pritchard, Jonathan K},
    journal = {Nature},
    year={2010},
    pages = {768--772}
    }

  • Engelhardt, B. E., Jordan, M. I., & Brenner, S. E.. (2006). A graphical model for predicting protein molecular function. Proceedings of the 23rd International Conference on Machine Learning (ICML), 297–304. doi:10.1145/1143844.1143882
    [BibTeX] [Download PDF]
    @article{Engelhardt2006,
    address = {New York, New York, USA},
    author = {Engelhardt, Barbara E and Jordan, Michael I and Brenner, Steven E},
    doi = {10.1145/1143844.1143882},
    url = {http://www.cs.princeton.edu/~bee/pubs/sifter-icml.pdf},
    isbn = {1595933832},
    journal= {{Proceedings of the 23rd International Conference on Machine Learning (ICML)}},
    pages = {297--304},
    title = {{A graphical model for predicting protein molecular function}},
    year = {2006}
    }

  • Engelhardt, B. E., Jordan, M. I., Muratore, K. E., & Brenner, S. E.. (2005). Protein molecular function prediction by Bayesian phylogenomics. PLoS Computational Biology, 1(5), e45. doi:10.1371/journal.pcbi.0010045
    [BibTeX] [Download PDF]
    @article{Engelhardt2005,
    author = {Engelhardt, Barbara E and Jordan, Michael I and Muratore, Kathryn E and Brenner, Steven E},
    doi = {10.1371/journal.pcbi.0010045},
    url = {http://www.cs.princeton.edu/~bee/pubs/sifter-plos.pdf},
    issn = {1553-7358},
    journal = {{PLoS Computational Biology}},
    month = oct,
    number = {5},
    pages = {e45},
    pmid = {16217548},
    title = {{Protein molecular function prediction by Bayesian phylogenomics}},
    volume = {1},
    year = {2005}
    }

Preprints that will likely never be published (year represents the year they were posted)

* indicates equal authorship