Ricardo Henao
Assistant Professor in Biostatistics and Bioinformatics
Current Appointments & Affiliations
- Assistant Professor in Biostatistics and Bioinformatics, Biostatistics & Bioinformatics, Basic Science Departments 2021
- Assistant Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2018
- Member of Duke Center for Applied Genomics and Precision Medicine, Duke Center for Applied Genomics and Precision Medicine, Medicine 2014
- Member of the Duke Clinical Research Institute, Duke Clinical Research Institute, Institutes and Centers 2017
Contact Information
- 140 Science Drive, Durham, NC 27710
- Duke Box 90984, Durham, NC 27710
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ricardo.henao@duke.edu
(919) 668-0647
- Background
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Education, Training, & Certifications
- Ph.D., Technical University of Denmark (Denmark) 2011
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Duke Appointment History
- Assistant Professor in Biostatistics and Bioinformatics, Biostatistics & Bioinformatics, Basic Science Departments 2017 - 2020
- Assistant Research Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2015 - 2017
- Recognition
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In the News
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SEP 14, 2020 Precision Medicine -
MAR 16, 2016
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- Research
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Selected Grants
- Antibacterial Resistance Leadership Group (ARLG) awarded by National Institutes of Health 2013 - 2026
- Sepsis Characterization in Kilimanjaro (R01) awarded by National Institutes of Health 2020 - 2025
- Improving stroke risk prediction with cohort data and machine learning methods awarded by National Institute of Neurological Disorders and Stroke 2020 - 2025
- Machine Learning for Submesoscale Characterization, Ocean Prediction, and Exploration (ML-SCOPE) awarded by Massachusetts Institute of Technology 2019 - 2024
- A multidisciplinary study of biological disparities in NASH progression and response to statins to inform personalized liver cancer chemoprevention in NAFLD awarded by Department of Defense 2020 - 2024
- Novel Approaches to Infant Screening for ASD in Pediatric Primary Care awarded by National Institutes of Health 2019 - 2024
- Predictive Analytics in Hemodialysis: Enabling Precision Care for Patient with ESKD awarded by National Institutes of Health 2020 - 2024
- Clinical and Molecular Epidemiology of High Risk Coronary Plaque awarded by National Institutes of Health 2019 - 2023
- Diagnosis and Outcomes of Pulmonary Invasive Mold Infections in Immunocompromised Children awarded by National Institutes of Health 2018 - 2023
- Addressing variability in peripheral arterial disease outcomes using machine learning techniques awarded by National Institutes of Health 2020 - 2022
- Abbott Diagnostics Collaborative Sponsored Research Agreement awarded by Abbott Laboratories 2016 - 2022
- Pfizer QI Telehealth in Rheumatology awarded by Pfizer, Inc. 2020 - 2021
- One-Carbon Metabolism Genetics Data Analyses awarded by KBRwyle 2020 - 2021
- Mapping Epigenetic Memory of Exposure New To Observe (MEMENTO) awarded by Defense Advanced Research Projects Agency 2019 - 2021
- Gene expression profiling of children with SARS-CoV-2 infection or exposure awarded by Merck & Co., Inc. 2021
- Machine learning driven transthoracic echocardiographic analysis and screening for cardiac amyloidosis awarded by Vigilant Medical 2020 - 2021
- Enhanced X-ray Angiography Analysis and Interpretation Using Deep Learning awarded by Vigilant Medical 2019 - 2021
- QuBBD: Deep Poisson Methods for Biomedical Time-to-Event and Longitudinal Data awarded by National Institutes of Health 2017 - 2021
- Postdoctoral Training in Genomic Medicine Research awarded by National Institutes of Health 2017 - 2021
- Resilience Prediction to RSV Infection awarded by Sanofi US 2018 - 2020
- PREdicting contagion using Systems And GEnomic analysis (PRESAGE) awarded by Defense Advanced Research Projects Agency 2017 - 2020
- Host-derived biomarker signatures to differentiate acute viral, bacterial, and fungal infection awarded by National Institutes of Health 2017 - 2020
- Novel host-based diagnostics of febrile illness in the warfighter awarded by Department of Defense 2016 - 2020
- Role of the Stroma in Fibrolamellar Hepatocellular Carcinoma awarded by Fibrolamellar Cancer Foundation 2016 - 2019
- Development and Application of Mathematical Methods for Tracking Biochronicity and Baseline Variation awarded by Defense Advanced Research Projects Agency 2016 - 2019
- Deep learning driven angiographic analysis to enhance evaluation of coronary artery stenosis awarded by Vigilant Medical 2018 - 2019
- CTOT Ancillary Studies Fund Carry Forward BAL awarded by Mount Sinai School of Medicine 2016 - 2017
- Hyaluronan in Acute Rejection and Chronic Lung Allograft Dysfunction after Human Lung Transplantation awarded by Mount Sinai School of Medicine 2016 - 2017
- HARDAC+:Reproducible HPC for next-generation genomics awarded by North Carolina Biotechnology Center 2016 - 2017
- Feasibility for Predicting Warfighter Health Using Transcriptional Markers on the MAP Platform awarded by Ibis Biosciences, Inc. 2016
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External Relationships
- Helix OpCo, LLC
- Infinia ML
- Predigen, Inc.
- Vigilant Medical Inc
- Publications & Artistic Works
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Selected Publications
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Academic Articles
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McClain, Micah T., et al. “A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study.” Lancet Infect Dis, vol. 21, no. 3, Mar. 2021, pp. 396–404. Pubmed, doi:10.1016/S1473-3099(20)30486-2.Full Text Open Access Copy Link to Item
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Dov, David, et al. “Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images.” Med Image Anal, vol. 67, Jan. 2021, p. 101814. Pubmed, doi:10.1016/j.media.2020.101814.Full Text Open Access Copy Link to Item
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Draelos, Rachel Lea, et al. “Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.” Med Image Anal, vol. 67, Jan. 2021, p. 101857. Pubmed, doi:10.1016/j.media.2020.101857.Full Text Link to Item
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Wang, Liuyang, et al. “An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility.” Medrxiv, Dec. 2020. Pubmed, doi:10.1101/2020.12.20.20248572.Full Text Link to Item
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Wisely, C. Ellis, et al. “Convolutional neural network to identify symptomatic Alzheimer's disease using multimodal retinal imaging.” Br J Ophthalmol, Nov. 2020. Pubmed, doi:10.1136/bjophthalmol-2020-317659.Full Text Open Access Copy Link to Item
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Chapfuwa, Paidamoyo, et al. “Calibration and Uncertainty in Neural Time-to-Event Modeling.” Ieee Trans Neural Netw Learn Syst, vol. PP, Oct. 2020. Pubmed, doi:10.1109/TNNLS.2020.3029631.Full Text Link to Item
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Liu, Ying, et al. “Average Weighted Accuracy: Pragmatic Analysis for a Rapid Diagnostics in Categorizing Acute Lung Infections (RADICAL) Study.” Clin Infect Dis, vol. 70, no. 12, June 2020, pp. 2736–42. Pubmed, doi:10.1093/cid/ciz437.Full Text Open Access Copy Link to Item
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Elliott Range, Danielle D., et al. “Application of a machine learning algorithm to predict malignancy in thyroid cytopathology.” Cancer Cytopathol, vol. 128, no. 4, Apr. 2020, pp. 287–95. Pubmed, doi:10.1002/cncy.22238.Full Text Link to Item
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Chapfuwa, P., et al. “Survival cluster analysis.” Acm Chil 2020 Proceedings of the 2020 Acm Conference on Health, Inference, and Learning, Feb. 2020, pp. 60–68. Scopus, doi:10.1145/3368555.3384465.Full Text
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Xiu, Z., et al. “Variational learning of individual survival distributions.” Acm Chil 2020 Proceedings of the 2020 Acm Conference on Health, Inference, and Learning, Feb. 2020, pp. 10–18. Scopus, doi:10.1145/3368555.3384454.Full Text
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Assaad, Serge, et al. “Counterfactual Representation Learning with Balancing Weights.” Corr, vol. abs/2010.12618, 2020.
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Chapfuwa, Paidamoyo, et al. “Survival Analysis meets Counterfactual Inference.” Corr, vol. abs/2006.07756, 2020.
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Du, Kuo, et al. “Increased Glutaminolysis Marks Active Scarring in Nonalcoholic Steatohepatitis Progression.” Cell Mol Gastroenterol Hepatol, vol. 10, no. 1, 2020, pp. 1–21. Pubmed, doi:10.1016/j.jcmgh.2019.12.006.Full Text Link to Item
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Engelhard, Matthew, et al. “Neural Conditional Event Time Models.” Corr, vol. abs/2004.01376, 2020.
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Shen, D., et al. “Improved semantic-aware network embedding with fine-grained word alignment.” Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Emnlp 2018, Jan. 2020, pp. 1829–38.
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Si, Shijing, et al. “Students Need More Attention: BERT-based AttentionModel for Small Data with Application to AutomaticPatient Message Triage.” Corr, vol. abs/2006.11991, 2020.
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Xiu, Zidi, et al. “Variational Disentanglement for Rare Event Modeling.” Corr, vol. abs/2009.08541, 2020.
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Yuan, Siyang, et al. “Weakly supervised cross-domain alignment with optimal transport.” Corr, vol. abs/2008.06597, 2020.
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Lorenzi, E., et al. “Hierarchical infinite factor models for improving the prediction of surgical complications for geriatric patients.” Annals of Applied Statistics, vol. 13, no. 4, Dec. 2019, pp. 2637–61. Scopus, doi:10.1214/19-AOAS1292.Full Text
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Lydon, Emily C., et al. “Validation of a host response test to distinguish bacterial and viral respiratory infection.” Ebiomedicine, vol. 48, Oct. 2019, pp. 453–61. Pubmed, doi:10.1016/j.ebiom.2019.09.040.Full Text Open Access Copy Link to Item
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Engelhard, Matthew M., et al. “Identifying Smoking Environments From Images of Daily Life With Deep Learning.” Jama Netw Open, vol. 2, no. 8, Aug. 2019, p. e197939. Pubmed, doi:10.1001/jamanetworkopen.2019.7939.Full Text Link to Item
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Chapfuwa, Paidamoyo, et al. “Survival Function Matching for Calibrated Time-to-Event Predictions.” Corr, vol. abs/1905.08838, 2019.
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Cheng, Pengyu, et al. “Straight-Through Estimator as Projected Wasserstein Gradient Flow.” Corr, vol. abs/1910.02176, 2019.
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Dov, David, et al. “Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images.” Mlhc, edited by Finale Doshi-Velez et al., vol. 106, PMLR, 2019, pp. 553–70.Open Access Copy
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Dov, David, et al. “Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images.” Corr, vol. abs/1904.00839, 2019.
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Liang, K. J., et al. “Kernel-based approaches for sequence modeling: Connections to neural methods.” Advances in Neural Information Processing Systems, vol. 32, Jan. 2019.
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Limkakeng, Alexander T., et al. “Pilot study of myocardial ischemia-induced metabolomic changes in emergency department patients undergoing stress testing.” Plos One, vol. 14, no. 2, 2019, p. e0211762. Pubmed, doi:10.1371/journal.pone.0211762.Full Text Open Access Copy Link to Item
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Lydon, Emily C., et al. “A host gene expression approach for identifying triggers of asthma exacerbations.” Plos One, vol. 14, no. 4, 2019, p. e0214871. Pubmed, doi:10.1371/journal.pone.0214871.Full Text Open Access Copy Link to Item
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Wang, Rui, et al. “Discriminative Clustering for Robust Unsupervised Domain Adaptation.” Corr, vol. abs/1905.13331, 2019.
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Wang, W., et al. “Improving textual network learning with variational homophilic embeddings.” Advances in Neural Information Processing Systems, vol. 32, Jan. 2019.
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Fourati, Slim, et al. “A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection.” Nat Commun, vol. 9, no. 1, Oct. 2018, p. 4418. Pubmed, doi:10.1038/s41467-018-06735-8.Full Text Open Access Copy Link to Item
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Bradley, Todd, et al. “RAB11FIP5 Expression and Altered Natural Killer Cell Function Are Associated with Induction of HIV Broadly Neutralizing Antibody Responses.” Cell, vol. 175, no. 2, Oct. 2018, pp. 387-399.e17. Pubmed, doi:10.1016/j.cell.2018.08.064.Full Text Link to Item
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Sweeney, Timothy E., et al. “Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters.” Crit Care Med, vol. 46, no. 6, June 2018, pp. 915–25. Pubmed, doi:10.1097/CCM.0000000000003084.Full Text Link to Item
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Sweeney, Timothy E., et al. “A community approach to mortality prediction in sepsis via gene expression analysis.” Nat Commun, vol. 9, no. 1, Feb. 2018, p. 694. Pubmed, doi:10.1038/s41467-018-03078-2.Full Text Link to Item
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Chapfuwa, P., et al. “Adversarial time-to-event modeling.” 35th International Conference on Machine Learning, Icml 2018, vol. 2, Jan. 2018, pp. 1143–56.
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Poore, Gregory D., et al. “A miRNA Host Response Signature Accurately Discriminates Acute Respiratory Infection Etiologies.” Front Microbiol, vol. 9, 2018, p. 2957. Pubmed, doi:10.3389/fmicb.2018.02957.Full Text Open Access Copy Link to Item
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Pu, Y., et al. “JointGAN: Multi-domain joint distribution learning with generative adversarial nets.” 35th International Conference on Machine Learning, Icml 2018, vol. 9, Jan. 2018, pp. 6626–35.
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Shen, D., et al. “Deconvolutional latent-variable model for text sequence matching.” 32nd Aaai Conference on Artificial Intelligence, Aaai 2018, Jan. 2018, pp. 5438–45.
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Shen, D., et al. “NasH: Toward end-to-end neural architecture for generative semantic hashing.” Acl 2018 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), vol. 1, Jan. 2018, pp. 2041–50. Scopus, doi:10.18653/v1/p18-1190.Full Text Open Access Copy
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Shen, D., et al. “Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms.” Acl 2018 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), vol. 1, Jan. 2018, pp. 440–50. Scopus, doi:10.18653/v1/p18-1041.Full Text
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Wang, G., et al. “Joint embedding of words and labels for text classification.” Acl 2018 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), vol. 1, Jan. 2018, pp. 2321–31. Scopus, doi:10.18653/v1/p18-1216.Full Text
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Wegermann, Kara, et al. “Branched chain amino acid transaminase 1 (BCAT1) is overexpressed and hypomethylated in patients with non-alcoholic fatty liver disease who experience adverse clinical events: A pilot study.” Plos One, vol. 13, no. 9, 2018, p. e0204308. Pubmed, doi:10.1371/journal.pone.0204308.Full Text Link to Item
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Zhang, Xinyuan, et al. “Multi-Label Learning from Medical Plain Text with Convolutional Residual Models.” Corr, vol. abs/1801.05062, 2018.
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Hald, Ditte Høvenhoff, et al. “Gaussian process based independent analysis for temporal source separation in fMRI.” Neuroimage, vol. 152, May 2017, pp. 563–74. Pubmed, doi:10.1016/j.neuroimage.2017.02.070.Full Text Link to Item
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Patel, Yuval A., et al. “Reply to Kim et al.” Am J Gastroenterol, vol. 112, no. 5, May 2017, pp. 807–08. Pubmed, doi:10.1038/ajg.2017.45.Full Text Link to Item
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Burke, Thomas W., et al. “Nasopharyngeal Protein Biomarkers of Acute Respiratory Virus Infection.” Ebiomedicine, vol. 17, Mar. 2017, pp. 172–81. Pubmed, doi:10.1016/j.ebiom.2017.02.015.Full Text Link to Item
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Li, Chunyuan, et al. “Towards Understanding Adversarial Learning for Joint Distribution Matching.” Corr, vol. abs/1709.01215, 2017.
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Pu, Y., et al. “Adversarial symmetric variational autoencoder.” Advances in Neural Information Processing Systems, vol. 2017-December, Jan. 2017, pp. 4331–40.
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Pu, Yunchen, et al. “Stein Variational Autoencoder.” Corr, vol. abs/1704.05155, 2017.
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Zhang, Y., et al. “Adversarial feature matching for text generation.” 34th International Conference on Machine Learning, Icml 2017, vol. 8, Jan. 2017, pp. 6093–102.
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Zhang, Y., et al. “Deconvolutional paragraph representation learning.” Advances in Neural Information Processing Systems, vol. 2017-December, Jan. 2017, pp. 4170–80.
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Zhang, Y., et al. “Stochastic gradient monomial gamma sampler.” 34th International Conference on Machine Learning, Icml 2017, vol. 8, Jan. 2017, pp. 6083–92.
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Patel, Yuval A., et al. “Vitamin D is Not Associated With Severity in NAFLD: Results of a Paired Clinical and Gene Expression Profile Analysis.” Am J Gastroenterol, vol. 111, no. 11, Nov. 2016, pp. 1591–98. Pubmed, doi:10.1038/ajg.2016.406.Full Text Link to Item
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Henao, R., et al. “Electronic health record analysis via deep poisson factor models.” Journal of Machine Learning Research, vol. 17, Apr. 2016.
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McClain, M. T., et al. “Differential evolution of peripheral cytokine levels in symptomatic and asymptomatic responses to experimental influenza virus challenge.” Clin Exp Immunol, vol. 183, no. 3, Mar. 2016, pp. 441–51. Pubmed, doi:10.1111/cei.12736.Full Text Link to Item
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Tsalik, Ephraim L., et al. “Host gene expression classifiers diagnose acute respiratory illness etiology.” Sci Transl Med, vol. 8, no. 322, Jan. 2016, p. 322ra11. Pubmed, doi:10.1126/scitranslmed.aad6873.Full Text Open Access Copy Link to Item
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Gan, Zhe, et al. “Unsupervised Learning of Sentence Representations using Convolutional Neural Networks.” Corr, vol. abs/1611.07897, 2016.
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Zhang, Y., et al. “Learning a hybrid architecture for sequence regression and annotation.” 30th Aaai Conference on Artificial Intelligence, Aaai 2016, Jan. 2016, pp. 1415–21.Open Access Copy
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Vikeså, Jonas, et al. “Cancers of unknown primary origin (CUP) are characterized by chromosomal instability (CIN) compared to metastasis of know origin.” Bmc Cancer, vol. 15, Mar. 2015, p. 151. Pubmed, doi:10.1186/s12885-015-1128-x.Full Text Link to Item
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Gan, Z., et al. “Deep temporal sigmoid belief networks for sequence modeling.” Advances in Neural Information Processing Systems, vol. 2015-January, Jan. 2015, pp. 2467–75.
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Yuan, X., et al. “Non-Gaussian discriminative factor models via the max-margin rank-likelihood.” 32nd International Conference on Machine Learning, Icml 2015, vol. 2, Jan. 2015, pp. 1254–63.Open Access Copy
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Tsalik, Ephraim L., et al. “An integrated transcriptome and expressed variant analysis of sepsis survival and death.” Genome Med, vol. 6, no. 11, 2014, p. 111. Pubmed, doi:10.1186/s13073-014-0111-5.Full Text Open Access Copy Link to Item
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Benjamin, Ashlee M., et al. “A flexible statistical model for alignment of label-free proteomics data--incorporating ion mobility and product ion information.” Bmc Bioinformatics, vol. 14, Dec. 2013, p. 364. Pubmed, doi:10.1186/1471-2105-14-364.Full Text Open Access Copy Link to Item
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Henao, R., et al. “Latent protein trees.” Annals of Applied Statistics, vol. 7, no. 2, June 2013, pp. 691–713. Scopus, doi:10.1214/13-AOAS639.Full Text Open Access Copy
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Kurokawa, Manabu, et al. “A network of substrates of the E3 ubiquitin ligases MDM2 and HUWE1 control apoptosis independently of p53.” Sci Signal, vol. 6, no. 274, May 2013, p. ra32. Pubmed, doi:10.1126/scisignal.2003741.Full Text Open Access Copy Link to Item
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Henao, Ricardo, et al. “Patient clustering with uncoded text in electronic medical records.” Amia Annu Symp Proc, vol. 2013, 2013, pp. 592–99.Link to Item
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Henao, R., et al. “Hierarchical factor modeling of proteomics data.” 2012 Ieee 2nd International Conference on Computational Advances in Bio and Medical Sciences, Iccabs 2012, May 2012. Scopus, doi:10.1109/ICCABS.2012.6182638.Full Text
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Henao, R., and O. Winther. “Predictive active set selection methods for Gaussian processes.” Neurocomputing, vol. 80, Mar. 2012, pp. 10–18. Scopus, doi:10.1016/j.neucom.2011.09.017.Full Text
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Rossing, Maria, et al. “Down-regulation of microRNAs controlling tumourigenic factors in follicular thyroid carcinoma.” J Mol Endocrinol, vol. 48, no. 1, Feb. 2012, pp. 11–23. Pubmed, doi:10.1530/JME-11-0039.Full Text Link to Item
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Carin, Lawrence, et al. “High-Dimensional Longitudinal Genomic Data: An analysis used for monitoring viral infections.” Ieee Signal Process Mag, vol. 29, no. 1, Jan. 2012, pp. 108–23. Pubmed, doi:10.1109/MSP.2011.943009.Full Text Link to Item
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Friis, Morten, et al. “Gene expression of the endolymphatic sac.” Acta Otolaryngol, vol. 131, no. 12, Dec. 2011, pp. 1257–63. Pubmed, doi:10.3109/00016489.2011.616910.Full Text Link to Item
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Henao, R., and O. Winther. “Sparse linear identifiable multivariate modeling.” Journal of Machine Learning Research, vol. 12, Mar. 2011, pp. 863–905.
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Henao, R., and O. Winther. “PASS-GP: Predictive active set selection for Gaussian processes.” Proceedings of the 2010 Ieee International Workshop on Machine Learning for Signal Processing, Mlsp 2010, Nov. 2010, pp. 148–53. Scopus, doi:10.1109/MLSP.2010.5589264.Full Text
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Borup, Rehannah, et al. “Molecular signatures of thyroid follicular neoplasia.” Endocr Relat Cancer, vol. 17, no. 3, Sept. 2010, pp. 691–708. Pubmed, doi:10.1677/ERC-09-0288.Full Text Link to Item
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Walder, Christian, et al. “Semi-Supervised Kernel PCA.” Corr, vol. abs/1008.1398, 2010.
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Alvarez López, M. A., et al. “Myocardial ischemia detection using Hidden Markov principal component analysis.” Ifmbe Proceedings, vol. 18, Jan. 2008, pp. 99–103. Scopus, doi:10.1007/978-3-540-74471-9_24.Full Text
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Alvarez, M., et al. “Kernel Principal Component analysis through time for voice disorder classification.” Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Conference, 2006, pp. 5511–14.
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Alvarez, Mauricio, et al. “Kernel Principal Component analysis through time for voice disorder classification.” Conf Proc Ieee Eng Med Biol Soc, vol. 2006, 2006, pp. 5511–14. Pubmed, doi:10.1109/IEMBS.2006.260357.Full Text Link to Item
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Henao, Ricardo, and Joseph E. Lucas. Efficient hierarchical clustering for continuous data.Link to Item
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Book Sections
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Gan, Zhe, et al. “Inference of gene networks associated with the host response to infectious disease.” Big Data over Networks, Cambridge University Press, pp. 365–90. Crossref, doi:10.1017/cbo9781316162750.014.Full Text
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Conference Papers
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Chapfuwa, Paidamoyo, et al. “Survival cluster analysis.” Chil, edited by Marzyeh Ghassemi, ACM, 2020, pp. 60–68.
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Chen, Liqun, et al. “Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning.” Aaai, AAAI Press, 2020, pp. 7512–20.
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Wang, Rui, et al. “Integrating Task Specific Information into Pretrained Language Models for Low Resource Fine Tuning.” Emnlp (Findings), edited by Trevor Cohn et al., Association for Computational Linguistics, 2020, pp. 3181–86.
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Xiu, Zidi, et al. “Variational learning of individual survival distributions.” Chil, edited by Marzyeh Ghassemi, ACM, 2020, pp. 10–18.
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Benitez, M., et al. “Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10950, 2019. Scopus, doi:10.1117/12.2512886.Full Text
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Han, S., et al. “Classifying abnormalities in computed tomography radiology reports with rule-based and natural language processing models.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10950, 2019. Scopus, doi:10.1117/12.2513577.Full Text
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Li, C., et al. “Communication-Efficient stochastic gradient mcmc for neural networks.” 33rd Aaai Conference on Artificial Intelligence, Aaai 2019, 31st Innovative Applications of Artificial Intelligence Conference, Iaai 2019 and the 9th Aaai Symposium on Educational Advances in Artificial Intelligence, Eaai 2019, 2019, pp. 4173–80.
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Liang, Kevin J., et al. “Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods.” Neurips, edited by Hanna M. Wallach et al., 2019, pp. 3387–98.
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Liu, Y., et al. “Deep learning of 3D computed tomography (CT) images for organ segmentation using 2D multi-channel SegNet model.” Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10954, 2019. Scopus, doi:10.1117/12.2512887.Full Text
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Wang, Wenlin, et al. “Improving Textual Network Learning with Variational Homophilic Embeddings.” Neurips, edited by Hanna M. Wallach et al., 2019, pp. 2074–85.
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Chapfuwa, Paidamoyo, et al. “Adversarial Time-to-Event Modeling.” Icml, edited by Jennifer G. Dy and Andreas Krause, vol. 80, PMLR, 2018, pp. 734–43.
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Chen, Liqun, et al. “Variational Inference and Model Selection with Generalized Evidence Bounds.” Icml, edited by Jennifer G. Dy and Andreas Krause, vol. 80, PMLR, 2018, pp. 892–901.
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Pu, Yunchen, et al. “JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets.” Icml, edited by Jennifer G. Dy and Andreas Krause, vol. 80, PMLR, 2018, pp. 4148–57.
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Rusincovitch, Shelley A., et al. “The Duke Health Data Science Internship Program: Integrating the Educational Mission into Real-World Research.” Amia, AMIA, 2018.
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Shen, Dinghan, et al. “Deconvolutional Latent-Variable Model for Text Sequence Matching.” Aaai, edited by Sheila A. McIlraith and Kilian Q. Weinberger, AAAI Press, 2018, pp. 5438–45.
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Shen, Dinghan, et al. “Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms.” Acl (1), edited by Iryna Gurevych and Yusuke Miyao, Association for Computational Linguistics, 2018, pp. 440–50.
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Shen, Dinghan, et al. “NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing.” Acl (1), edited by Iryna Gurevych and Yusuke Miyao, Association for Computational Linguistics, 2018, pp. 2041–50.
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Shen, Dinghan, et al. “Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment.” Emnlp, edited by Ellen Riloff et al., Association for Computational Linguistics, 2018, pp. 1829–38.
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Tao, C., et al. “X2 generative adversarial network.” 35th International Conference on Machine Learning, Icml 2018, vol. 11, 2018, pp. 7787–96.
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Tao, C., et al. “Supplementary material for "x2 Generative Adversarial Net".” 35th International Conference on Machine Learning, Icml 2018, vol. 11, 2018, pp. 7797–809.
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Tao, C., et al. “Variational inference and model selection with generalized evidence bounds.” 35th International Conference on Machine Learning, Icml 2018, vol. 2, 2018, pp. 1419–35.
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Tao, Chenyang, et al. “Chi-square Generative Adversarial Network.” Icml, edited by Jennifer G. Dy and Andreas Krause, vol. 80, PMLR, 2018, pp. 4894–903.
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Wang, Guoyin, et al. “Joint Embedding of Words and Labels for Text Classification.” Acl (1), edited by Iryna Gurevych and Yusuke Miyao, Association for Computational Linguistics, 2018, pp. 2321–31.
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Zhang, Xinyuan, et al. “Multi-Label Learning from Medical Plain Text with Convolutional Residual Models.” Mlhc, edited by Finale Doshi-Velez et al., vol. 85, PMLR, 2018, pp. 280–94.
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Fan, K., et al. “Triply stochastic variational inference for non-linear beta process factor analysis.” Proceedings Ieee International Conference on Data Mining, Icdm, 2017, pp. 121–30. Scopus, doi:10.1109/ICDM.2016.36.Full Text
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Zhang, Y., et al. “Dynamic poisson factor analysis.” Proceedings Ieee International Conference on Data Mining, Icdm, 2017, pp. 1359–64. Scopus, doi:10.1109/ICDM.2016.111.Full Text
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Gan, Z., et al. “Learning generic sentence representations using convolutional neural networks.” Emnlp 2017 Conference on Empirical Methods in Natural Language Processing, Proceedings, 2017, pp. 2390–400. Scopus, doi:10.18653/v1/d17-1254.Full Text
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Komives, Eugenie, et al. “Guiding Principles for the Duke Connected Care Predictive Modeling Pilot.” Amia, AMIA, 2017.
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Li, C., et al. “ALICE: Towards understanding adversarial learning for joint distribution matching.” Advances in Neural Information Processing Systems, vol. 2017-December, 2017, pp. 5496–504.
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Li, Chunyuan, et al. “ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching.” Nips, edited by Isabelle Guyon et al., 2017, pp. 5495–503.
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Pu, Y., et al. “VAE learning via Stein variational gradient descent.” Advances in Neural Information Processing Systems, vol. 2017-December, 2017, pp. 4237–46.
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Pu, Yunchen, et al. “VAE Learning via Stein Variational Gradient Descent.” Nips, edited by Isabelle Guyon et al., 2017, pp. 4236–45.
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Pu, Yunchen, et al. “Adversarial Symmetric Variational Autoencoder.” Nips, edited by Isabelle Guyon et al., 2017, pp. 4330–39.
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Rusincovitch, Shelley A., et al. “Rationale and Design for the Duke Connected Care Predictive Modeling Pilot with a Medicare Shared Savings Program Population.” Amia, AMIA, 2017.
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Zhang, Yizhe, et al. “Stochastic Gradient Monomial Gamma Sampler.” Icml, edited by Doina Precup and Yee Whye Teh, vol. 70, PMLR, 2017, pp. 3996–4005.
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Zhang, Yizhe, et al. “Adversarial Feature Matching for Text Generation.” Icml, edited by Doina Precup and Yee Whye Teh, vol. 70, PMLR, 2017, pp. 4006–15.
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Zhang, Yizhe, et al. “Deconvolutional Paragraph Representation Learning.” Nips, edited by Isabelle Guyon et al., 2017, pp. 4169–79.
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Patel, Yuval A., et al. “Vitamin D is Not Associated with Histologic Severity in NAFLD: Results of a Paired Clinical and Hepatic Gene Expression Profile Analysis.” Hepatology, vol. 64, WILEY, 2016, pp. 577A-577A.Link to Item
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Wegermann, Kara, et al. “BCAT1 is Associated with Clinical Decompensation in NAFLD: A Pilot Study.” Hepatology, vol. 64, WILEY, 2016, pp. 577A-577A.Link to Item
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Pu, Y., et al. “Variational autoencoder for deep learning of images, labels and captions.” Advances in Neural Information Processing Systems, 2016, pp. 2360–68.
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Pu, Yunchen, et al. “Variational Autoencoder for Deep Learning of Images, Labels and Captions.” Nips, edited by Daniel D. Lee et al., 2016, pp. 2352–60.
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Song, Z., et al. “Learning sigmoid belief networks via monte carlo expectation maximization.” Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Aistats 2016, 2016, p. 1347.
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Yizhe, Z., et al. “Bayesian dictionary learning with Gaussian processes and sigmoid belief networks.” Ijcai International Joint Conference on Artificial Intelligence, vol. 2016-January, 2016, pp. 2364–70.
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Zhang, Y., et al. “Towards unifying hamiltonian Monte Carlo and Slice sampling.” Advances in Neural Information Processing Systems, 2016, pp. 1749–57.
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Zhang, Yizhe, et al. “Towards Unifying Hamiltonian Monte Carlo and Slice Sampling.” Nips, edited by Daniel D. Lee et al., 2016, pp. 1741–49.
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Gan, Zhe, et al. “Deep Temporal Sigmoid Belief Networks for Sequence Modeling.” Nips, edited by Corinna Cortes et al., 2015, pp. 2467–75.
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- Teaching & Mentoring
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Recent Courses
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- COMPSCI 394: Research Independent Study 2019
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