Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., Technical University of Denmark (Denmark) 2011
• 

  Previous Appointments & Affiliations

  • 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
• 

  In the News

  • SEP 14, 2020

    Precision Medicine

    Duke Researchers Look to Help Protect Astronaut's Eyes in Space

  • MAR 16, 2016

    New Collaborative Seed Grant Program Gives Eight Awards
• Research
• 

  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 2022 - 2024
  • Host response-based diagnostics for identifying bacterial versus viral causes of lower respiratory infection in resource-limited settings awarded by National Institutes of Health 2022 - 2024
  • Rapid, point-of-care, host gene expression test for presymptomatic viral infection and to distinguish bacterial from viral illness awarded by Department of Defense 2021 - 2023
  • 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
  • Pfizer QI Telehealth in Rheumatology awarded by Pfizer, Inc. 2020 - 2022
  • 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
  • Gene expression profiling of children with SARS-CoV-2 infection or exposure awarded by Merck & Co., Inc. 2021 - 2022
  • Enhanced X-ray Angiography Analysis and Interpretation Using Deep Learning awarded by Vigilant Medical 2019 - 2022
  • Postdoctoral Training in Genomic Medicine Research awarded by National Institutes of Health 2017 - 2022
  • Mapping Epigenetic Memory of Exposure New To Observe (MEMENTO) awarded by Defense Advanced Research Projects Agency 2019 - 2022
  • Resilience Prediction to RSV Infection awarded by Sanofi US 2018 - 2021
  • One-Carbon Metabolism Genetics Data Analyses awarded by KBRwyle 2020 - 2021
  • Machine learning driven transthoracic echocardiographic analysis and screening for cardiac amyloidosis awarded by Vigilant Medical 2020 - 2021
  • QuBBD: Deep Poisson Methods for Biomedical Time-to-Event and Longitudinal Data awarded by National Institutes of Health 2017 - 2021
  • 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
• 

  External Relationships

  • Biomeme, Inc.
  • Helix OpCo, LLC
  • Infinia ML
  • Vigilant Medical Inc
• Publications & Artistic Works
• 

  Selected Publications

  • Academic Articles

    • Draelos, Rachel L., et al. “GENESIS: Gene-Specific Machine Learning Models for Variants of Uncertain Significance Found in Catecholaminergic Polymorphic Ventricular Tachycardia and Long QT Syndrome-Associated Genes.” Circ Arrhythm Electrophysiol, vol. 15, no. 4, Apr. 2022, p. e010326. Pubmed, doi:10.1161/CIRCEP.121.010326.
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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, vol. 106, no. 3, Mar. 2022, pp. 388–95. Pubmed, doi:10.1136/bjophthalmol-2020-317659.
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    • Pagidipati, Neha J., et al. “The importance of weight stabilization amongst those with overweight or obesity: Results from a large health care system.” Prev Med Rep, vol. 24, Dec. 2021, p. 101615. Pubmed, doi:10.1016/j.pmedr.2021.101615.
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    • Tsalik, Ephraim L., et al. “Discriminating Bacterial and Viral Infection Using a Rapid Host Gene Expression Test.” Crit Care Med, vol. 49, no. 10, Oct. 2021, pp. 1651–63. Pubmed, doi:10.1097/CCM.0000000000005085.
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    • Zhang, Hao, et al. “Learning Hierarchical Document Graphs From Multilevel Sentence Relations.” Ieee Trans Neural Netw Learn Syst, vol. PP, Sept. 2021. Pubmed, doi:10.1109/TNNLS.2021.3113297.
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    • Van Neste, Leander, et al. “Evaluation of an RNAseq-Based Immunogenomic Liquid Biopsy Approach in Early-Stage Prostate Cancer.” Cells, vol. 10, no. 10, Sept. 2021. Pubmed, doi:10.3390/cells10102567.
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    • Markwalter, Christine F., et al. “Antibody signatures of asymptomatic Plasmodium falciparum malaria infections measured from dried blood spots.” Malar J, vol. 20, no. 1, Sept. 2021, p. 378. Pubmed, doi:10.1186/s12936-021-03915-8.
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    • Alexopoulos, Anastasia-Stefania, et al. “Glycemic Control Predicts Severity of Hepatocyte Ballooning and Hepatic Fibrosis in Nonalcoholic Fatty Liver Disease.” Hepatology, vol. 74, no. 3, Sept. 2021, pp. 1220–33. Pubmed, doi:10.1002/hep.31806.
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    • Grzesiak, Emilia, et al. “Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset.” Jama Netw Open, vol. 4, no. 9, Sept. 2021, p. e2128534. Pubmed, doi:10.1001/jamanetworkopen.2021.28534.
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    • Mahle, Rachael E., et al. “Validation of a Host Gene Expression Test for Bacterial/Viral Discrimination in Immunocompromised Hosts.” Clin Infect Dis, vol. 73, no. 4, Aug. 2021, pp. 605–13. Pubmed, doi:10.1093/cid/ciab043.
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    • She, Xichen, et al. “Adaptive Multi-Channel Event Segmentation and Feature Extraction for Monitoring Health Outcomes.” Ieee Trans Biomed Eng, vol. 68, no. 8, Aug. 2021, pp. 2377–88. Pubmed, doi:10.1109/TBME.2020.3038652.
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    • Garside, Nicholas, et al. “CPT to RVU conversion improves model performance in the prediction of surgical case length.” Sci Rep, vol. 11, no. 1, July 2021, p. 14169. Pubmed, doi:10.1038/s41598-021-93573-2.
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    • Gao, Qitong, et al. Gradient Importance Learning for Incomplete Observations. July 2021.
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    • Steinbrink, Julie M., et al. “The host transcriptional response to Candidemia is dominated by neutrophil activation and heme biosynthesis and supports novel diagnostic approaches.” Genome Med, vol. 13, no. 1, July 2021, p. 108. Pubmed, doi:10.1186/s13073-021-00924-9.
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    • Kong, Fanjie, and Ricardo Henao. Efficient Classification of Very Large Images with Tiny Objects. June 2021.
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    • Xiu, Zidi, et al. “Variational Disentanglement for Rare Event Modeling.” Proc Conf Aaai Artif Intell, vol. 35, no. 12, May 2021, pp. 10469–77.
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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.” Genome Med, vol. 13, no. 1, May 2021, p. 83. Pubmed, doi:10.1186/s13073-021-00904-z.
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    • Turpin, Miles, et al. Machine Learning Prediction of Surgical Intervention for Small Bowel Obstruction. Apr. 2021. Epmc, doi:10.1101/2021.04.13.21255428.
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    • Xia, Meng, et al. Malignancy Prediction and Lesion Identification from Clinical Dermatological Images. Apr. 2021.
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    • Wegermann, Kara, et al. “Serum Bile Acid, Vitamin E, and Serotonin Metabolites Are Associated With Future Liver-Related Events in Nonalcoholic Fatty Liver Disease.” Hepatol Commun, vol. 5, no. 4, Apr. 2021, pp. 608–17. Pubmed, doi:10.1002/hep4.1665.
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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.
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    • McClain, Micah T., et al. “Dysregulated transcriptional responses to SARS-CoV-2 in the periphery.” Nat Commun, vol. 12, no. 1, Feb. 2021, p. 1079. Pubmed, doi:10.1038/s41467-021-21289-y.
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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.
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    • Kong, Fanjie, et al. “Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification.” Corr, vol. abs/2101.03154, 2021.
    • Wosik, Jedrek, et al. “Impact of the COVID-19 pandemic on patterns of outpatient cardiovascular care.” Am Heart J, vol. 231, Jan. 2021, pp. 1–5. Pubmed, doi:10.1016/j.ahj.2020.10.074.
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    • Assaad, Serge, et al. “Counterfactual Representation Learning with Balancing Weights.” 24th International Conference on Artificial Intelligence and Statistics (Aistats), vol. 130, 2021.
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    • Chen, L., et al. “Wasserstein contrastive representation distillation.” Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, Jan. 2021, pp. 16291–300. Scopus, doi:10.1109/CVPR46437.2021.01603.
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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.
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    • Dov, David, et al. “Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images.” Medical Image Anal., vol. 67, 2021, pp. 101814–101814. Dblp, doi:10.1016/j.media.2020.101814.
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    • Draelos, Rachel Lea, et al. “Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.” Medical Image Anal., vol. 67, 2021, pp. 101857–101857. Dblp, doi:10.1016/j.media.2020.101857.
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    • Ross, Melissa, et al. “A comparison of host response strategies to distinguish bacterial and viral infection.” Plos One, vol. 16, no. 12, 2021, p. e0261385. Pubmed, doi:10.1371/journal.pone.0261385.
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    • She, Xichen, et al. “Adaptive Multi-Channel Event Segmentation and Feature Extraction for Monitoring Health Outcomes.” Ieee Trans. Biomed. Eng., vol. 68, 2021, pp. 2377–88. Dblp, doi:10.1109/TBME.2020.3038652.
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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.
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    • Giroux, Nicholas S., et al. “Chromatin remodeling in peripheral blood cells reflects COVID-19 symptom severity.” Biorxiv, Dec. 2020. Pubmed, doi:10.1101/2020.12.04.412155.
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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.
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    • Abdulrahim, Jawan W., et al. “Identification of Undetected Monogenic Cardiovascular Disorders.” J Am Coll Cardiol, vol. 76, no. 7, Aug. 2020, pp. 797–808. Pubmed, doi:10.1016/j.jacc.2020.06.037.
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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.
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    • Tillekeratne, L. Gayani, et al. “Previously Derived Host Gene Expression Classifiers Identify Bacterial and Viral Etiologies of Acute Febrile Respiratory Illness in a South Asian Population.” Open Forum Infect Dis, vol. 7, no. 6, June 2020, p. ofaa194. Pubmed, doi:10.1093/ofid/ofaa194.
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    • Engelhard, Matthew, et al. Neural Conditional Event Time Models. Apr. 2020.
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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.
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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.
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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.
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    • Chapfuwa, Paidamoyo, et al. “Survival Analysis meets Counterfactual Inference.” Corr, vol. abs/2006.07756, 2020.
    • Chen, Junya, et al. “Supercharging Imbalanced Data Learning With Causal Representation Transfer.” Corr, vol. abs/2011.12454, 2020.
    • 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.
    • Wang, Dong, et al. “Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models.” Corr, vol. abs/2012.03369, 2020.
    • Yuan, Siyang, et al. “Weakly supervised cross-domain alignment with optimal transport.” Corr, vol. abs/2008.06597, 2020.
    • 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.
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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.
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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.
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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.
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    • Dov, David, et al. “Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images.” Proceedings of Machine Learning Research, 2019, Vol. 106, Mar. 2019.
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    • Chapfuwa, Paidamoyo, et al. “Survival Function Matching for Calibrated Time-to-Event Predictions.” Corr, vol. abs/1905.08838, 2019.
    • Cheng, Pengyu, et al. “Straight-Through Estimator as Projected Wasserstein Gradient Flow.” Corr, vol. abs/1910.02176, 2019.
    • Dov, David, et al. “Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images.” Corr, vol. abs/1904.00839, 2019.
    • 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.
    • 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.
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    • Wang, Rui, et al. “Discriminative Clustering for Robust Unsupervised Domain Adaptation.” Corr, vol. abs/1905.13331, 2019.
    • Wang, W., et al. “Improving textual network learning with variational homophilic embeddings.” Advances in Neural Information Processing Systems, vol. 32, Jan. 2019.
    • 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.
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    • Glass, Oliver, et al. “Serum Interleukin-8, Osteopontin, and Monocyte Chemoattractant Protein 1 Are Associated With Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease.” Hepatol Commun, vol. 2, no. 11, Nov. 2018, pp. 1344–55. Pubmed, doi:10.1002/hep4.1237.
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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.
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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.
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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.
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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.
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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.
    • 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.
    • 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.
    • 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.
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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.
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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.
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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.
    • 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.
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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. 2018, pp. 1829–38.
    • 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.
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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.
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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.
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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.
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    • Li, Chunyuan, et al. “Towards Understanding Adversarial Learning for Joint Distribution Matching.” Corr, vol. abs/1709.01215, 2017.
    • Pu, Y., et al. “Adversarial symmetric variational autoencoder.” Advances in Neural Information Processing Systems, vol. 2017-December, Jan. 2017, pp. 4331–40.
    • Pu, Yunchen, et al. “Stein Variational Autoencoder.” Corr, vol. abs/1704.05155, 2017.
    • 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.
    • Zhang, Y., et al. “Deconvolutional paragraph representation learning.” Advances in Neural Information Processing Systems, vol. 2017-December, Jan. 2017, pp. 4170–80.
    • Zhang, Y., et al. “Stochastic gradient monomial gamma sampler.” 34th International Conference on Machine Learning, Icml 2017, vol. 8, Jan. 2017, pp. 6083–92.
    • 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.
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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.
    • 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.
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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.
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    • Gan, Zhe, et al. “Unsupervised Learning of Sentence Representations using Convolutional Neural Networks.” Corr, vol. abs/1611.07897, 2016.
    • 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.
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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.
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    • Gan, Zhe, et al. “Deep temporal sigmoid belief networks for sequence modeling.” Advances in Neural Information Processing Systems, 2015, pp. 2467–75.
    • 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.
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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.
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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.
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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.
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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.
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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.
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    • Henao, Ricardo, and Joseph E. Lucas. Efficient hierarchical clustering for continuous data. Apr. 2012.
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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.
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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.
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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.
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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.
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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.
    • 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.
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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.
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    • Walder, Christian, et al. “Semi-Supervised Kernel PCA.” Corr, vol. abs/1008.1398, 2010.
    • 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.
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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.
    • 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.
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    • Jeong, Hyeon Ki, et al. Privacy Protection With Facial Deidentification Machine Learning Methods: Can Current Methods Be Applied to Dermatology? (Preprint). Crossref, doi:10.2196/preprints.35431.
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    • Jeong, Hyeon Ki, et al. “Privacy Protection With Facial Deidentification Machine Learning Methods: Can Current Methods Be Applied to Dermatology?” Iproceedings, vol. 6, no. 1, JMIR Publications Inc., pp. e35431–e35431. Crossref, doi:10.2196/35431.
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  • Book Sections

    • 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.
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  • Conference Papers

    • Chapfuwa, P., et al. “Enabling counterfactual survival analysis with balanced representations.” Acm Chil 2021  Proceedings of the 2021 Acm Conference on Health, Inference, and Learning, 2021, pp. 133–45. Scopus, doi:10.1145/3450439.3451875.
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    • Dov, D., et al. “Affinitention nets: Kernel perspective on attention architectures for set classification with applications to medical text and images.” Acm Chil 2021  Proceedings of the 2021 Acm Conference on Health, Inference, and Learning, 2021, pp. 14–24. Scopus, doi:10.1145/3450439.3451856.
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    • Assaad, Serge, et al. “Counterfactual Representation Learning with Balancing Weights.” Aistats, edited by Arindam Banerjee and Kenji Fukumizu, vol. 130, PMLR, 2021, pp. 1972–80.
    • Subramanian, Vivek, et al. “SpanPredict: Extraction of Predictive Document Spans with Neural Attention.” Naacl Hlt, edited by Kristina Toutanova et al., Association for Computational Linguistics, 2021, pp. 5234–58.
    • Xiu, Zidi, et al. “Variational Disentanglement for Rare Event Modeling.” Aaai, AAAI Press, 2021, pp. 10469–77.
    • Chapfuwa, Paidamoyo, et al. “Survival cluster analysis.” Chil, edited by Marzyeh Ghassemi, ACM, 2020, pp. 60–68.
    • Chen, L., et al. “Sequence generation with optimal-transport-enhanced reinforcement learning.” Aaai 2020  34th Aaai Conference on Artificial Intelligence, 2020, pp. 7512–20.
    • Engelhard, Matthew, et al. “Neural Conditional Event Time Models.” Mlhc, edited by Finale Doshi-Velez et al., vol. 126, PMLR, 2020, pp. 223–44.
    • Xiu, Zidi, et al. “Variational learning of individual survival distributions.” Chil, edited by Marzyeh Ghassemi, ACM, 2020, pp. 10–18.
    • Xu, Hongteng, et al. “Learning Autoencoders with Relational Regularization.” Icml, vol. 119, PMLR, 2020, pp. 10576–86.
    • Yuan, Siyang, et al. “Advancing weakly supervised cross-domain alignment with optimal transport.” Bmvc, BMVA Press, 2020.
    • Wang, R., et al. “Integrating task specific information into pretrained language models for low resource fine tuning.” Findings of the Association for Computational Linguistics Findings of Acl: Emnlp 2020, 2020, pp. 3181–86.
    • 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.
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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.
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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.
    • 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.
    • 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.
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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.
    • 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.
    • 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.
    • Rusincovitch, Shelley A., et al. “The Duke Health Data Science Internship Program: Integrating the Educational Mission into Real-World Research.” Amia, AMIA, 2018.
    • 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.
    • 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.
    • 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.
    • 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.
    • Tao, C., et al. “X² generative adversarial network.” 35th International Conference on Machine Learning, Icml 2018, vol. 11, 2018, pp. 7787–96.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • Tao, Chenyang, et al. “Variational Inference and Model Selection with Generalized Evidence Bounds.” International Conference on Machine Learning, Vol 80, vol. 80, 2018.
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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.
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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.
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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.
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    • Komives, Eugenie, et al. “Guiding Principles for the Duke Connected Care Predictive Modeling Pilot.” Amia, AMIA, 2017.
    • 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.
    • Pu, Y., et al. “VAE learning via Stein variational gradient descent.” Advances in Neural Information Processing Systems, vol. 2017-December, 2017, pp. 4237–46.
    • 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.
    • 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.
    • Zhang, Yizhe, et al. “Deconvolutional Paragraph Representation Learning.” Nips, edited by Isabelle Guyon et al., 2017, pp. 4169–79.
    • Li, Chunyuan, et al. “ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching.” Advances in Neural Information Processing Systems 30 (Nips 2017), edited by I. Guyon et al., vol. 30, NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), 2017.
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    • Pu, Yunchen, et al. “VAE Learning via Stein Variational Gradient Descent.” Advances in Neural Information Processing Systems 30 (Nips 2017), edited by I. Guyon et al., vol. 30, NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), 2017.
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    • Pu, Yunchen, et al. “Adversarial Symmetric Variational Autoencoder.” Advances in Neural Information Processing Systems 30 (Nips 2017), edited by I. Guyon et al., vol. 30, NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), 2017.
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    • Zhang, Yizhe, et al. “Stochastic Gradient Monomial Gamma Sampler.” International Conference on Machine Learning, Vol 70, vol. 70, 2017.
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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.
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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.
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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.
    • 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.
    • 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.
    • Zhang, Y., et al. “Laplacian Hamiltonian Monte Carlo.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9851 LNAI, 2016, pp. 98–114. Scopus, doi:10.1007/978-3-319-46128-1_7.
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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.
    • Zhang, Yizhe, et al. “Learning a Hybrid Architecture for Sequence Regression and Annotation.” Aaai, edited by Dale Schuurmans and Michael P. Wellman, AAAI Press, 2016, pp. 1415–21.
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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.
    • Song, Zhao, et al. “Learning sigmoid belief networks via Monte Carlo expectation maximization.” Artificial Intelligence and Statistics, 2016, pp. 1347–55.
    • Henao, Ricardo, et al. “Plasma Metabolomic Profiles Predict Advanced Hepatic Fibrosis in Patients with Nonalcoholic Fatty Liver Disease.” Hepatology, vol. 62, WILEY-BLACKWELL, 2015, pp. 290A-291A.
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    • Gan, Zhe, et al. “Learning deep sigmoid belief networks with data augmentation.” Artificial Intelligence and Statistics, 2015, pp. 268–76.
    • Gan, Zhe, et al. “Deep Temporal Sigmoid Belief Networks for Sequence Modeling.” Nips, edited by Corinna Cortes et al., 2015, pp. 2467–75.
    • Henao, R., et al. “Deep poisson factor modeling.” Advances in Neural Information Processing Systems, vol. 2015-January, 2015, pp. 2800–08.
    • Lian, W., et al. “A multitask point process predictive model.” 32nd International Conference on Machine Learning, Icml 2015, vol. 3, 2015, pp. 2030–38.
    • Raiy, P., et al. “Large-scale Bayesian multi-label learning via topic-based label embeddings.” Advances in Neural Information Processing Systems, vol. 2015-January, 2015, pp. 3222–30.
    • Gan, Zhe, et al. “Scalable Deep Poisson Factor Analysis for Topic Modeling.” Icml, 2015.
    • Henao, R., et al. “Bayesian nonlinear support vector machines and discriminative factor modeling.” Advances in Neural Information Processing Systems, vol. 2, no. January, 2014, pp. 1754–62.
    • Ouinones, Quintin J., et al. “Proteomnic and Metabolomic Profiling After Isehemia-Reperfusion Injury Reveals Bottlenecks That Underlie Cardioprotection in Hibernating Mammals.” Circulation, vol. 128, no. 22, LIPPINCOTT WILLIAMS & WILKINS, 2013.
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    • Henao, R., and O. Winther. “Bayesian sparse factor models and DAGs inference and comparison.” Advances in Neural Information Processing Systems 22  Proceedings of the 2009 Conference, 2009, pp. 736–44.
    • Alvarez, M., and R. Henao. “Probabilistic kernel principal component analysis through time.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4232 LNCS, 2006, pp. 747–54. Scopus, doi:10.1007/11893028_83.
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    • Alvarez, Mauricio, et al. “Kernel principal component analysis through time for voice disorder classification.” 2006 28th Annual International Conference of the Ieee Engineering in Medicine and Biology Society, Vols 1 15, IEEE, 2006, pp. 2620-+.
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    • Rengifo, Carlos Lino, et al. “Active learning on the classification of voice pathologies.” Odyssey, edited by Javier Ortega-Garcia et al., ISCA, 2004, pp. 271–74.

  • Preprints

    • Yang, Tianqi, and Ricardo Henao. “TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile.” BioRxiv, 19 Feb. 2022. Epmc, doi:10.1101/2022.02.15.480482.
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    • Isik, Yamac Alican, et al. Flexible Triggering Kernels for Hawkes Process Modeling. 3 Feb. 2022.
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• Teaching & Mentoring
• 

  Recent Courses

  • ECE 392: Projects in Electrical and Computer Engineering 2022
  • ECE 494: Projects in Electrical and Computer Engineering 2022
  • ECE 891: Internship 2022
  • EGR 393: Research Projects in Engineering 2022
  • ECE 392: Projects in Electrical and Computer Engineering 2021
  • ECE 493: Projects in Electrical and Computer Engineering 2021
  • ECE 891: Internship 2021
  • EGR 393: Research Projects in Engineering 2021
  • ECE 891: Internship 2020
  • EGR 190: Special Topics in Engineering 2020
  • EGR 393: Research Projects in Engineering 2020
  • EGR 590: Special Topics in Engineering 2020
• Scholarly, Clinical, & Service Activities
• 

  Outreach & Engaged Scholarship

  • Bass Connections Faculty Team Member. Data+. June 2016 - July 2016  2016

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