Scholars@Duke

• Background
• 

  Education, Training, & Certifications

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

  Duke Appointment History

  • 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

  • MAR 16, 2016

    New Collaborative Seed Grant Program Gives Eight Awards
• Research
• 

  Selected Grants

  • Novel Approaches to Infant Screening for ASD in Pediatric Primary Care awarded by National Institutes of Health 2019 - 2024
  • Clinical and Molecular Epidemiology of High Risk Coronary Plaque awarded by National Institutes of Health 2019 - 2023
  • Non-Invasive Diagnosis of Pediatric Pulmonary Invasive Mold Infections awarded by National Institutes of Health 2018 - 2023
  • Mapping Epigenetic Memory of Exposure New To Observe (MEMENTO) awarded by Defense Advanced Research Projects Agency 2019 - 2021
  • Postdoctoral Training in Genomic Medicine Research awarded by National Institutes of Health 2017 - 2021
  • Resilience Prediction to RSV Infection 2018 - 2020
  • One-Carbon Metabolism Genetics Data Analyses 2020
  • Enhanced X-ray Angiography Analysis and Interpretation Using Deep Learning 2019 - 2020
  • QuBBD: Deep Poisson Methods for Biomedical Time-to-Event and Longitudinal Data awarded by National Institutes of Health 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
  • PREdicting contagion using Systems And GEnomic analysis (PRESAGE) awarded by Defense Advanced Research Projects Agency 2017 - 2020
  • Abbott Diagnostics Collaborative Sponsored Research Agreement 2016 - 2019
  • Role of the Stroma in Fibrolamellar Hepatocellular Carcinoma 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 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 2016 - 2017
  • Feasibility for Predicting Warfighter Health Using Transcriptional Markers on the MAP Platform 2016
• 

  External Relationships

  • Infinia ML Inc.
  • Predigen, Inc.
  • Vigilant Medical Inc
• Publications & Artistic Works
• 

  Selected Publications

  • Academic Articles

    • 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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    • Draelos, Rachel Lea, et al. “Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes.” Corr, vol. abs/2002.04752, 2020.
    • Engelhard, Matthew, et al. “Neural Conditional Event Time Models.” Corr, vol. abs/2004.01376, 2020.
    • 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.
    • Du, Kuo, et al. “Increased Glutaminolysis Marks Active Scarring in Nonalcoholic Steatohepatitis Progression.” Cell Mol Gastroenterol Hepatol, vol. 10, no. 1, Dec. 2019, 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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    • 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.” Mlhc, edited by Finale Doshi-Velez et al., vol. 106, PMLR, 2019, pp. 553–70.
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    • Dov, David, et al. “A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images.” Corr, vol. abs/1904.12739, 2019.
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    • Dov, David, et al. “Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images.” Corr, vol. abs/1904.00839, 2019.
    • Liang, Kevin J., et al. “Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods.” Corr, vol. abs/1910.04233, 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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    • 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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    • Wang, Rui, et al. “Discriminative Clustering for Robust Unsupervised Domain Adaptation.” Corr, vol. abs/1905.13331, 2019.
    • Wang, Wenlin, et al. “Improving Textual Network Learning with Variational Homophilic Embeddings.” Corr, vol. abs/1909.13456, 2019.
    • 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.
    • 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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    • 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.
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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.
    • 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.
    • 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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    • Zhang, Xinyuan, et al. “Multi-Label Learning from Medical Plain Text with Convolutional Residual Models.” Corr, vol. abs/1801.05062, 2018.
    • 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.
    • 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.
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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.
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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.
    • 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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    • 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, 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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    • Henao, Ricardo, and Joseph E. Lucas. Efficient hierarchical clustering for continuous data.
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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, Paidamoyo, et al. “Survival cluster analysis.” Chil, edited by Marzyeh Ghassemi, ACM, 2020, pp. 60–68.
    • Xiu, Zidi, et al. “Variational learning of individual survival distributions.” Chil, edited by Marzyeh Ghassemi, ACM, 2020, pp. 10–18.
    • 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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    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • Li, Chunyuan, et al. “ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching.” Nips, edited by Isabelle Guyon et al., 2017, pp. 5495–503.
    • Pu, Y., et al. “VAE learning via Stein variational gradient descent.” Advances in Neural Information Processing Systems, vol. 2017-December, 2017, pp. 4237–46.
    • Pu, Yunchen, et al. “VAE Learning via Stein Variational Gradient Descent.” Nips, edited by Isabelle Guyon et al., 2017, pp. 4236–45.
    • Pu, Yunchen, et al. “Adversarial Symmetric Variational Autoencoder.” Nips, edited by Isabelle Guyon et al., 2017, pp. 4330–39.
    • 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. “Stochastic Gradient Monomial Gamma Sampler.” Icml, edited by Doina Precup and Yee Whye Teh, vol. 70, PMLR, 2017, pp. 3996–4005.
    • 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.
    • 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.
    • 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.
    • 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. “Towards Unifying Hamiltonian Monte Carlo and Slice Sampling.” Nips, edited by Daniel D. Lee et al., 2016, pp. 1741–49.
    • 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, Z., et al. “Learning deep sigmoid belief networks with data augmentation.” Journal of Machine Learning Research, vol. 38, 2015, pp. 268–76.
    • Gan, Z., et al. “Scalable deep Poisson factor analysis for topic modeling.” 32nd International Conference on Machine Learning, Icml 2015, vol. 3, 2015, pp. 1823–32.
    • 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.
    • 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.
    • 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.
• Teaching & Mentoring
• 

  Recent Courses

  • 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
  • COMPSCI 394: Research Independent Study 2019
  • ECE 391: Projects in Electrical and Computer Engineering 2019
  • ECE 899: Special Readings in Electrical Engineering 2019
  • POE 190: Practice Oriented Education 2019
• Scholarly, Clinical, & Service Activities
• 

  Outreach & Engaged Scholarship

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

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