Assistant Research Professor of Statistical Science
Neurobiology Training Program awarded by National Institutes of Health (Mentor). 2019 to 2024
HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by National Science Foundation (Senior Investigator). 2019 to 2022
Building Better Teams: A Network Analysis Approach (Research Areas of Interest: 2. Leader Development, 3. Personnel Testing and Performance, and 4. Organizational Effectiveness) awarded by U.S. Army Research Inst. for Behavioral and Social Sciences (Co-Principal Investigator). 2018 to 2021
Bioinformatics and Computational Biology Training Program awarded by National Institutes of Health (Mentor). 2005 to 2021
Postdoctoral Training in Genomic Medicine Research awarded by National Institutes of Health (Co-Mentor). 2017 to 2021
CAREER: Interacting Dynamic Bayesian Models for Social Behavior and Reasoning awarded by National Science Foundation (Principal Investigator). 2016 to 2021
Basic predoctoral training in neuroscience awarded by National Institutes of Health (Training Faculty). 1992 to 2018
BRAIN EAGER: Bayesian Models of Translational Neural Networks: Motivation and Reward awarded by National Science Foundation (Principal Investigator). 2014 to 2017
Collaborative Research: Workshop for Women in Machine Learning awarded by National Science Foundation (Principal Investigator). 2013 to 2016
Bayesian Models of Social Behavior Using Online Resources awarded by National Science Foundation (Principal Investigator). 2013 to 2015
Ghahramani, Z., et al. “A simple and general exponential family framework for partial membership and factor analysis.” Handbook of Mixed Membership Models and Their Applications, 2014, pp. 67–88. Scopus, doi:10.1201/b17520. Full Text
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Bu, F., et al. “SMOGS: Social network metrics of game success.” Aistats 2019 22nd International Conference on Artificial Intelligence and Statistics, Jan. 2020.
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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Vu, Mai-Anh T., et al. “A Shared Vision for Machine Learning in Neuroscience.” J Neurosci, vol. 38, no. 7, Feb. 2018, pp. 1601–07. Pubmed, doi:10.1523/JNEUROSCI.0508-17.2018. Full Text
Madlon-Kay, Seth, et al. “Using Machine Learning to Discover Latent Social Phenotypes in Free-Ranging Macaques.” Brain Sci, vol. 7, no. 7, July 2017. Pubmed, doi:10.3390/brainsci7070091. Full Text
Dusenberry, M. W., et al. “Analyzing the role of model uncertainty for electronic health records.” Acm Chil 2020 Proceedings of the 2020 Acm Conference on Health, Inference, and Learning, 2020, pp. 204–13. Scopus, doi:10.1145/3368555.3384457. Full Text
Wei, Q., et al. “InverseNet: Solving inverse problems of multimedia data with splitting networks.” Proceedings Ieee International Conference on Multimedia and Expo, vol. 2019-July, 2019, pp. 1324–29. Scopus, doi:10.1109/ICME.2019.00230. Full Text
Jerfel, G., et al. “Reconciling meta-learning and continual learning with online mixtures of tasks.” Advances in Neural Information Processing Systems, vol. 32, 2019.
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
Fai, K., et al. “An Inner-loop Free Solution to Inverse Problems using Deep Neural Networks.” Advances in Neural Information Processing Systems, vol. 2017-December, 2017, pp. 2371–81.
Futoma, J., et al. “Learning to detect sepsis with a multitask Gaussian process RNN classifier.” 34th International Conference on Machine Learning, Icml 2017, vol. 3, 2017, pp. 1914–22.
Tan, X., et al. “Content-based modeling of reciprocal relationships using Hawkes and Gaussian processes.” 32nd Conference on Uncertainty in Artificial Intelligence 2016, Uai 2016, 2016, pp. 726–34.
Fan, K., et al. “A unifying variational inference framework for hierarchical graph-coupled HMM with an application to influenza infection.” 30th Aaai Conference on Artificial Intelligence, Aaai 2016, 2016, pp. 3828–34.
Fan, K., et al. “Hierarchical graph-coupled HMMs for heterogeneous personalized health data.” Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, vol. 2015-August, 2015, pp. 239–48. Scopus, doi:10.1145/2783258.2783326. Full Text
Wang, X., et al. “Parallelizing MCMC with random partition trees.” Advances in Neural Information Processing Systems, vol. 2015-January, 2015, pp. 451–59.