Katherine Heller

Katherine Heller

Assistant Professor of Statistical Science

Overview

Education & Training

  • NSF Postdoctoral Fellow, Computational Cognitive Science Group, Massachusetts Institute of Technology 2011 - 2011

  • EPSRC Postdoctoral Fellow, Engineering Department, University of Cambridge (UK) 2008 - 2011

  • Ph.D., University College London (United Kingdom) 2008

  • M.S., Columbia University 2003

  • B.S., State University of New York at Stony Brook 2000

Selected Grants

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

Advancing Artificial Intelligence for the Naval Domain awarded by Office of Naval Research (Co-Principal Investigator). 2018 to 2022

Postdoctoral Training in Genomic Medicine Research awarded by National Institutes of Health (Co-Mentor). 2017 to 2022

Building Better Teams: A Network Analysis Approach awarded by (Co-Principal Investigator). 2018 to 2021

+ awarded by National Science Foundation (Principal Investigator). 2016 to 2021

Bioinformatics and Computational Biology Training Program awarded by National Institutes of Health (Mentor). 2005 to 2020

An Integrated Nonparametric Bayesian and Deep Neural Network Framework for Biologically-Inspired Lifelong Learning awarded by Defense Advanced Research Projects Agency (Principal Investigator). 2018 to 2020

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

Pages

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

Williamson, S., et al. “Nonparametric mixed membership modelling using the IBP compound dirichlet process.” Mixtures: Estimation and Applications, 2011, pp. 145–60. Scopus, doi:10.1002/9781119995678.ch7. Full Text

Blundell, C., et al. “Discovering nonbinary hierarchical structures with Bayesian rose trees.” Mixtures: Estimation and Applications, 2011, pp. 161–87. Scopus, doi:10.1002/9781119995678.ch8. Full Text

Wiens, Jenna, et al. “Author Correction: Do no harm: a roadmap for responsible machine learning for health care..” Nature Medicine, vol. 25, no. 10, Oct. 2019. Epmc, doi:10.1038/s41591-019-0609-x. Full Text

Wang, Shangying, et al. “Massive computational acceleration by using neural networks to emulate mechanism-based biological models..” Nature Communications, vol. 10, no. 1, Sept. 2019. Epmc, doi:10.1038/s41467-019-12342-y. Full Text

Wiens, Jenna, et al. “Do no harm: a roadmap for responsible machine learning for health care..” Nature Medicine, vol. 25, no. 9, Sept. 2019, pp. 1337–40. Epmc, doi:10.1038/s41591-019-0548-6. Full Text

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

Futoma, J., et al. “Scalable joint modeling of longitudinal and point process data for disease trajectory prediction and improving management of chronic kidney disease.” 32nd Conference on Uncertainty in Artificial Intelligence 2016, Uai 2016, Jan. 2016, pp. 222–31.

Jiang, Jiefeng, et al. “An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands..” Nat Commun, vol. 6, Sept. 2015. Pubmed, doi:10.1038/ncomms9165. Full Text

Jiang, Jiefeng, et al. “Bayesian modeling of flexible cognitive control..” Neuroscience and Biobehavioral Reviews, vol. 46 Pt 1, Oct. 2014, pp. 30–43. Epmc, doi:10.1016/j.neubiorev.2014.06.001. Full Text

Letham, B., et al. “Growing a list.” Data Mining and Knowledge Discovery, vol. 27, no. 3, Dec. 2013, pp. 372–95. Scopus, doi:10.1007/s10618-013-0329-7. Full Text

Pages

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

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

Guo, F., et al. “The Bayesian echo chamber: Modeling social inuence via linguistic accommodation.” Journal of Machine Learning Research, vol. 38, 2015, pp. 315–23.

Wang, X., et al. “Parallelizing MCMC with random partition trees.” Advances in Neural Information Processing Systems, vol. 2015-January, 2015, pp. 451–59.

Fan, K., et al. “Fast second-order stochastic backpropagation for variational inference.” Advances in Neural Information Processing Systems, vol. 2015-January, 2015, pp. 1387–95.