Katherine Heller

Katherine Heller

Assistant Professor of Statistical Science

Appointments and Affiliations

  • Assistant Professor of Statistical Science
  • Assistant Professor in the Department of Electrical and Computer Engineering
  • Assistant Professor in Neurobiology
  • Assistant Professor in Neurology
  • Assistant Professor of Computer Science
  • Affiliate of the Duke Initiative for Science & Society
  • Member of the Center for Cognitive Neuroscience

Contact Information

  • Email Address: katherine.heller@duke.edu

Education

  • Massachusetts Institute of Technology, 2011
  • University of Cambridge (UK), 2011
  • Ph.D. University College of London, 2008
  • M.S. Columbia University, 2003
  • B.S. State University of New York at Stony Brook, 2000

Courses Taught

  • BRAINSOC 395-1: Bass Connections in Brain and Society: Interdisciplinary Team Projects
  • BRAINSOC 395: Bass Connections in Brain and Society: Interdisciplinary Team Projects
  • COMPSCI 391: Independent Study
  • COMPSCI 393: Research Independent Study
  • COMPSCI 394: Research Independent Study
  • MATH 230: Probability
  • NEUROSCI 493: Research Independent Study 1
  • NEUROSCI 755: Interdisciplinary Program in Cognitive Neuroscience (IPCN) Independent Research Rotation
  • STA 102: Introductory Biostatistics
  • STA 230: Probability
  • STA 440: Case Studies in the Practice of Statistics
  • STA 493: Research Independent Study
  • STA 571: Advanced Probabilistic Machine Learning
  • STA 582L: DataFest
  • STA 701S: Readings in Statistical Science
  • STA 993: Independent Study

In the News

Representative Publications

  • Madlon-Kay, S; Brent, L; Montague, M; Heller, K; Platt, M, Using Machine Learning to Discover Latent Social Phenotypes in Free-Ranging Macaques., Brain Sciences, vol 7 no. 7 (2017) [10.3390/brainsci7070091] [abs].
  • Fan, K; Zhang, Y; Henao, R; Heller, K, Triply stochastic variational inference for non-linear beta process factor analysis, Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining (2017), pp. 121-130 [10.1109/ICDM.2016.36] [abs].
  • Futoma, J; Sendak, M; Cameron, CB; Heller, K, 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 (2016), pp. 222-231 [abs].
  • Tan, X; Naqvi, SAZ; Qi, A; Heller, KA; Rao, V, Content-based modeling of reciprocal relationships using Hawkes and Gaussian processes, 32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016 (2016), pp. 726-734 [abs].
  • Fan, K; Li, C; Heller, K, 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. 3828-3834 [abs].