Katherine Heller

Katherine Heller

Assistant Professor of Statistical Science

External address: 
Box 90251, Durham, NC 27708-0251

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

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

+ 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

Pages

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

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, p. 1627. 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, p. 4354. 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

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