I'm a 5th year Ph.D. student in Statistical Science at Duke advised by Katherine Heller and funded on an NDSEG fellowship. I work on developing new (mostly Bayesian) machine learning methods to solve real clinical problems in the Duke healthcare system. In the past I've worked on models to predict surgical complications, and on time series and event-time models to predict disease progression and adverse events in patients with chronic kidney disease. Recently, I've been working on a deep learning model that we're deploying in Duke Hospital to improve early detection of sepsis. Before Duke, I obtained my AB in mathematics at Dartmouth College working with Dan Rockmore on time-varying topic models and on scalable inference in Bayesian network models.
Futoma, J, Sendak, M, Cameron, CB, and 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 (January 1, 2016): 222-231.