Machine learning and Bayesian statistics. Discovering the latent structure in data.
Applying machine learning to find underlying structure within neurological disease.
Variational inference for Bayesian deep generative models and GPU computing with healthcare applications.
Bayesian models that support clinical decision making, to solve real world healthcare problems.
Transfer learning and clustering techicals to predict surgical complications at Duke Hospital.
Understanding brains, including neuronal recording and functional imaging data.
Generative machine learning approaches to biological data in order to understand natural processes.
Implementing variational inference methods for nonparameteric models that infer latent states.
Studying autism and it's connection with endogenous neural electromagnetic fields.
Runs the Predictive Analysis Lab to develop machine learning models that are transparent to human experts.
Developing interpretable models for predictive tasks in biomedical and clinical research.