Research Interests
Learning and Inference for Random Objects Theory-backed methods and inference for analyzing metric space–valued data, aka random objects, with applications in brain imaging studies, child neurological development, traffic network analysis, social network analysis, genetics, and compositional data.
Functional and Longitudinal Data Analysis Statistical analysis of dynamic metric space data, including time-varying networks, distributions, and tree-valued objects, and their connections with metric geometry.
Modeling and Inference for Dynamic Networks Generating mechanisms, temporal evolution, and network archaeology.
Online Learning Problems Learning and inference for streaming object-valued data.