Statistics on non-Euclidean object-valued data-
developing broadly applicable statistical methods and inference for analyzing non-Euclidean data residing in abstract metric spaces, with applications in brain imaging studies, mortality and life expectancy distributions, child neurological development, traffic network analysis, and genetics data.
Functional and longitudinal data analysis, and its overlap with metric geometry for studying samples of time-varying metric space-valued data, examples being dynamic networks or distribution objects
Reproducing kernel Hilbert spaces- developing abstract mathematical and computational methods for infinite dimensional data in connection with metric geometry and sufficient dimension reduction.
Nonparametric statistics, analysis of high-dimensional and geometrical data.
Causal inference for distributional and object data- developing methodological work for object data analysis with application in the fields of bio-sciences.
