Dr. Asta’s research interests include network inference and non-parametric methods on non-Euclidean spaces. Network inference aims to give statistically rigorous methods of analyzing and comparing brain networks, collaboration networks, and other real-world networks. Non-parametric methods on non-Euclidean spaces adapts non-parametric methods of inference to data that naturally lives in spaces other than Euclidean space; such data for example arises in brain imaging, astrostatistics, and radar tracking.
PhD, Statistics and Engineering & Public Policy, Carnegie Mellon University
MPA, Institut d’études politiques de Paris (Sciences-Po)
BS, Mathematics, University of Chicago