About the Speaker:

Minh Ha is currently a Unit Leader (equivalent to Associate Professor) at the RIKEN Center for Advanced Intelligence Project (RIKEN-AIP) in central Tokyo, Japan, where he leads the Functional Analytic Learning Unit. Prior to joining the RIKEN-AIP, he was a researcher at the Pattern Analysis and Computer Vision (PAVIS) group, at the Istituto Italiano di Tecnologia (IIT) – Italian Institute of Technology, in Genova (Genoa), Italy.  He received his PhD in Mathematics from Brown University, Providence, RI, USA, under the supervision of Steve Smale, and his dissertation was on Reproducing Kernel Hilbert Spaces (RKHS).

Minh Ha interested in both the mathematical foundations and algorithmic developments in machine learning, AI, computer vision, and image and signal processing, and problems in applied and computational functional analysis, and applied and computational differential geometry.

His current research focuses on the following two principal directions, which are closely related

  1. Functional analytic methods in machine learning, including in particular methods from matrix and operator theory, and the theory of vector-valued Reproducing Kernel Hilbert Spaces (RKHS)
  2. Geometrical methods in machine learning, including in particular methods from Riemannian Geometry, Information Geometry, Optimal Transport,  and related areas. His current focus is on the Geometry of Covariance Operators (especially RKHS covariance operators) and Gaussian Measures, along with their applications. The RKHS setting, in particular, provides mathematically rigorous formulations for the kernelized versions of the Kullback-Leibler, Renyi, Stein, Bures-Wasserstein, Fisher-Rao divergences/distances, among others

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