My current research is in the area of rational approximation in the complex domain. For example, I investigate the convergence of rational function series on the extended complex plane.
Prof. Zhang develops algebraic and geometric methods for data analysis. Algebraic methods are based on theories of topology and partially ordered sets (in particular lattice theory); an example being formal concept analysis (FCA). Geometric methods include Information Geometry, which studies the manifold of probability density functions. Zhang also has interests in machine learning, including reproducing kernel Banach space (RKBS) and reinforcement learning (RL).
Mahesh Agarwal is Associate Professor of Mathematics and Statistics at the University of Michigan, Dearborn.
Prof. Agarwal’s is primarily interested in number theory, in particular in p-adic L-functions, Bloch-Kato conjecture and automorphic forms. His secondary research interests are polynomials, geometry and math education.
My interests include randomized approximation algorithms for massive data sets, including, specifically, sublinear-time algorithms for sparse recovery in the Fourier and other domains. Other interests include data privacy, including privacy of energy usage data.
My research interests include mathematical analysis, probability, networking, and algorithms. I am especially interested in randomized algorithms with applications to harmonic analysis, signal and image processing, computer networking, and massive datasets.