Sui Tang's research interests are situated at the intersection of several disciplines, including Statistical Learning, Harmonic Analysis, Approximation Theory, and Probability. Her primary focus is on tackling challenges in machine learning, inverse problems, signal processing, and dynamical systems. She is currently dedicating her efforts to AI for science, with a specific emphasis on utilizing data-driven approaches for modeling complex systems.
Sui Tang is an Assistant Professor at the Department of Mathematics at the University of California Santa Barbara. She received her PhD in Mathematics from Vanderbilt University in 2016 and worked as an Assistant Research Professor in the Department of Mathematics at Johns Hopkins University from 2016 to 2020. In the fall of 2021, she was a visiting scientist at the Simons Institute at UC Berkeley.