His research lies at the intersection of applied harmonic analysis, machine learning and signal processing. His recent work focuses on geometric deep learning and the development of robust, geometry-aware data representations. His teaching interests at Duke Kunshan include calculus, linear algebra, machine learning, and deep learning.

He has published in leading journals such as Applied and Computational Harmonic Analysis, SIAM Journal on Mathematics of Data Science, IEEE Transactions on Information Theory, and IEEE Transactions on Neural Networks and Learning Systems, as well as top conferences including ICLR, KDD, ECCV, and AISTATS. He is also a co-author of the textbook “Lecture Notes in Deep Learning” (World Scientific Publishing, 2025).

Zou has a B.Sc. (First Honour) in Mathematics from the Chinese University of Hong Kong and a Ph.D. in Applied Mathematics from the University of Maryland, College Park. Before joining Duke Kunshan, he was a postdoctoral researcher at the University of Minnesota, Twin Cities.