His primary research is in the intersection among applied harmonic analysis, machine learning and signal processing. He is especially interested in robust representations and structures in geometric and graph deep learning. His teaching interests at Duke Kunshan include calculus, linear algebra, principles of machine learning, statistical machine learning.
He has had papers published in leading academic journals in various areas including Applied and Computational Harmonic Analysis and IEEE Transactions on Information Theory, and at and conferences such as the International Conference on Learning Representations.
Zou has a B.Sc. 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 served as a post-doctorate researcher at the University of Minnesota, Twin Cities.