Jiayang is a PhD student at the NYU Center for Data Science (Shanghai track), advised by Mathieu Laurière and Shuyang Ling. She is broadly interested in theoretical machine learning and deep learning, with a particular emphasis on investigating the mathematical tools that facilitate their interpretation. Currently, she is also interested in the branches of mathematics such as stochastic analysis, high-dimensional probability/statistics, and random matrix theory with their applications in data science. Prior to joining NYU in 2023, Jiayang obtained an M.Sc. in Statistics from the University of British Columbia and a B.Sc. in Statistics from East China Normal University.
Research Interests
- Stochastic Analysis
- Deep learning theory
- Generative model