Dr. Yangyang Xu earned a bachelor degree in Computational Mathematics from Nanjing University, a master degree from the Institute of Applied Mathematics at Chinese Academy of Sciences, and his Ph.D from the Department of Computational and Applied Mathematics at Rice University in 2014. Before joining RPI, Dr. Xu was an assistant professor at University of Alabama. He also spent one year as a postdoctoral fellow at University of Waterloo and another year as an NSF postdoc at University of Minnesota.
Dr. Xu's broad research interests are optimization theory and methods and their applications such as in machine learning, statistics, and signal processing. He worked on developing algorithms for compressed sensing, matrix completion, and tensor factorization and learning. Recently, his research focuses on first-order methods, operator splitting, stochastic optimization methods, and high performance parallel computing. These works are motivated by very "big" problems arising in machine learning and image processing.
Optimization has played a significant role in many areas such as engineering, sciences, health care. My research focuses on continuous optimization and is mainly motivated by “big” problems arising from image processing, machine learning, statistics, finance, and so on. Currently, we work on first-order optimization methods for solving constrained nonlinear programs and stochastic optimization with applications in deep learning.
3:50pm - 4:50pm on Tuesday and Friday
MATP6960: Stochastic Optimization and Reinforcement Learning
The following is a selection of recent publications in Scopus. Yangyang Xu has 36 indexed publications in the subjects of Mathematics, Computer Science, and Engineering.