Mathematical Foundations of Deep Learning

Dr. Xiaojing Ye is a Professor of Mathematics at Georgia State University in Atlanta, USA. His research interests lie in applied and computational mathematics, with a particular focus on numerical methods that integrate deep learning techniques for scientific computing. His work also spans network science, numerical optimization, image processing, and related interdisciplinary areas.Dr. Ye began his undergraduate studies at Peking University in China in 2001, initially majoring in chemistry. Motivated by a growing interest in mathematics and physics, he transferred to the mathematics major in 2002 while pursuing physics as a minor. He received his Bachelor’s degree in Mathematics major in July 2005. After one year of professional experience, he returned to academia to pursue graduate studies at the University of Florida in the USA, supported by a prestigious four-year university alumni fellowship. Dr. Ye earned a Master’s degree in Statistics in 2009 and completed his Ph.D. in Mathematics in May 2011. He subsequently served as a Visiting Assistant Professor in the School of Mathematics at the Georgia Institute of Technology for two years. In 2013, he joined the Department of Mathematics and Statistics at Georgia State University as a tenure-track Assistant Professor, where he was awarded tenure and later promoted to Full Professor.

• 1. Deep Neural Networks. 2 Network Training. 3 Deep Optimal Control. 4 Deep Reinforcement Learning. 5 Generative Models.