Machine Learning
Lin Yang
Dr. Lin Yang is an assistant professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. His research focuses on developing and applying fast algorithms for machine learning and data science. His current research focus is on reinforcement learning theory and applications, learning for control, non-convex optimization, and streaming algorithms. Previously, he was a postdoc at Princeton University. He obtained two Ph.D. degrees (in Computer Science and in Physics & Astronomy) from Johns Hopkins University. He was a recipient of the Simons’ Research Fellowship and Dean Robert H. Roy Fellowship.
Harry Xu
Cho-Jui Hsieh
Quanquan Gu
Xiang Anthony Chen
Anthony’s research mission is to expand the interaction bandwidth between human and AI, specifically, enabling domain-specific users to comprehend and control AI with an eventual vision of human-AI collaboration. He received his Ph.D. in the School of Computer Science at Carnegie Mellon University in 2017 and was a recipient of the Hellman Fellowship, NSF CISE CRII Award and the Adobe Ph.D. Fellowship. Anthony’s work has won two best paper awards and two honorable mentioned in top-tier HCI conferences.
Yizhou Sun
Kai-Wei Chang
I am an associate professor in the Department of Computer Science at the UCLA Samueli School of Engineering. My research goal is to build intelligence systems that solve real-world problems by automatically acquiring knowledge. This challenging goal involves two fundamental components: A machine learning component that can efficiently make coherent decisions for problems with complex structures, and a natural language understanding component that enables the system to extract knowledge from unstructured text. I have been published broadly in machine learning, natural language processing, artificial intelligence, and data mining.






