Xiangyu Yue is an assistant professor in the Department of Information Engineering at The Chinese University of Hong Kong. He took a bachelor's degree in automation at Nanjing University in 2014, a master's degree in electrical engineering at Stanford University in 2016 and a PhD from the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where he received the Lotfi A. Zadeh Prize for outstanding doctoral graduates. He has carried out research at Google Research, Google [X] Robotics, the Baidu AI Lab and the Tencent AI Lab. His research spans machine learning, transfer learning, label-efficient learning and computer vision, with applications in autonomous driving, robotics and medical imaging.
Yue's work addresses the practical shortage of labels in machine learning: transfer learning and label-efficient learning both aim to keep models reliable when annotated data are scarce, with applications from autonomous driving to medical imaging.
Achievements
- 2016 · Completed a master's degree in electrical engineering at Stanford University.
- 2022 · Completed his PhD at Berkeley and received the Lotfi A. Zadeh Prize.
- 2023 · Joined CUHK's Department of Information Engineering.