Description

AI is advancing by leaps and bounds in learning algorithm development, but AI has many challenges when put to practice. One of the major challenges faced by AI is the serious lack of data, which has led to the inability of many good algorithm models to be effectively applied. Another major challenge is the need to protect user privacy and security in many AI applications. In this talk, Prof. Yang will present federated learning as a solution designed to connect data silos while protecting user privacy and provide security. Prof. Yang will illustrate some theoretical advances and practical applications.

Speaker Biography

Dr. Qiang Yang is Chief Artificial Intelligence Officer of WeBank and Chair Professor of CSE Department of HKUST. He is the Conference Chair of AAAI-21, President of Hong Kong Society of Artificial Intelligence and Robotics (HKSAIR) , the President of Investment Technology League (ITL) and a former President of IJCAI (2017-2019). He is a fellow of AAAI, ACM, IEEE and AAAS. His research interests include transfer learning and federated learning. He is the founding EiC of two journals: IEEE Transactions on Big Data and ACM Transactions on Intelligent Systems and Technology. His latest books are Transfer Learning and Federated Learning.