Time & Date: 10:30 - 11:30 AM on September 6 (Friday), Beijing Time

Venue: Room 203, Teaching Complex C

Speaker: Prof. Ningning DING, Hong Kong University of Science and Technology (Guangzhou)

Host: Prof. Jianwei HUANG, The Chinese University of Hong Kong, Shenzhen

Abstract: 

In the rapidly evolving landscape of artificial intelligence (AI), the intricate interplay of AI technology, human engagement, and networking dynamics brings forth a mosaic of challenges and opportunities. The complex coupling of AI and network systems is apparent in several areas. For example, in federated learning setups, distributed AI nodes form a network to collaboratively train machine learning models; Internet of Things (IoT) networks connect numerous devices that collect massive data, enabling AI-driven analytics. While the literature has made significant contributions to algorithmic enhancements to AI and network performance, there remain understudied challenges tied to human participation, encompassing factors like the willingness to participate, strategic self-interest, and the handling of private and dynamic information. In this talk, I will focus on the interdisciplinary area involving AI, network systems, and network economics to address these challenges, and highlight human-aware optimization in AI-network systems to enhance efficiency, privacy, and social welfare. Specifically, I will introduce network mechanism designs tailored for federated learning and unlearning frameworks, as well as IoT systems.

Biography

Ningning Ding is a Tenure-Track Assistant Professor in the Data Science and Analytics Thrust at the Hong Kong University of Science and Technology (Guangzhou). Before that, she was a Postdoctoral Scholar in the Department of Electrical and Computer Engineering at Northwestern University, USA. She received her Ph.D. in Information Engineering from The Chinese University of Hong Kong in 2022 and her B.S. degree in Information Science and Engineering from Southeast University in 2018. Her research focuses on interdisciplinary areas of artificial intelligence, network systems, and network economics, with a current emphasis on federated learning, machine unlearning, and data trading. Her work has been published in prestigious journals and conferences, including IEEE JSAC, IEEE TMC, IEEE INFOCOM, ACM MobiHoc, and ACM Sigmetrics.