Dr. Ningning Ding

Assistant Professor

The Hong Kong University of Science and Technology (Guangzhou)

The rapid development of artificial intelligence (AI) applications such as large models has triggered complex interactions among human agents, machine learning systems, and network systems. My research focuses on the interdisciplinary area involving AI, network system, and network economics, with a key contribution of human-aware optimization within AI and network systems to enhance efficiency, privacy, and social welfare.

The complex coupling of AI and network systems is profoundly apparent in several areas:
1) Distributed AI Systems: Built across numerous AI nodes (often representing users or computational workers), distributed AI systems (e.g., federated learning and distributed coded machine learning) form a cohesive network where nodes collaborate or compete based on their objectives.
2) Network Systems: This covers systems like Internet of Things (IoT) and sharing platforms. IoT networks connect numerous devices that collect massive data, enabling AI-driven analytics. Sharing platforms connect sellers and buyers to utilize underused resources, with AI optimizing interactions.

While the literature has made significant strides in algorithmic enhancements to AI performance, there remain understudied challenges tied to human participation in AI and network systems, such as willingness to participate, strategic self-interest, and the handling of private and dynamic information. Network economics is a promising solution to these challenges. A carefully designed network economic mechanism can elicit truthful information, augment system efficiency, and foster cooperation.

To this end, my current emphasis is optimization and resource management for human-machine-things-integrated intelligent networks, such as designing mechanisms for multidimensional resource optimization, heterogeneous agent collaboration, and data privacy protection.