Zhidan Liu

Zhidan Liu (刘志丹) is currently an Assistant Professor affiliated with the Thrust of Intelligent Transportation, Systems Hub, and the Thrust of Artificial Intelligence, Information Hub, at The Hong Kong University of Science and Technology (Guangzhou). He is also a Cross-Campus Faculty Affiliate at The Hong Kong University of Science and Technology. Before joining HKUST (GZ), he was a faculty member with College of Computer Science and Software Engineering, Shenzhen University (2017-2024), and a postdoctoral research fellow at School of Computer Science and Engineering, Nanyang Technological University (2015-2017), working closely with Prof. Mo Li. He received the B.E. degree and Ph.D. degree, both in computer science and technology, from Northeastern University in June 2009 and Zhejiang University in September 2014, respectively. He is now heading the MobiX research group at HKUST (GZ). He is on the editorial board of Journal of Social Computing and IEEE Transactions on Emerging Topics in Computational Intelligence (IF=6.5). He is a senior member of IEEE and CCF, a member of ACM.

My CV is available here, and my publications are listed on Google Scholar.

Research Interests

• Intelligent Systems & Edge Intelligence: Advancing intelligent systems through efficient sensing, learning, reasoning, and deployment across mobile, edge, and resource-constrained environments. My research explores how cutting-edge AI can be integrated into real-world systems to enable scalable and trustworthy intelligence.

• Artificial Internet of Things (AIoT) & Human-Centric Computing: Developing next-generation AIoT systems that seamlessly combine sensing, communication, and intelligence to understand human behaviors and interactions, enabling pervasive and human-centered intelligent services.

• Smart Mobility & Urban Intelligence: Leveraging large-scale urban and mobility data to improve transportation efficiency, mobility services, and urban operations through data-driven modeling, prediction, optimization, and decision-making.

• Spatio-Temporal Data Analytics & Foundation Models: Building intelligent methods for learning from complex spatio-temporal, graph, and multimodal data, while exploring foundation models that can generalize across diverse domains, environments, and real-world applications.

Hiring: We are consistently seeking self-motivated Ph.D. students, RBM, Research Assistants, and Postdoc. If interested, please feel free to send an email with your information (e.g., resume, transcripts, research proposal, and publications if any). Please read this file for more details (in Chinese). 招聘详情请参考此文件以及公众号文章.