Zhidan Liu (刘志丹) is currently an Assistant Professor affiliated with the Thrust of Intelligent Transportation, System Hub, and the Thrust of Artificial Intelligence, Information Hub, at The Hong Kong University of Science and Technology (Guangzhou), and 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 a senior member of IEEE and CCF, a member of ACM.

Please find my CV here.

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.

Recent Highlights:

• 2026.07 - Joint appointment as Assistant Professor of the Thrust of Artificial Intelligence, Information Hub, HKUST (GZ).
• 2026.06 - Invited to serve on the TPC of The 22nd International Conference on Mobility, Sensing and Networking (MSN 2026). Please consider submitting.
• 2026.05 - Our paper “Orchestrating Reasoning and Reaction: An Asynchronous Hierarchical Framework for LLM-driven Traffic Signal Control” has been accepted by ACM KDD 2026. Congrats to Fansheng and Jiyu. 🎉
• 2026.05 - Invited to serve on the TPC of The 21st International Conference on Green, Pervasive, and Cloud Computing (GPC 2026). Please consider submitting.
• 2026.05 - Invited to serve on the TPC of The 18th International Conference on Networking, Architecture, and Storage (NAS 2026). Please consider submitting.
• 2026.04 - Two co-authored papers have been accepted by ACL 2026. Congratulations. 🎉
• 2026.03 - Our paper “A Two-Stage Imputation Method for Urban Traffic Data Based on Sparse Spatiotemporal Attention” has been accepted by IEEE Transactions on Intelligent Transportation Systems. Congrats to Jiyu. 🎉
• 2026.03 - Our paper “ArmPad: Transforming Forearms into Interaction Interfaces with Smartwatches” has been accepted by IEEE Transactions on Mobile Computing. Congrats to Zhenyu. 🎉
• 2026.03 - Our paper “Heterogeneous Multi-Modal Multi-Label Behavioral Context Recognition Using Wearable Sensors and Mobile Devices” has been accepted by IEEE Transactions on Computational Social Systems. Congratulations. 🎉

Selected Publications:

• Zhidan Liu, Yingqian Zhou, Xiaosi Liu, Haodi Zhang, Yabo Dong, Dongming Lu, Kaishun Wu. Learning Road Network Index Structure for Efficient Map Matching, IEEE Transactions on Knowledge and Data Engineering, Vol. 37, Issue 1, Pages 423-437, January 2025. (codes)

  • Enhancing HMM-Based map matching through learned Indexing and adaptive search range for precise candidate refinement

• Zhidan Liu, Jiancong Liu, Xiaowen Xu, Kaishun Wu. DeepGPS: Deep Learning Enhanced GPS Positioning in Urban Canyons, IEEE Transactions on Mobile Computing, Vol. 23, Issue 1, Pages 376-392, January 2024. (codes)

  • Exploiting deep learning to decode the relationship between positioning contexts and GPS estimations in urban canyons

• Zhidan Liu, Jiangzhou Li, Kaishun Wu. Context-Aware Taxi Dispatching at City-Scale Using Deep Reinforcement Learning, IEEE Transactions on Intelligent Transportation Systems, Vol. 23, Issue 3, Pages 1996-2009, March 2022. (codes)

  • An ESI highly cited paper, which innovates the application of deep reinforcement learning in large-scale vehicle dispatching

• Zhidan Liu, Zengyang Gong, Jiangzhou Li, Kaishun Wu. Mobility-Aware Dynamic Taxi Ridesharing, in IEEE ICDE, Dallas, Texas, USA, April 2020.

  • Exploiting mobility information to efficiently serve both online and offline riders in dynamic ridesharing

• Zhidan Liu, Pengfei Zhou, Zhenjiang Li, Mo Li. Think Like A Graph: Real-Time Traffic Estimation at City-Scale, IEEE Transactions on Mobile Computing, Vol. 18, Issue 10, Pages 2446-2459, October 2019.

  • Leveraging graph-parallel computing for precise, timely, and scalable traffic estimations through Apache Spark implementation