About me

Hello there! I am Mutong LIU (刘牧潼), a Ph.D. candidate in the Department of Computer Science at Hong Kong Baptist University, supervised by Prof. Yang LIU and co-supervised by Prof. Jiming LIU. My primary research interests inlcudes artificial intelligence, machine learning, computational epidemiology, and complex system modeling, specifically focus on developing multi-agent reinforcement learning, physical/epidemiological-informed machine learning, and spatiotemporal analysis methods.

My work aims to solve complex real-world problems such as infectious disease transmission risk assessment and prediction, adaptive intervention strategy inference, and effective cooperative behavior learning in multi-agent systems. My research spans AI/ML methodology development and application deployment in the context of infectious disease dynamics:

• MLs for Assessing Infectious Disease Risk and Inferring Transmission Patterns:
  Malaria Transmission Intensity Assessment (IDM’23) · TransCode for COVID‑19 Transmission Patterns (IDP’23)

• MLs for Epidemic Dynamics Prediction:
  Survey on ML for Infectious Disease Risk Prediction (ACM CSUR’25) · EpiDL (CIKM’23)

• MLs & RLs for Infectious Disease Control:
  RLs in Infectious Disease Control (WI‑IAT’25) · Resource Allocation for Effective Disease Control (IDP’22)

• MARL for Learning Cooperative Behavior in Multi-agent Systems:
  Probing Diametric Coordination Graphs for Multi-Agent Reinforcement Learning [Under Review]

Email address: csmtliu@comp.hkbu.edu.hk (Academic) ·  gigg0@icloud.com (Personal)

Research Interests

• Machine Learning, Reinforcement Learning, Spatiotemporal Analytics, Epidemic Prediction, Infectious Disease Modeling and Control

Education and Academic Qualification

Publications (Google Scholar)

Published:

Mutong Liu, Yang Liu, and Jiming Liu (2025). Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control. 2025 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (Accepted). [paper]

Mutong Liu, Yang Liu, and Jiming Liu (2025). Machine Learning for Infectious Disease Risk Prediction: A Survey. ACM Computing Survey, 57(8), Article 212. [paper] [supplementary] [2024 Impact Factor: 28.0 (ranked 1/147 in Computer Science Theory & Methods)]

Mutong Liu, Yang Liu, Jiming Liu (2023). Epidemiology-aware Deep Learning for Infectious Disease Dynamics Prediction. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23). [paper] [poster] [code]

Mutong Liu, Yang Liu, Ly Po, Shang Xia, Rekol Huy, Xiao-Nong Zhou, and Jiming Liu (2023). Assessing the spatiotemporal malaria transmission intensity with heterogeneous risk factors: A modeling study in Cambodia. Infectious Disease Modelling, 8(1), 253-269. [paper]

Jinfu Ren*, Mutong Liu*, Yang Liu, and Jiming Liu (2022). Optimal resource allocation with spatiotemporal transmission discovery for effective disease control. Infectious Diseases of Poverty, 11(1), 1-11. [paper]

Jinfu Ren, Mutong Liu, Yang Liu, and Jiming Liu (2023). TransCode: Uncovering COVID-19 transmission patterns via deep learning. Infectious Diseases of Poverty, 12(1), 1-20. [paper] [Feature article]

Li Tao, Mutong Liu, Zili Zhang, and Liang Luo (2022). Identifying multiple influential spreaders in complex networks by considering the dispersion of nodes. Frontiers in Physics, 9, 766615. [paper]

Under Review:

Mutong Liu, Tiantian He, Yang Liu, Jiming Liu, and Yew-Soon Ong (2026). Probing Diametric Coordination Graphs for Multi-Agent Reinforcement Learning. Under Review.

* Co-first author (Contributed equally).