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, organized into the four topics below. Each topic lists representative publications, and the complete list is maintained on the About Me page.

MARL for Learning Cooperative Behavior in Multi-agent Systems

Developing multi-agent reinforcement learning methods that learn effective cooperative behavior under complex and dynamic interactions.

Probing Diametric Coordination Graphs for Multi-Agent Reinforcement Learning

2026CCF-A

Probing Diametric Coordination Graphs for Multi-Agent Reinforcement Learning

Artificial Intelligence, volume 359, 104603 [paper]

Mutong Liu, Tiantian He, Yang Liu, Jiming Liu, and Yew-Soon Ong.

ML for Assessing Infectious Disease Risk and Inferring Transmission Patterns

Using machine learning to assess transmission intensity, uncover hidden transmission patterns, and identify heterogeneous risk factors from spatiotemporal surveillance data.

Assessing the spatiotemporal malaria transmission intensity with heterogeneous risk factors: A modeling study in Cambodia

2023

Assessing the spatiotemporal malaria transmission intensity with heterogeneous risk factors: A modeling study in Cambodia

Infectious Disease Modelling, 8(1), 253-269 [paper]

Mutong Liu, Yang Liu, Ly Po, Shang Xia, Rekol Huy, Xiao-Nong Zhou, and Jiming Liu.

TransCode: Uncovering COVID-19 transmission patterns via deep learning

2023Feature article

TransCode: Uncovering COVID-19 transmission patterns via deep learning

Infectious Diseases of Poverty, 12(1), 1-20 [paper]

Jinfu Ren, Mutong Liu, Yang Liu, and Jiming Liu.

ML for Epidemic Dynamics Prediction

Forecasting epidemic dynamics with epidemiological priors and deep spatiotemporal models, including survey-level syntheses of the field.

Machine Learning for Infectious Disease Risk Prediction: A Survey

20252025 Impact Factor: 30.4 (ranked 1/146 in Computer Science Theory & Methods)

Machine Learning for Infectious Disease Risk Prediction: A Survey

ACM Computing Survey, 57(8), Article 212 [paper] [supplementary]

Mutong Liu, Yang Liu, and Jiming Liu.

Epidemiology-aware Deep Learning for Infectious Disease Dynamics Prediction

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, Jiming Liu.

ML & RL for Infectious Disease Control

Inferring adaptive intervention strategies and allocating limited resources for effective disease control.

Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control

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.

Optimal resource allocation with spatiotemporal transmission discovery for effective disease control

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.

A complete and up-to-date publication list is available on the About Me page and on Google Scholar.