Conference Papers
(^ indicates equal contributions)
[In Submission] Juntong Ni, Shiyu Wang, Zewen Liu, Xiaoming Shi, Xinyue Zhong, Zhou Ye, Wei Jin
Are We Overlooking the Dimensions? Learning Latent Hierarchical Channel Structure for High-Dimensional Time Series Forecasting [pdf][In Submission] Zewen Liu, Juntong Ni, Bohan Wang, Max S. Y. Lau, Wei Jin
Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes [pdf][ACL’2026] Zewen Liu, Juntong Ni, Xianfeng Tang, Max S.Y. Lau, Qi He, Wenpeng Yin, Wei Jin,
SymbolBench: Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series? [pdf][ICML’2026] Bohan Wang, Zewen Liu, Lu Lin, Hui Liu, Li Xiong, Ming Jin, Wei Jin
Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks [pdf][KDD’2026] Juntong Ni, Zewen Liu, Shiyu Wang, Ming Jin, Wei Jin
TimeDistill: Efficient Long-term Time Series Forecasting with MLPs via Cross-Architecture Distillation [pdf][ICML’2026] Guancheng Wan, Zewen Liu, Max S. Y. Lau, B. Aditya Prakash, Wei Jin
Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph [pdf][PSB’2026] Songyuan Liu, Shengbo Gong, Tianning Feng, Zewen Liu, Max SY Lau, Wei Jin
Higher-order Interaction Matters: Dynamic Hypergraph Neural Networks for Epidemic Modeling [pdf][KDD’2025] Zewen Liu, Xiaoda Wang, Bohan Wang, Zijie Huang, Carl Yang, Wei Jin
Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks [pdf][WWW’2025] Yunxiao Li, Mingyang Wei, Zewen Liu, Max SY Lau, Wei Jin
Efficient Epidemic Intervention Generation: A Graph Adversarial Attack Perspective [pdf][KDD’2024 epiDAMIK] Zewen Liu, Yunxiao Li, Mingyang Wei, Guancheng Wan, Max S.Y. Lau, Wei Jin
EpiLearn: A Python Library for Machine Learning in Epidemic Modeling [pdf] [repo][KDD’2024] Zewen Liu^, Guancheng Wan^, B. Aditya Prakash, Max S. Y. Lau, Wei Jin
A Review of Graph Neural Networks in Epidemic Modeling [pdf] [reading list][07/2024] Kun Guo^, Zewen Liu^, Zhiwei Chen, Haoteng Wen, Wei Jin, Jiliang Tang, Yi Chang
Learning on Graphs with Large Language Models (LLMs): A Deep Dive into Model Robustness [pdf]
Journal Papers
(^ indicates equal contributions)
[In Submission] Zewen Liu, Max SY Lau, Wei Jin
EpiLearn: From Cross-Paradigm Comparison to Regime-Aware Deployment of Epidemic Forecasting Models[In Submission] Zewen Liu, Bryan T. Grenfell, C. Jessica E. Metcalf, Wei Jin, Max SY Lau
Vaccination restructures the spatial geometry of measles transmission[In Submission] Saurabh Kataria, Yi Wu, Zhaoliang Chen, Hyunjung Gloria Kwak, Yuhao Xu, Lovely Yeswanth Panchumarthi, Ran Xiao, Jiaying Lu, Ayca Ermis, Anni Zhao, Runze Yan, Alex Federov, Zewen Liu, Xu Wu, Wei Jin, Carl Yang, Jocelyn Grunwell, Stephanie R. Brown, Amit Shah, Craig Jabaley, Tim Buchman, Sivasubramanium V Bhavani, Randall J. Lee, Xiao Hu
Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals [pdf][PNAS’2026] Max SY Lau, C Jessica E Metcalf, Zewen Liu, Bryan T Grenfell, Wei Jin
Toward AI foundation models for epidemics: Promise, challenges, and paths forward [pdf][Analytical Biochemistry’2024] Fei Li, Jiale Zhang, Kewei Li, Yu Peng, Haotian Zhang, Yiping Xu, Yue Yu, Yuteng Zhang, Zewen Liu, Ying Wang, Lan Huang, Fengfeng Zhou
GANSamples-ac4C: Enhancing ac4C site prediction via generative adversarial networks and transfer learning [pdf]