PDGM: Percolation-based directed graph matching in social networks. - Lijing Wang 001; Jin-Hee Cho; Ing-Ray Chen; Jiangzhuo Chen (ICC, 2017)

Social Media based Simulation Models for Understanding Disease Dynamics. - Ting Hua; Chandan K. Reddy; Lei Zhang 158; Lijing Wang 001; Liang Zhao 002; Chang-Tien Lu; Naren Ramakrishnan (IJCAI, 2018)

DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information. - Lijing Wang 001; Jiangzhuo Chen; Madhav V. Marathe (AAAI, 2019)

Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction. - Songgaojun Deng; Shusen Wang; Huzefa Rangwala; Lijing Wang 001; Yue Ning 001 (CoRR, 2019)

A framework for discovering health disparities among cohorts in an influenza epidemic. - Lijing Wang 001; Jiangzhuo Chen; Achla Marathe (World Wide Web, 2019)

Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting. - Lijing Wang 001; Aniruddha Adiga; Srinivasan Venkatramanan; Jiangzhuo Chen; Bryan L. Lewis; Madhav V. Marathe (IEEE BigData, 2020)

Cola-GNN: Cross-location Attention based Graph Neural Networks for Long-term ILI Prediction. - Songgaojun Deng; Shusen Wang; Huzefa Rangwala; Lijing Wang 001; Yue Ning 001 (CIKM, 2020)

Wisdom of the Ensemble: Improving Consistency of Deep Learning Models. - Lijing Wang 001; Dipanjan Ghosh; Maria Teresa Gonzalez Diaz; Ahmed K. Farahat; Mahbubul Alam; Chetan Gupta 001; Jiangzhuo Chen; Madhav V. Marathe (NeurIPS, 2020)

TDEFSI: Theory-guided Deep Learning-based Epidemic Forecasting with Synthetic Information. - Lijing Wang 001; Jiangzhuo Chen; Madhav V. Marathe (ACM Trans. Spatial Algorithms Syst., 2020)

Cohorting to Isolate Asymptomatic Spreaders: An Agent-Based Simulation Study on the Mumbai Suburban Railway. - Alok Talekar; Sharad Shriram; Nidhin K. Vaidhiyan; Gaurav Aggarwal; Jiangzhuo Chen; Srinivasan Venkatramanan; Lijing Wang 001; Aniruddha Adiga; Adam Sadilek; Ashish Tendulkar; Madhav V. Marathe; Rajesh Sundaresan; et al. (AAMAS, 2021)

All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. - Aniruddha Adiga; Lijing Wang 001; Benjamin Hurt; Akhil Sai Peddireddy; Przemyslaw J. Porebski; Srinivasan Venkatramanan; Bryan Leroy Lewis; Madhav V. Marathe (KDD, 2021)

CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting. - Lijing Wang 001; Aniruddha Adiga; Jiangzhuo Chen; Adam Sadilek; Srinivasan Venkatramanan; Madhav V. Marathe (AAAI, 2022)

Enhancing COVID-19 Ensemble Forecasting Model Performance Using Auxiliary Data Sources. - Aniruddha Adiga; Gursharn Kaur; Benjamin Hurt; Lijing Wang 001; Przemyslaw J. Porebski; Srinivasan Venkatramanan; Bryan L. Lewis; Madhav V. Marathe (IEEE Big Data, 2022)

Phase-Informed Bayesian Ensemble Models Improve Performance of COVID-19 Forecasts. - Aniruddha Adiga; Gursharn Kaur; Lijing Wang 001; Benjamin Hurt; Przemyslaw J. Porebski; Srinivasan Venkatramanan; Bryan L. Lewis; Madhav V. Marathe (AAAI, 2023)

Two-Stage Fine-Tuning for Improved Bias and Variance for Large Pretrained Language Models. - Lijing Wang 001; Yingya Li; Timothy Miller 002; Steven Bethard; Guergana Savova (ACL (1), 2023)

Challenges of COVID-19 Case Forecasting in the US, 2020-2021. - Velma K. Lopez; Estee Y. Cramer; Robert Pagano; John M. Drake; Eamon B. O'Dea; Madeline Adee; Turgay Ayer; Jagpreet Chhatwal; Ozden O. Dalgic; Mary A. Ladd; Benjamin P. Linas; Peter P. Mueller; et al. (PLoS Comput. Biol., 2024)

KEGNN: Knowledge-Enhanced Graph Neural Networks for User Engagement Prediction. - Ching-Hao Fan; Hao Zhou; Yao Sun; Geovanny Palomino Roldan; Olga Kokshagina; Marc Santolini; Lijing Wang 001 (ICMR, 2025)

Assessing the Macro and Micro Effects of Random Seeds on Fine-Tuning Large Language Models. - Hao Zhou; Guergana Savova; Lijing Wang 001 (CoRR, 2025)