Lijing Wang, Amy R Zipursky, Alon Geva, Andrew J McMurry, Kenneth D Mandl, and Timothy A Miller. A computable case definition for patients with SARS-CoV2 testing that occurred outside the hospital .Journal of the American Medical Informatics Association Open Access journal (JAMIA Open), Volume 6, Issue 3, October 2023, ooad047, https://doi.org/10.1093/jamiaopen/ooad047 [ pdf | bibtex | code]
Lijing Wang*, Yingya Li*, Timothy A. Miller, Steven Bethard, and Guergana Savova. Two-Stage Fine-Tuning for Improved Bias and Variance for Large Pretrained Language Models .The 61st Annual Meeting of the Association for Computational Linguistics (ACL'23), Toronto, Canada, July 9-14th, 2023. To appear. [ pdf | bibtex | code]
Katharine Sherratt, et al. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations .eLife 12:e81916. April 21, 2023. [ pdf | bibtex | code]
(alphabet order) Aniruddha Adiga, Bryan Lewis, Simon Levin, Madhav V. Marathe, H. Vincent Poor, S. S. Ravi, Daniel J. Rosenkrantz, Richard E. Stearns, Srinivasan Venkatramanan, Anil Vullikanti, Lijing Wang. AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice. Lidströmer, N., Eldar, Y.C. (eds) Artificial Intelligence in Covid-19. Springer, Cham. (Book Chapter), 2022. [ pdf | bibtex ]
Aniruddha Adiga, Gursharn Kaur, Benjamin Hurt, Lijing Wang, Przemyslaw Porebski, Srinivasan Venkatramanan, Bryan Lewis, and Madhav Marathe. Enhancing COVID-19 Ensemble Forecasting Model Performance Using Auxiliary Data Sources. IEEE BigData 2022 , Osaka, Japan. Dec 17-20, 2022. Best paper award. [ pdf | bibtex ]
Estee Y. Cramer, Yuxin Huang, and et al & US COVID-19 Forecast Hub Consortium. The United States COVID-19 Forecast Hub dataset. Scientific Data 9, 462(2022). August 01, 2022. [ pdf | bibtex ]
Lijing Wang, Timothy Miller, Steven Bethard, Guergana Savova. Ensemble-based Fine-Tuning Strategy for Temporal Relation Extraction from the Clinical Narrative. The 4th Clinical Natural Language Processing Workshop (Clinical NLP at NAACL 2022), Seattle, WA. Jul 14, 2022 [ pdf | bibtex ]
Shaun Truelove, and et al. Projected resurgence of COVID-19 in the United States in July—December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination. Elife , 11 (2022): e73584. [ pdf | bibtex]
Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Adam Sadilek, Srinivasan Venkatramanan, Madhav Marathe. CausalGNN: Causal-based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting. The 36th AAAI Conference on Artificial Intelligence (AAAI'22), Vancouver, BC, Canada. Feb 22 - Mar 1, 2022. Oral presentation. Acceptance rate: 15%. [ pdf | bibtex | apx ]
Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Bryan Lewis, Adam Sadilek, Srinivasan Venkatramanan, Madhav Marathe. Combining Theory and Data Driven Approaches for Epidemic Forecasts. Data Mining and Knowledge Discovery Series of CRC Press (Book Chapter), 2021. [ pdf | bibtex ]
Aniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Peddireddy, Przemyslaw Porebski, Srinivasan Venkatramanan, Bryan Lewis, Madhav Marathe. All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), online, Aug 14-18, 2021. [ pdf | bibtex ]
Borchering RK, Viboud C, Howerton E, et al. Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios — United States, April–September 2021. MMWR and Morbidity and Mortality Weekly Report 2021 ;70:719–724. [ DOI ]
Alok Talekar, Nidhin Vaidhiyan, Sharad Shriram, Gaurav Aggarwal, Jiangzhuo Chen, Srini Venkatramanan, Lijing Wang, Aniruddha Adiga, Adam Sadilek, Ashish Tendulkar, Madhav Marathe, Rajesh Sundaresan and Milind Tambe. Cohorting to Isolate Asymptomatic Spreaders: An Agent-based Simulation Study on the Mumbai Suburban Railway. The 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS'21), online, May 03-07, 2021. Main track: extended abstracts. Acceptance rate: 24.8%. Oral presentation. [ pdf | bibtex ]
Srinivasan Venkatramanan, Adam Sadilek, Arindam Fadikar, Christopher L. Barrett, Matthew Biggerstaff, Jiangzhuo Chen, Xerxes Dotiwalla, Paul Eastham, Bryant Gipson, Dave Higdon, Onur Kucuktunc, Allison Lieber, Bryan L Lewis, Zane Reynolds, Anil K Vullikanti, Lijing Wang, Madhav Marathe. Forecasting Influenza Activity Using Machine-Learned Mobility Map. Nature Communications (NatureComms), 2021 Feb 09;12(1):1-12. Impact factor: 12.121. [ pdf | bibtex ]
Lijing Wang*, Xue Ben*, Aniruddha Adiga*, Adam Sadilek, Ashish Tendulkar, Srinivasan Venkatramanan, Anil Vullikanti, Gaurav Aggarwal, Alok Talekar, Jiangzhuo Chen, Bryan Lewis, Samarth Swarup, Amol Kapoor, Milind Tambe, Madhav Marathe. Using Mobility Data to Understand and Forecast COVID19 Dynamics. The 29th International Joint Conference on Artificial Intelligence Workshop on AI for Social Good (IJCAI AI4SG'21), online, Jan 07-15, 2021. Long talk. Acceptance rate: 28%. [ pdf | bibtex ]
Lijing Wang, Aniruddha Adiga, Srinivasan Venkatramanan, Jiangzhuo Chen, Bryan Lewis, Madhav Marathe. Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting. IEEE BigData 2020 Workshop on Data Science in Medicine and Healthcare (IEEE BigData DSMH'20), online, Dec 10-13, 2020. [ pdf | bibtex ]
Lijing Wang, Dipanjan Ghosh, Maria Gonzalez Diaz, Ahmed Farahat, Mahbubul Alam, Chetan Gupta, Jiangzhuo Chen, Madhav Marathe. Wisdom of the Ensemble: Improving Consistency of Deep Learning Models. Advances in Neural Information Processing Systems 33 (NeurIPS'20), online, Dec 06-12, 2020. Acceptance rate: 20.1%. [ pdf | bibtex | code | poster]
Aniruddha Adiga*, Lijing Wang*, Adam Sadilek*, Ashish Tendulkar*, Srinivasan Venkatramanan, Anil Vullikanti, Gaurav Aggarwal, Alok Talekar, Xue Ben, Jiangzhuo Chen, Bryan Lewis, Samarth Swarup, Milind Tambe, Madhav Marathe. Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics. medRxiv (DOI). [ pdf | bibtex]
Songgaojun Deng, Shusen Wang, Huzefa Rangwala, Lijing Wang, Yue Ning. Cola-GNN: Cross-location Attention based Graph Neural Networks for Long-term ILI Prediction. The 20th ACM International Conference on Information and Knowledge Management (CIKM'20), online, Oct 19-23, 2020. Research track. Acceptance rate: 20%. [ pdf | bibtex | code ]
Lijing Wang, Jiangzhuo Chen, Madhav Marathe. TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information. ACM Transactions on Spatial Algorithms and Systems (TSAS'20), Deep Learning for Spatial Algorithms and Systems, 2020 May;6(3):1-39. Impact factor: 1.69. [ pdf | bibtex | poster ]
Lijing Wang, Jiangzhuo Chen, Madhav Marathe. DEFSI: Deep Epidemic Forecasting with Synthetic Information. The 32nd Innovative Applications of Artificial Intelligence (IAAI'19), Hawaii, USA, Jan 27-Feb 01, 2019. Acceptance rate: 35%. [ pdf | bibtex ]
Lijing Wang, Jiangzhuo Chen, Achla Marathe. A framework for discovering health disparities among cohorts in an influenza epidemic. World Wide Web Journal Special Issue on Social computing and big data applications (WWWJ'19), 2019 Nov;22(6):2997-3020. Impact factor: 2.892. [ pdf | bibtex | poster]
Ting Hua, Chandan K Reddy, Lei Zhang, Lijing Wang, Liang Zhao, Chang-Tien Lu, Naren Ramakrishnan.
Social Media based Simulation Models for Understanding Disease Dynamics.
The 27th International Joint Conference on Artificial Intelligence
(IJCAI'18), Stockholm, Sweden, Jul 13-19, 2018. Acceptance rate: 20.5%. [ pdf | bibtex ]
Lijing Wang, Jin-Hee Cho, Ing-Ray Chen, Jiangzhuo Chen. PDGM: Percolation-based directed graph matching in social networks. 2017 IEEE International Conference on Communications (IEEE ICC'17), Paris, France, May 21-25, 2017. Acceptance rate: 36.1%. [ pdf | bibtex ]
Lijing Wang, Jiangzhuo Chen, Achla Marathe. Understanding Health Disparities in an Influenza Epidemic. The Computational Social Science Society of the Americas (CSSSA'16), New Mexico, Nov 17-20, 2016. [ pdf | bibtex ]
Lijing Wang, Xinbo Song, Yanlong Tan, Shuai Niu, Yao Cui. The Update Strategies of Global Statistics in Distributed Information Retrieval Systems. The 18th China Conference on Information Retrieval (CCIR'12), Jiangxi, China, Nov 28-30, 2012. [ pdf ]
Dipanjan Ghosh, Maria Teresa Gonzalez Diaz, Mahbubul Alam, Ahmed Farahat, Chetan Gupta, and Lijing Wang. Method for Reproducibility of Deep Learning Classifiers Using Ensembles. U.S. Patent Application 16/886,344, filed December 2, 2021.