AI-Driven Forecasting: Harnessing Social Media for Event and Epidemic Prediction

This talk explores how social media time series can be leveraged for the early warning of societal events using the AI-driven forecasting system EMBERS. By modeling temporal patterns in open-source data, augmented by Dynamic Query Expansion (DQE), EMBERS generates real-time predictions of civil unrest with actionable lead times. I will outline its key components, including time-aware event modeling and retrospective evaluation using metrics such as lead time and precision. Case studies from Latin America illustrate how temporal modeling of social signals can yield proactive insights into complex societal dynamics. The second part of the talk introduces SimNest, a deep learning framework that integrates computational epidemiology with social media for real-time flu surveillance. I will also present a multi-task learning approach for spatiotemporal forecasting across regions, designed to address data sparsity and geographic heterogeneity. Together, these approaches demonstrate how AI can effectively model social media time series to enhance crisis anticipation and response.
Bio: Dr. Chang-Tien Lu is a Professor of Computer Science, Curriculum Lead at the Innovation Campus, and Associate Director of the Sanghani Center for AI and Data Analytics at Virginia Tech. He earned his Ph.D. from the University of Minnesota in 2001. An ACM Distinguished Scientist and IEEE Fellow, Dr. Lu’s research spans spatial informatics, urban computing, artificial intelligence, and intelligent transportation systems. He has authored over 250 publications in top-tier journals and conferences, with research supported by the NSF, NIH, DoD, and DoE. He serves as an Associate Editor for ACM Transactions on Spatial Algorithms and Systems, Data & Knowledge Engineering, IEEE Transactions on Big Data, and GeoInformatica. Dr. Lu has held leading roles in organizing major conferences, including serving as General Co-Chair of ACM SIGSPATIAL (2009, 2020, 2021), SSTD (2017), IEEE Big Data (2024), and IEEE ICDM (2025). He also served as Secretary (2008–2011) and Vice Chair (2011–2014) of ACM SIGSPATIAL, actively contributing to the advancement of the field and the broader computing community.

Li Zhang is an assistant professor in the Department of Computer Science at University of Texas Rio Grande Valley. Her research focuses on designing reliable time series data mining tools for various tasks including time series forecasting, pattern discovery, anomaly detection, classification. Her work have appeared on top-tier data mining conferences such as ICDM, SDM and CIKM, as well as interdisciplinary applications related to time series including one best student paper runner-up. She has hosted a tutorial on time series pattern mining in SDM24 and organized AI4TS time series workshop in IJCAI 2024. She has served as an associate editor for Big Data Research, and a PC member in major data mining and machine learning conferences such as KDD, SDM, ICDM, Neurips, AAAI, etc. Her research is sponsored by the National Science Foundation.

Dongjin Song is an assistant professor in the Department of Computer Science and Engineering at the University of Connecticut. He has strong expertise in machine learning, deep learning, and related applications for time series data analysis (including representation, prediction, anomaly detection, and diagnosis). Papers describing his research have been published at top-tier data science and artificial intelligence conferences, such as AAAI, IJCAI, NeurIPS, ICML, ICLR, KDD, ICCV, ICDM, SDM, etc. He has served as Senior PC for AAAI, IJCAI, and CIKM. He won the UConn Research Excellence Research (REP) Award in 2021.

Qingsong Wen is currently the Head of AI Research & Chief Scientist at Squirrel AI Learning by Yixue Education Inc., working on AI for Education via SOTA technologies (like LLM, AI Agent, GenAI, Transformer, SSL, GNN, etc.). Previously, he worked at Alibaba on AI for Time Series in Cloud Computing, E-Commerce, and Energy Industries, at Qualcomm and Marvell in big data and signal processing. He received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Georgia Institute of Technology, Atlanta, USA. He has published over 80 top-ranked conference and journal papers, received AAAI/IAAI 2023 Innovative Application Award, and won the First Place in 2022 ICASSP Grand Challenge Competition. He is an Associate Editor for Neurocomputing, Guest Editor for IEEE Internet of Things Journal, Guest Editor for Applied Energy, and regularly served as an Area Chiar/SPC/PC member of the major AI/DM/SP conferences including AAAI, IJCAI, KDD, ICDM, ICASSP, etc.

Shirui Pan is a Professor and an ARC Future Fellow with the School of Information and Communication Technology, Griffith University, Australia. Before joining Griffith in 2022, he is a Senior Member of IEEE and ACM, and a Fellow of Queensland Academy of Arts and Sciences (FQA). His research focuses on artificial intelligence and machine learning. He has made contributions to advance graph machine learning methods for solving hard AI problems for real-life applications, including graph classification, anomaly detection, recommender systems, and multivariate time series forecasting. His research has been published in top conferences and journals including NeurIPS, ICML, KDD, TPAMI, TNNLS, and TKDE. He is recognised as one of the AI 2000 AAAI/IJCAI Most Influential Scholars in Australia (2023, 2022), and one of the World’s Top 2% Scientists (2022, 2021). His research received the 2020 IEEE ICDM Best Student Paper Award (2020), and the 2024 IEEE CIS TNNLS Outstanding Paper Award. He has eight papers recognised as the Most Influential Papers in KDD, IJCAI, AAAI, and CIKM (x1). He received a prestigious Future Fellowship (2022-2025), one of the most competitive grants from the Australian Research Council (ARC).