Professor of Computer Science
ACM Distinguished Scientist, IEEE Fellow
Faculty Fellow, College of Engineering
Associate Director, Sanghani Center for AI and DA
Curriculum Lead, Innovation Campus
Prof. Chang-Tien Lu
Can social media data serve as an early warning system for societal disruptions? This talk demonstrates how AI-driven models transform noisy, real-time social media signals into actionable forecasts of civil unrest and disease outbreaks. First, I present EMBERS, an automated forecasting system that detects emerging civil unrest by modeling spatiotemporal patterns in open-source data. By combining Dynamic Query Expansion (DQE) with time-aware event modeling, EMBERS generates predictions with actionable lead times—validated through retrospective evaluations across Latin America. Case studies reveal how these techniques extract proactive insights from complex social dynamics. The second part introduces SimNest, a deep learning framework that bridges computational epidemiology with social media for real-time flu surveillance. To address challenges of data sparsity and regional variation, I present a multi-task learning approach for spatiotemporal forecasting that enables robust predictions across heterogeneous geographies. Together, these systems illustrate how AI can turn unstructured social media data into a strategic asset, shifting crisis response from reactive to anticipatory.
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 funding from the NSF, NIH, DoD, and DoE. Dr. Lu serves as an Associate Editor for ACM Transactions on Spatial Algorithms and Systems, Data & Knowledge Engineering, IEEE Transactions on Big Data, and GeoInformatica. He has also held leadership roles in organizing major conferences, including General Co-Chair for ACM SIGSPATIAL (2009, 2020, 2021), SSTD (2017), IEEE Big Data (2024), and IEEE ICDM (2025). Previously, he served as Secretary (2008–2011) and Vice Chair (2011–2014) of ACM SIGSPATIAL, contributing significantly to the advancement of the field and the broader computing community.
Department of Electrical and Computer Engineering
Co-Director, Cross Pacific AI Initiative of UW
University of Washington, Seattle, WA, USA
Prof. Jenq-Neng Hwang
Human motion analysis via human pose estimation in both 2D and 3D remains
a fundamental yet challenging problem in computer vision. On the other hand,
it has broad applications in action recognition, human-computer interaction, motion analysis,
and object tracking. Despite recent advances, achieving robustness and efficiency in real-world and edge-device scenarios remains difficult.
This talk will first review perceptual AI techniques for human motion understanding, based on 2D/3D human pose estimation,
and generative AI techniques for human motion generation based on a novel diffusion-based framework for joint motion and text generation via mutual prompting.
Several practical applications of these techniques will also be discussed.
Dr. Jenq-Neng Hwang received the BS and MS degrees,
both in electrical engineering from the National Taiwan University,
Taipei, Taiwan, in 1981 and 1983 separately. He then received his Ph.D.
degree from the University of Southern California. In the summer of 1989, Dr. Hwang joined the
Department of Electrical and Computer Engineering (ECE) of the University of Washington in Seattle,
where he has been promoted to Full Professor since 1999. He served as the Associate Chair for Research from 2003 to 2005,
and from 2011-2015. He also served as the Associate Chair for Global Affairs from 2015-2020.
He is currently the International Programs Lead in the ECE Department. Currently, he serves as the Co-Director of Cross-Pacific
AI Initiative (X-PAI) in the College of Engineering (CoE), UW. He is the Founder and Director of the Information Processing Lab.,
which has won several AI City Challenges awards in the past years. He has written more than 450 journal, conference papers and book chapters in the areas of
machine learning, multimedia signal processing, computer vision, and multimedia system integration and networking (my Google citation),
including an authored textbook on "Multimedia Networking: from Theory to Practice," published by Cambridge University Press. Dr. Hwang has close working relationship
with the industry on artificial intelligence and machine learning.
Dr. Hwang received the 1995 IEEE Signal Processing Society's Best Journal Paper Award.
He is a founding member of Multimedia Signal Processing Technical Committee of IEEE Signal
Processing Society and was the Society's representative to IEEE Neural Network Council from
1996 to 2000. He is currently a member of Multimedia Technical Committee (MMTC) of IEEE Communication
Society and also a member of Multimedia Signal Processing Technical Committee (MMSP TC) of IEEE Signal Processing Society.
He served as associate editors for IEEE T-SP, T-NN and T-CSVT, T-IP and Signal Processing Magazine (SPM).
He served as the General Co-Chair of 2021 and 2022 IEEE World AI IoT Congress,
Seattle, WA. He also served as the Program Co-Chair of IEEE ICME 2016 and was the Program Co-Chairs of ICASSP 1998 and ISCAS 2009.
Dr. Hwang is a fellow of IEEE since 2001.