Yingshu Li
Professor Computer Science- Education
B.S., Computer Science, Beijing Institute of Technology, 2001
M.S., Computer Science, University of Minnesota, 2003
Ph.D., Computer Science, University of Minnesota, 2005
- Specializations
Artificial Intelligence of Things (AIoT), privacy-aware computing, Internet of Things (IoT), social networks, wireless networking
- Biography
Dr. Li is currently a Professor in the Department of Computer Science and an affiliated faculty member in the INSPIRE Center at Georgia State University. Dr. Li received her Ph.D. and M.S. degrees from the Department of Computer Science and Engineering at University of Minnesota-Twin Cities. Her research interests include Artificial Intelligence of Things (AIoT), Privacy-aware Computing, Internet of Things (IoT), Social Networks, and Wireless Networking. Dr. Li is the recipient of the NSF CAREER Award and the 2021 Outstanding Graduate Director Award. Dr. Li was nominated to the 2022 N²Women: Stars in Computer Networking and Communications. She was on the list of The World’s Top 2% Scientists (2020–2024) published by Stanford University. Dr. Li’s research has been funded by the National Science Foundation, the U.S. Department of State, and a range of esteemed international academic and industrial sponsors. Dr. Li regularly publishes papers in prestigious journals, conference proceedings, and books; her publications have received more than 10,000 citations. Dr. Li has been an editor for such top-tier journals as ACM Transactions on Sensor Networks, IEEE Transactions on Computers, IEEE Transactions on Network Science and Engineering, and IEEE Internet of Things Journal. She has also served as a Steering Committee Chair, General Chair, Program Chair, Steering Committee member, and TPC member for many well-known international conferences. Dr. Li has supervised more than 20 Ph.D. students, many of whom have successfully transitioned to faculty positions in academia. Dr. Li plays a vital role in Eureka Labs, an innovative platform that delivers practical, interactive lab exercises to teach essential cybersecurity principles. This resource offers accessible, top-tier educational content designed for students and educators alike.
- Publications
[1] Distributed Generative Model: A Data Synthesizing Framework for Multi-Source Heterogeneous Data, Z. Xiong, W. Li, Y. Li, and Z. Cai, Accepted by IEEE Transactions on Artificial Intelligence, 2025.
[2] Human Activity Recognition in Mobile Edge Computing: A Low-Cost and High-Fidelity Digital Twin Approach with Deep Reinforcement Learning, C. Wang, Z. Cai, and Y. Li, Accepted by IEEE Transactions on Consumer Electronics, 2024. DOI: 10.1109/TCE.2024.3375859
[3] TMETA: Trust Management for the Cold Start of IoT Services with Digital-Twin-Aided Blockchain, C. Wang, Z. Cai, D. Seo, and Y. Li, IEEE Internet of Things Journal, 10(24):21337–21348, 2023. DOI: 10.1109/JIOT.2023.3285108
[4] Quantum Cognition-Inspired EEG-based Recommendation via Graph Neural Networks, J. Han, W. Li, Y. Li, and Z. Cai, CIKM, 2024. DOI: 10.1145/3627673.3679564
[5] Exact-Fun: An Exact and Efficient Federated Unlearning Approach, Z. Xiong, W. Li, Y. Li, and Z. Cai, ICDM, 2023. DOI: 10.1109/ICDM58522.2023.00188