Jundong Li

Jundong Li is an Assistant Professor at the University of Virginia with primary appointment in the Department of Electrical and Computer Engineering and secondary appointments in Computer Science and the School of Data Science. He is affiliated with the School of Engineering and Applied Science and conducts research at the intersection of machine learning, data mining, and artificial intelligence.

Education:

• Ph.D. in Computer Science, Arizona State University, 2019
• M.Sc. in Computer Science, University of Alberta, 2014
• B.Eng. in Software Engineering, Zhejiang University, 2012

His research focuses on graph machine learning, trustworthy and fair AI, and large language models. He investigates how to make deep learning models more interpretable, robust, and equitable, especially in graph-structured data and NLP applications. His work combines causal inference, feature selection, and model explanation techniques to build reliable AI systems.

His recent publications (2024–2022) reveal a strong trend toward large language models, with topics including in-context learning, knowledge editing, and collaborative reasoning. Simultaneously, he continues pioneering research on fairness and interpretability in graph neural networks, addressing structural bias, adversarial attacks, and node attribution. His work is highly interdisciplinary, spanning computer science, data science, and social impact.

Scientific Awards:

• SIGKDD Rising Star Award (2024)
• PAKDD Best Paper Award (2024)
• NSF CAREER Award (2022)
• SIGKDD Best Research Paper Award (2022)
• JP Morgan Faculty Research Award (2021, 2022)
• Cisco Faculty Research Award (2021)
• Stanford/Elsevier Top 2% Scientist (2024)

Jundong Li actively advises graduate students, as seen in his co-authored papers with researchers like Song Wang, Yushun Dong, and Binchi Zhang. His research is generously funded by the National Science Foundation (NSF) through multiple programs including CAREER, III, SaTC, SAI, and S&CC, as well as by the Department of Energy (DOE), Office of Naval Research (ONR), Jefferson Lab, and industry partners including JP Morgan, Cisco, Netflix, and Snap.

He leads a dynamic research group focused on advancing the frontiers of graph learning and trustworthy AI, with projects on causal inference, model unlearning, and explainable systems. His lab contributes to both theoretical foundations and real-world applications in public health, transportation, and network security.