Contact
Location
E226, Thornton Hall

Jundong Li

Assistant Professor, Electrical and Computer Engineering (Primary) Assistant Professor, Computer Science (Secdonary) Assistant Professor, Data Science (Secdonary)
Contact
Location
E226, Thornton Hall
Google Scholar

About

Jundong Li is an Assistant Professor at the University of Virginia with appointments in the Department of Electrical and Computer Engineering, Department of Computer Science, and School of Data Science. Prior to joining UVA, he received his Ph.D. degree in Computer Science at Arizona State University in 2019 under the supervision of Dr. Huan Liu, his M.Sc. degree in Computer Science at the University of Alberta in 2014, and his B.Eng. degree in Software Engineering at Zhejiang University in 2012. His research interests are generally in data mining and machine learning, with a particular focus on graph machine learning, trustworthy/safe machine learning, and, more recently, large language models. He has published over 150 papers in high-impact venues (including KDD, WWW, WSDM, NeurIPS, ICML, ICLR, IJCAI, AAAI, ACL, EMNLP, NAACL, SIGIR, CIKM, ICDM, SDM, ECML-PKDD, CSUR, TPAMI, TKDE, TKDD, TIST, etc), with over 14,000 citation count. He has won several prestigious awards, including SIGKDD Rising Star Award (2024), PAKDD Best Paper Award (2024), PAKDD Early Career Research Award (2023), NSF CAREER Award (2022), SIGKDD Best Research Paper Award (2022), JP Morgan Chase Faculty Research Award (2021 & 2022), and Cisco Faculty Research Award (2021), among others. His group's research is generously supported by NSF (CAREER, III, SaTC, SAI, S&CC), DOE, ONR, Commonwealth Cyber Initiative, Jefferson Lab, JP Morgan, Cisco, Netflix, and Snap.

Education

Ph.D. Arizona State University, 2019

M.Sc. University of Alberta, 2014

B.Eng. Zhejiang University, 2012

Research Interests

Artificial Intelligence
Machine Learning
Data Mining
Natural Lanaguage Processing

Selected Publications

"Mixture of Demonstrations for In-Context Learning", Neural Information Processing Systems (NeurIPS), 2024. Song Wang, Zihan Chen, Chengshuai Shi, Cong Shen, Jundong Li
"Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective on Molecule Graphs", Conference on Empirical Methods in Natural Language Processing (EMNLP Findings), 2024. Yinhan He, Zaiyi Zheng, Patrick Soga, Yaochen Zhu, Yushun Dong, Jundong Li
"Verification of Machine Unlearning is Fragile", International Conference on Machine Learning (ICML), 2024. Binchi Zhang, Zihan Chen, Cong Shen, Jundong Li
"Towards Certified Unlearning for Deep Neural Networks", International Conference on Machine Learning (ICML), 2024. Binchi Zhang, Yushun Dong, Tianhao Wang, Jundong Li
"Collaborative Large Language Model for Recommender Systems", The Web Conference (formerly WWW), 2024. Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
"Adversarial Attacks on Fairness of Graph Neural Networks", International Conference on Learning Representations (ICLR), 2024. Binchi Zhang, Yushun Dong, Chen Chen, Yada Zhu, Minnan Luo, Jundong Li
"Interpreting Pretrained Language Models via Concept Bottlenecks", Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2024.
(Best Paper Award) Zhen Tan, Lu Cheng, Song Wang, Bo Yuan, Jundong Li, Huan Liu
"Knowledge Editing for Large Language Models: A Survey", ACM Computing Surveys (CSUR), 2024. Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, Jundong Li
"Federated Few-shot Learning", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023. Song Wang, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, Jundong Li
"Interpreting Unfairness in Graph Neural Networks via Training Node Attribution", AAAI Conference on Artificial Intelligence (AAAI), 2023. Yushun Dong, Song Wang, Jing Ma, Ninghao Liu, Jundong Li
"Fairness in Graph Mining: A Survey", IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023. Yushun Dong, Jing Ma, Song Wang, Chen Chen, Jundong Li
"Task-Adaptive Few-shot Node Classification", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022. Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, Jundong LI.
"Learning Causal Effects on Hypergraphs", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022. (Best Research Paper Award) Jing Ma, Mengting Wan, Longqi Yang, Jundong LI, Brent Hecht, Jaime Teevan.
"On Structural Explanation of Bias in Graph Neural Networks", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022. Yushun Dong, Song Wang, YU Wang, Tyler Derr, Jundong LI.
"EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks", The Web Conference (formerly WWW), 2022. Yushun Dong, Ninghao LIU, Brian Jalaian, Jundong LI.
"Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US", The Web Conference (formerly WWW), 2022. Jing Ma, Yushun Dong, Zheng Huang, Daniel Mietchen, Jundong LI.
"AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter", ACM International Conference on Information and Knowledge Management (CIKM), 2021. Yushun Dong, Kaize Ding, Brian Jalaian, Shuiwang Ji, Jundong LI.
"Individual Fairness for Graph Neural Networks: A Ranking based Approach", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021. Yushun Dong, Jian Kang, Hanghang Tong, Jundong LI.
"Learning Individual Causal Effects from Networked Observational Data", ACM International Conference on Web Search and Data Mining (WSDM), 2020. Ruocheng Guo, Jundong LI, Huan Liu.
"A Survey of Learning Causality with Data: Problems and Methods", ACM Computing Surveys (CSUR), 2020. Ruocheng Guo, Lu Cheng, Jundong LI, P. Richard Hahn, Huan Liu.
"Adaptive Unsupervised Feature Selection on Attributed Networks", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019. Jundong LI, Ruocheng Guo, Chenghao Liu, Huan Liu.
"Unsupervised Personalized Feature Selection", AAAI Conference on Artificial Intelligence (AAAI), 2018. Jundong LI, Liang Wu, Harsh Dani, Huan Liu.
"Attributed Network Embedding for Learning in a Dynamic Environment", ACM International Conference on Information and Knowledge Management (CIKM), 2017. Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu.
"Label Informed Attributed Network Embedding", ACM International Conference on Web Search and Data Mining (WSDM), 2017. Xiao Huang, Jundong Li, Xia Hu
"Feature Selection: A Data Perspective", ACM Computing Surveys (CSUR), 2017. Feature Selection: A Data Perspective

Awards

SIGKDD Rising Star Award 2024
PAKDD Best Paper Award 2024
Stanford/Elsevier Top 2% Scientist List 2024
PAKDD Early Career Research Award 2023
SIGKDD Best Research Paper Award 2022
NSF CAREER Award 2022
UVA ECE Department Faculty Research Award 2022
J.P. Morgan AI Faculty Research Award 2022
J.P. Morgan AI Faculty Research Award 2021
Cisco Faculty Research Award 2021
AAAI New Faculty Highlights 2021
INFORMS QSR Best Refereed Paper Finalist 2019
INFORMS QSR Best Student Paper Finalist 2019

Featured Grants & Projects

NSF CAREER Program CAREER: Toward A Knowledge-Guided Framework for Personalized Decision Making This project develops a suite of novel causal inference models and algorithms to analyze observational data by harnessing the power of human knowledge and gaining deeper insights to advance personalized decision making.
Read More
NSF III Core Program Collaborative Research: III: Small: Graph-Oriented Usable Interpretation This project aims to systematically explore usable interpretation in three different stages of a graph learning pipeline in backward order, ranging from system diagnosis, model improvement, back to data refinement.
Read More
NSF SC&C Program SCC-IRG Track 1: Community-Responsive Electrified and Adaptive Transit Ecosystem (CREATE): Planning, Operations, and Management The overarching research goal of this award is to tackle interrelated challenges that arise in the planning, operations, and management of public bus fleet electrification.
Read More
NSF III Core Program III: Small: Collaborative Research: Demystifying Deep Learning on Graphs: From Basic Operations to Applications The primary goal of this project is to develop novel operations to improve the essential building blocks of deep learning algorithms for graphs, propelling the state-of-the-art graph mining and deep learning research to a new frontier and advancing graph-related applications from different disciplines.
Read More
NSF SaTC Core Program SaTC: CORE: Small: Empowering Network Attack Detection with Complex Graph Modeling This project develops new attack detection approaches for large networks using complex graph modeling.
Read More
NSF SAI Program Collaborative Research: SAI-R: Dynamical Coupling of Physical and Social Infrastructures: Evaluating the Impacts of Social Capital on Access to Safe Well Water This SAI research project examines the availability of potable drinking water to individuals and households in settings where private wells are the predominant source of water for residents.
Read More
ONR Grant Modeling and Predicting Causal Effects on Complex Networks
DOE/Jefferson Lab Grant Graph Learning for Efficient and Explainable Operation of Particle Accelerators