Biography

I am a tenure-track assistant professor in the School of Computing, University of Connecticut (UConn) since August 2020. Before that, I was a research staff member at NEC Labs America in Princeton, NJ, since July 2016. I received my Ph.D. degree in the Department of Electrical and Computer Engineering from University of California San Diego (UCSD) with the guidance of Prof. David A. Meyer in June 2016. I also work closely with Prof. Dacheng Tao from Nanyang Technological University (NTU).

My research focuses on developing AI and machine learning models in the form of continual learning, graph machine learning, domain generalization, reinforcement learning, LLMs, and foundation models, to perform time series analysis (e.g., representation, similarity search, prediction/forecasting, and anomaly detection), graph representation learning, and decision making, making them more effective, trustworthy, interpretable, and robust for continuously evolving real world applications (e.g., evolving graphs, dynamic systems, physical world, IoT systems, environmental science, healthcare and biomedical data). Three of my papers DARNN, HetGNN, and MSCRED have been ranked as the most influential papers in IJCAI 2017, KDD 2019, and AAAI 2019 by paperdigest.org, respectively. I received the prestigious NSF Career Award in 2024 and Frontiers of Science Award (FSA) in the same year. I also received UCONN Research Excellence Program (REP) Award in 2021.

[Survey of LLMs for Time Series Analysis]: Empowering Time Series Analysis with Large Language Models: A Survey [Paper] [Github]

[Survey of Continual Learning on Graphs]: Continual Learning on Graphs: Challenges, Solutions, and Opportunities [Paper] [Github]

[Recruiting]: Looking for Ph.D. students in Machine Learning/Graph Representation Learning/Time Series Analysis/LLMs & Foundation Models/Reinforcement Learning to join our group! Please send me an email with your CV and transcript.