Biography

I am an assistant professor in the Department of Computer Science and Engineering, University of Connecticut (UConn). Before that, I was a research staff member at NEC Labs America in Princeton, NJ, since July 2016. In June 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. My thesis committee includes Prof. Lawrence Saul, Prof. Nuno Vasconcelos, Prof. Gert Lanckriet, and Prof. Julian McAuley. I also work closely with Prof. Dacheng Tao from the University of Sydney.

I have broad research interests in machine learning, data mining, deep learning, time series analysis (e.g., representation, similarity search, prediction/forecasting, and anomaly detection), graph representation learning, and reinforcement learning. Recently, I am particular interested in (1) continual learning on graphs, with a focus on evolving graphs, dynamic systems, and physical world (e.g., IoT systems, environmental science, etc.) and (2) federated learning, trustworthy reinforcement learning with applications to healthcare and biomedical data. Two of my papers DARNN and HetGNN have been ranked as the most influential papers in IJCAI 2017 (2nd) and KDD 2019 (3rd) by paperdigest.org, respectively. I received the prestigious NSF Career Award in 2024 and 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/reinforcement learning to join our group! Please send me an email with your CV and transcript.