Chuxu Zhang / 张初旭
Associate Professor of Computer Science and Engineering
Director, MINDS Lab
School of Computing, College of Engineering
University of Connecticut (UConn)
352 Mansfield Rd, Storrs, CT 06269, USA
chuxu.zhang@uconn.edu
News/ Announcement
About
I am an Associate Professor of Computer Science and Engineering at University of Connecticut. My research interests center around machine learning and data science. I have been developing machine learning models and algorithms on graph data. Besides, I apply machine learning to applications in various domains including healthcare, social media, science, and others. Now I focus on resource-efficient and safe learning on graph neural networks, large language models, and multi-modal data. My works are majorly published in top conferences of data science (e.g., KDD, WWW, and WSDM), machine learning (e.g., ICML, NeurIPS, and ICLR), and artificial intelligence (e.g., AAAI and IJCAI). I direct the Machine Intelligence and Data Science (MINDS) Lab.
I have received some awards and honors such as the NSF CAREER Award (2024), the Frontiers of Science Award (2024), the AAAI New Faculty Highlight (2023), etc. Besides, my work won several Best Paper (Candidate) Awards in major conferences including CIKM 2021, WWW 2019, and WAIM 2016.
Before joining UConn, I was an assistant professor of Computer Science at Brandeis University (2020-2024). I did my PhD study of Computer Science and Engineering at University of Notre Dame (2017-2020), advised by Professor Nitesh Chawla.
My wife and I live in the Greater New York area.
Recent Publications
Here are some recent publications. Please see my Google Scholar page for a complete list. ICML'24:
(Top 1.5% Oral Paper) From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble
ICLR'24:
Mitigating Severe Robustness Degradation on Graphs
KDD'24:
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with LLM-based Interpretable Dietary Patterns
KDD'24:
Graph Cross Supervised Learning via Generalized Knowledge
ICML'23:
When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
ICLR'23:
Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization
ICLR'23:
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
ICLR'23:
Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency
NeurIPS'22:
Label-invariant Augmentation for Semi-Supervised Graph Classification
NeurIPS'22:
Co-Modality Imbalanced Graph Contrastive Learning
KDD'22:
Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction
KDD'22:
Task-Adaptive Few-shot Node Classification