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. Recently, I have focused on developing foundational, resource-efficient, and safe machine learning algorithms and models, particularly on graph and language data. Besides, I apply them to applications in various domains including healthcare, social media, science, and others. My works are majorly published in top conferences of machine learning (e.g., ICML, NeurIPS, and ICLR) and data science (e.g., KDD). 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, WAIM 2016, etc.
Before joining UConn, I was an Assistant Professor of Computer Science at Brandeis University. I received my PhD degree in Computer Science and Engineering at University of Notre Dame in 2020, advised by Professor Nitesh Chawla.
My wife and I live in the Greater New York area.
Recent Publications
Here are selected recent publications. Please see my Google Scholar page for a complete list. NeurIPS'24:
GFT: Graph Foundation Model with Transferable Tree Vocabulary
ICML'24:
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