Scholar
Yongqiang Chen
Google Scholar ID: huQ_Ig8AAAAJ
The Chinese University of Hong Kong
Citations & Impact
Citations
787
H-index
11
i10-index
13
Publications
20
Co-authors
22
list available
Publications
Resume (English only)
Academic Achievements
- Selected Preprints and Publications:
- 1. Discovering and Reasoning of Causality in the Hidden World with Large Language Models
- 2. Can Large Language Models Help Experimental Design for Causal Discovery?
- 3. On the Thinking-Language Modeling Gap in Large Language Models
- 4. HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment
- 5. BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
- 6. Discovery of the Hidden World with Large Language Models
- 7. A Sober Look at the Robustness of CLIPs to Spurious Features
- 8. How Interpretable are Interpretable Graph Neural Networks?
- 9. Empowering Graph Invariance Learning with Deep Spurious Infomax
Research Experience
- Postdoctoral Researcher at the Causal Learning and Reasoning (CLeaR) group, working with Prof. Kun Zhang. Previously, worked at RIKEN AIP, Tencent AI Lab, and Microsoft Research Asia.
Education
- Ph.D. in Computer Science and Engineering (CSE) from The Chinese University of Hong Kong (CUHK), graduated in 2024, supervised by Prof. James Cheng and Prof. Bo Han.
Background
- Research Interests: Causal learning and reasoning, promoting alignment, generalization, and interpretability of modern machine learning systems. Overview: Focused on developing new foundations of machine learning with causality, to empower industrial applications and scientific practice.
Miscellany
- Open for collaborations and communications. Recruiting Research Assistants, MPhil, and PhD students at multiple institutes.
