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Resume (English only)
Academic Achievements
- Co-first author of 'From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?', ICML 2025.
- Co-first author of 'From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium', ICML 2025.
- Co-first author of 'Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models', ICLR/ICML 2025 Workshop.
- Author of 'Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond', ICLR 2025.
- Author of 'Noisy Test-Time Adaptation in Vision-Language Models', ICLR 2025.
- Author of 'Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?', NeurIPS 2024.
- Led or contributed to multiple projects and benchmarks including AR-Bench, Landscape of Thoughts, ECON, NoRa, DeepInception, G-effect, NTTA, EOE, GRA, RGIB, Subgraph, Neural Atoms, AdaProp, and KGTuner.
Background
- PhD student at the TMLR group, Hong Kong Baptist University, focusing on Trustworthy Machine Reasoning.
- Currently a visiting student at STAIR Lab, Stanford University, working with Prof. Sanmi Koyejo.
- Previously a visiting student at LARS group, Tsinghua University, working with Prof. Quanming Yao and Prof. Yongqi Zhang.
- Research focuses on trustworthy machine reasoning with foundation models (LLMs, VLMs) to solve complex problems like mathematics and coding, and to accelerate scientific discovery in biology, chemistry, and healthcare.
- Believes reasoning is the essential pathway to achieving AGI; trustworthy reasoning includes reasoning power, robustness, safety, and explainability.
- Works on developing advanced reasoning systems (with RL and tool learning), comprehensive evaluation benchmarks, and trustworthy reasoning on (knowledge) graphs.
Co-authors: 0 (list not available)