Yang Yang

About

I joined the Artificial Intelligence Thrust at the Hong Kong University of Science and Technology (Guangzhou) as a Ph.D. student in Fall 2025, under the supervision of Prof. Yutao Yue.

My research focuses on AI for knowledge discovery and creative generation. I study how large language models can synthesize knowledge from text into reusable representations and skills, identify and reconcile conflicting knowledge, uncover hidden patterns, generate scientific hypotheses, and produce original and compelling stories.

To support these goals, I explore LLM agent systems, neuro-symbolic methods that connect language models with structured knowledge and reasoning, and self-evolving mechanisms that enable AI systems to learn from experience and feedback. Ultimately, I hope to build AI that transforms existing knowledge into new and valuable discoveries, ideas, and creative content.

Yang Yang
I welcome research collaborations and internship opportunities. Please feel free to reach out.

Education

The Hong Kong University of Science and Technology (Guangzhou)

Ph.D. in Artificial Intelligence

Sep. 2025 - Present

The Chinese University of Hong Kong, Shenzhen

M.S. in Data Science

Sep. 2022 - Nov. 2024

Beijing University of Chemical Technology

B.S. in Mechanical Design, Manufacturing and Its Automation

Sep. 2018 - Jun. 2022

Selected Publications

RLIE method overview
RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
Yang Yang, Hua Xu, Zhangyi Hu, Yutao Yue
International Conference on Machine Learning, 2026
Robust hypothesis generation method overview
Robust Hypothesis Generation: LLM-Automated Language Bias for Inductive Logic Programming
Yang Yang, Jiemin Wu, Yutao Yue
AAAI 2026 Bridge Program on Logical and Symbolic Reasoning in Language Models
HyperLogic framework
HyperLogic: Enhancing Diversity and Accuracy in Rule Learning with HyperNets
Yang Yang, Wendi Ren, Shuang Li
Neural Information Processing Systems, 2024
Neuro-symbolic temporal point process framework
Neuro-Symbolic Temporal Point Processes
Yang Yang, Chao Yang, Boyang Li, Yinghao Fu, Shuang Li
International Conference on Machine Learning, 2024

Academic Service

  • Reviewer, AAAI Conference on Artificial Intelligence (AAAI)
  • Reviewer, International Conference on Learning Representations (ICLR, CCF-A)
  • Reviewer, International Conference on Machine Learning (ICML, CCF-A)
  • Reviewer, Conference on Neural Information Processing Systems (NeurIPS, CCF-A)
  • Reviewer, Transactions on Machine Learning Research (TMLR)