I am a researcher with research interests in artificial intelligence and machine learning.
I obtained my Ph.D. degree from Department of Computer Science and Technology in Nanjing University in June 2022,
where I was very fortunate to be advised by professor Yu-Feng Li (李宇峰).
Currently, I am an Assistant Professor in School of Intelligence Science and Technology,
Nanjing University (Suzhou Campus).
I am also a member of LAMDA Group (机器学习与数据挖掘研究所),
which is led by professor Zhi-Hua Zhou (周志华).
📖 Research
The long-term research goal of our team is to enhance the reasoning and planning capabilities of AI models in both digital and physical worlds,
contributing to the advancement of artificial general intelligence (AGI). Our core approach is neuro-symbolic learning,
which bridges data-driven machine learning with knowledge-driven symbolic reasoning — often regarded as the hallmark of third-generation AI.
The neural component provides grounding in perception and physical interaction, while the symbolic component augments reasoning and planning.
Recently, our research has primarily focused on overcoming the limitations of reasoning and planning capabilities in LLMs/MLLMs and
building AI agents capable of thinking, reasoning, planning, and acting in complex, dynamic, multi-modal environments.
Potential applications include, but are not limited to, Multi-Modal Reasoning & Planning (Modalities such as Language, Image, Tabular, Chart, etc.), Game Agent, Embodied Agent, Math Reasoning, Medical AI, etc.
Neuro-Symbolic Learning
How to bridge data-drive machine learning with knowledge-driven symbolic reasoning?
Multi-Modal Data
Language
Vision
Tabular
……
Symbolic Knowledge
Logic
Rule
Search
……
Multi-Modal Agent with Reasoning and Planning Capabilities
Perceive the Environment via Multi-Modal Input
Think, Reason, and Plan with the Environment Perception
