Zhejiang University
Visual Analytics and Intelligence Group (📮 ZJUVAI)
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I am a tenure-track Assistant Professor in the School of Software at Zhejiang University. I received Ph.D. Degree in Computer Science from the State Key Laboratory of CAD&CG, Zhejiang University under the supervision of Prof. Wei Chen. My research interests include explainable artificial intelligence, visualization and AI alignment. Here is the homepage of Visual Analytics and Intelligence Group of ZJU.
I am actively recruiting Master’s students to join VAI. Research in this area involves high coding, a math background, and hard work. Visiting scholars and interns are also very welcome! If you are interested, please submit your Application.
[2025-02-12] 🔥We have presented a multimodal reasoning model, R1-Onevision. 🦖
[🤖LLM] Multimodal Reasoning Large Language Models
Advancing artificial intelligence requires models that seamlessly integrate and reason across diverse data modalities, including text, images, and audio. My research focuses on developing large-scale language models capable of sophisticated multimodal reasoning, enabling more natural and context-aware interactions. By leveraging cross-modal learning techniques, I aim to create systems that comprehend and analyze complex information, leading to more intuitive AI applications.
[ 🧭AI4Science] Automated Mathematical Theorem Proving
The automation of mathematical theorem proving stands as a pinnacle of AI research, demanding deep logical reasoning and precision. My work centers on designing large-scale models that can autonomously prove mathematical theorems, pushing the boundaries of machine reasoning. This involves integrating formal methods with machine learning to tackle complex mathematical challenges, thereby enhancing the capabilities of AI in scientific discovery.
[ 🔍Explainable AI] Visual Analytics for Large Language Models
Understanding and interpreting the decision-making processes of large language models is crucial for transparency and trust. My research in visual analytics aims to develop tools that elucidate the inner workings of these models, providing clear visual representations of their reasoning paths and outputs. By making AI systems more interpretable, this work seeks to foster greater user trust and facilitate more effective human-AI collaboration.