Agentic AI for Scientific Discovery

Building intelligent systems that autonomously advance scientific knowledge

We focus on AI methods development for computational biology and healthcare applications, especially recent Agentic AI development and its potential in scientific discovery. We believe one of the most important factors in data-driven biomedical applications is that the learned knowledge is not just specific to datasets or experiment protocols.

Our research spans from developing novel agentic AI architectures that enable large language models to function autonomously in complex data scenarios, to creating practical tools that deliver innovations to domain experts free of technical barriers. We are constantly talking to motivated students interested in these topics, either about admission or virtual research collaborations. Feel free to reach out.

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Computational Biology

Understanding the genetic basis of human complex traits

We study computational biology because every progress we make has the potential to free millions from suffering. We are devoted to developing methods that help understand the genetic basis of human complex traits. Our previous studies mostly focus on Alzheimer's disease and cancer, with ongoing work exploring connections between autism, neurodegenerative conditions, and other complex diseases through innovative computational genomics approaches.

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Agentic AI and Its Security

Building trustworthy autonomous AI systems

We study the core ML topics such as agentic AI and its security issues, with a focus on building novel agentic AI architectures that enable LLMs to automate and function in complex data scenarios. We also study the issues of its security, protecting AI from being maliciously used. Our work includes developing transparent multi-agent systems and ensuring AI systems remain reliable, interpretable, and safe in real-world applications.

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Moonshot Projects

Tackling the problems others say can't be solved

We pursue research directions that might be deemed overambitious or unconventional.

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