Language Models & Agents
We study language models, foundation models, and AI agents for robust reasoning, interaction, and real-world problem solving. Our research focuses on developing, adapting, and evaluating large language models, as well as building agentic systems capable of reasoning, tool use, and domain-specific collaboration.
AI for BIO
We develop AI technologies to address complex problems in bio, medical, and pharmaceutical domains. Our research supports biomedical research and drug discovery through data-driven models, including drug candidate discovery, drug property prediction, biomedical literature analysis, and domain-specialized LLMs for medicine and pharmacy.
AI for Education
We study AI technologies that analyze learners' understanding and learning processes, and provide personalized learning experiences. Our research aims to build intelligent educational environments through educational dialogue agents, automatic assessment, feedback generation, and learning data analysis.
AI for Research Acceleration
We develop AI technologies that automate and accelerate the entire research process, from literature exploration and hypothesis generation to experimental design, result analysis, and evaluation. By leveraging LLM agents and knowledge-based reasoning, our research supports researchers' decision-making and expands the possibilities of scientific discovery.