Research

Robust NLP

We study how to build language models that are reliable and safe in real-world use. Our goal is to make NLP and multimodal systems robust, fair, and trustworthy in diverse environments.

Bias & fairness / Robustness / Trustworthy AI / Multimodal reliability

Code Intelligence

We study AI systems that understand and generate code. Our goal is to help AI reason about programs and support coding and data analysis tasks.

Code generation / Program reasoning / Language–code interaction

Recommendation System

We study user-centered recommendation systems that capture how user behavior and preferences change over time. Our goal is to build AI systems that provide personalized and adaptive recommendations for real-world and educational applications.

Sequential recommendation / LLM-based recommendation / AI for education

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