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I work on developing trustworthy, efficient, and generalizable AI systems. My research focuses on improving deep learning models in out-of-distribution and dynamically changing environments, while addressing fairness and bias. I study intelligence through the lens of acquiring, modulating, and composing prior knowledge, aiming to bridge human-like reasoning with modern machine learning. My work spans theory, algorithms, and applications to high-impact domains such as scientific discovery.

I work on trustworthy and efficient AI, focusing on improving generalization, robustness, and data efficiency in modern machine learning systems. My work includes developing algorithms with provable guarantees and supporting research collaborations across fundamental AI, machine learning methods, and AI for Science.

People can contact me about machine learning research, including topics such as out-of-distribution generalization, fairness, optimization, and generative models.