My research program focuses on advancing the foundations and applications of artificial intelligence, with an emphasis on machine learning methods that integrate statistical rigor, scalability, and real-world impact. A central theme of my work is developing next-generation learning algorithms—ranging from deep neural networks to large-scale generative and foundation models—that can reason, generalize, and operate reliably in complex environments. I am particularly interested in representation learning, efficient fine-tuning, memory-augmented architectures, and reinforcement-learning–based agents as steps toward more general and adaptive AI systems. I am also deeply interested in applications of these methods to biology and medicine, where AI can drive scientific discovery, enhance clinical decision-making, and improve population health.