I develop foundational and applied methods in probabilistic machine learning, emphasizing questions on measurement, preference, and decision. Specifically, I leverage generative models and adaptive algorithms to design efficient and reliable systems capable of operating in real-world environments. I then apply these methods downstream to challenging applications in machine learning systems and education.

Selected Works