We inhabit a vast, uncertain, and dynamic universe. To succeed in such an environment, machine learning approaches must handle massive amounts of noisy, changing evidence. My research tackles these challenges: I devise scalable algorithms for big data, design probabilistic models that gracefully capture uncertainty, and develop techniques for streaming inference that provide theoretical guarantees.

In addition to addressing fundamental challenges in machine learning, my work is also driven by important, practical questions in artificial intelligence. How can we use the wealth of knowledge on the Web to construct structured knowledge bases? When a user rates a new item, how can we update recommendations so other similar users benefit? What can we learn about the invisible influences of organizations from the social media activity of their followers? The central thread connecting these diverse questions is the need to exploit relationships and dependencies between instances -- whether they are facts in a knowledge base, items in a product catalog, or users of a social network.