I'm primarily interested in the area of reinforcement learning with the goal of building sequential decision-making agents that learn as efficiently and as remarkably as people do. My work employs a variety of techniques for developing principled approaches to address the core challenges of sample-efficient reinforcement learning: generalization, exploration, and credit assignment. Lately, I've been studying information theory as a collection of tools that facilitate rigorous analysis while also remaining amenable to the design of practical, scalable agents.