I'm a Ph.D candidate in Computer Science at Brown University focusing on Artificial Intelligence, advised by Prof. Michael Littman.
My research investigates the foundations of Artificial Intelligence and applications thereof to scientific and societal challenges.
I'm currently focused on abstraction and its role in intelligence. I study how intelligent agents model the worlds they inhabit, focusing on the representational practices that underly effective learning and planning. In these endeavors I value simple mathematical models with high explanatory power that allow for reproducible empirical inquiry. To these ends, I typically work with the Reinforcement Learning paradigm, drawing on tools from computational learning theory, probability, complex systems, and information theory.
I also care deeply about responsible applications of AI to problems of relevance in the world, with a current focus on the mission of computational sustainability.
Bandit-Based Solar Panel Control
We advocate for the use of bandit methods for solar tracking and control and verify that a bandit-based approach can increase energy harvested compared to typical solar trackers. Joint work with Edward C. Williams, Stephen Brawner, Emily Reif, and Michael Littman.
Toward Good Abstractions for Lifelong Learning
NIPS 2017 Workshop on Hierarchical Reinforcement Learning
Full list of papers here.
For fun, I'm a big fan of basketball, hiking, fitness, cooking, snowboarding, and music (I play guitar/violin and mostly listen to folk, progressive metal, classical, and trance).
I'm an advocate of a few specific causes: sustainability efforts, existential risk minimization, space exploration, and improving the diversity, quality, and accessibility of STEM education.
Always up for a chat, and happy to visit labs or companies to give talks - shoot me an email if you'd like to discuss further!