David Abel

Portrait [CV] [Git] [Scholar] david_abel@brown.edu


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 role of representation in intelligence; I seek a unifying mathematical theory for models of the world that support effective abstraction, exploration, planning, generalization, and causal inference. I typically work with the paradigm of Reinforcement Learning, drawing on tools from computational learning theory, probability, complex systems, and information theory. I also care deeply about responsible applications of AI to scientific and societal challenges, with a current focus on the mission of Computational Sustainability.

Recent Publications

Solar Diagram

Improving Solar Panel Efficiency with Reinforcement Learning
RLDM 2017


We develop a simulation for evaluating RL approaches for Solar Tracking and verify that simple RL algorithms can increase energy harvested over existing Solar Tracking algorithms. Joint work with Emily Reif and Michael Littman.

RL and Abstraction

Near Optimal Behavior via Approximate State Abstraction
ICML 2016

(paper, arXiv version, slides)

We investigate what approximate abstractions preserve near-optimality for Reinforcement Learning agents. Joint work with Ellis Hershkowitz and Michael Littman.

Full list of papers here.


For fun, I'm a big fan of basketball, hiking, travel, fitness, snowboarding, and music (especially folk, trance, and progressive metal).

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 - shoot me an email if you'd like to discuss anything!