David Abel



I'm a Ph.D candidate in Computer Science at Brown University focusing on Reinforcement Learning, advised by Prof. Michael Littman.

I am on the academic job market.



My research investigates the foundations of machine learning and applications thereof to scientific and societal challenges.

I'm currently focused on understanding abstraction and its role in agency. I study how rational agents model the worlds they inhabit, focusing on the representational practices that underly effective learning and planning. I typically work with the Reinforcement Learning paradigm, drawing on tools from computational learning theory, computational complexity, and information theory.

I also care deeply about responsible applications of ML to problems of relevance in the world, as in the mission of computational sustainability.

Featured Work

Value Preserving Abstractions

We prove which combinations of state abstractions and options are guaranteed to preserve representation of near-optimal policies in any finite Markov Decision Process.

Planned Information Processing

We develop a model that characterizes the planned use of information processing as a meta-reasoning problem and study this model's capacity to predict human reaction times in simple tasks.

The process of abstraction
The Value of Abstraction
Current Opinions in Behavioral Science 2019

We discuss the vital role that abstraction plays in efficient decision making.

Expected-Length Option Model

We introduce and motivate the Expected-Length Model of Options, a simpler alternative for characterizing the transition and reward functions of options.

State Abstr for Lifelong RL

We study state abstractions that trade-off between compression and optimality through rate-distortion theory.

Point Options

We prove that the problem of finding options that minimize planning time is NP-Hard.


For fun, I'm a big fan of basketball, rock climbing, snowboarding, games, and music (I play violin/guitar and mostly listen to 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!