I am a Research Scientist at Intel AI and an Adjunct Professor at the University of Alberta.

My goal is to develop the conceptual, methodological, and mathematical foundations for the science of AI.

I am specifically interested in how the computational aspects of intelligence and agency can be understood through the lens of Reinforcement Learning. My work strives not just to identify these aspects but to translate them into concrete, algorithmic terms with empirical cash-value. I love to frame new problems, and I often take a first principles approach—translating a conceptual idea into mathematical terms which can then be analyzed, programmed, and tested with computational experiments.

NEWS

October, 2024: Attending the OpenMind Retreat in Banff, Alberta.
October, 2024: Seattle Minds and Machines Talk: The Methodological Tangle of AI Research.
August, 2024: UofA Teatime Talk: The Methodological Tangle of AI Research.
August, 2024: Attending RLC in Amherst, Massachusetts.
July, 2024: New paper on Meta-Gradient Search Control: [pdf].
June, 2024: Talk at Cohere for AI.
March, 2024: Co-organizing an RLC Workshop: Finding the Frame.
July, 2023: Attending ICML in Honolulu to present Settling the Reward Hypothesis.
May, 2023: Attending Upperbound in Edmonton, Canada.
April, 2023: Settling the Reward Hypothesis, accepted to ICML 2023 as an oral!
March, 2023: Two ICLR workshop papers, led by Rafael Rafailov: [pdf-1], [pdf-2].
February, 2023: In Barbados for the RL Workshop on Lifelong Learning.