Nathan is a robot learning researcher, writer, non-professional athlete, and a mental-health advocate.

There really is too much noise.

I do my best to only contribute high signal content on machine learning, human optimization, and the nature of life.

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Blogos

Job Hunt as a PhD in AI / ML / RL: How it Actually Happens

The full breakdown of what a job search in AI with a new Ph.D. looks like.

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The last reliable (available) path into AI

A confluence of trends leaves AI+something, rather than pure AI, as the last great path into machine learning research.

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ML/RL & Microrobotics

A memo I wrote to my research group on the open questions when applying machine learning to another research area: novel microrobotics.

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Papers

Synergy of Prediction and Control in Model-based Reinforcement Learning

My thesis on model-based RL. Let's make models work with tasks!

Reward Reports for Reinforcement Learning

We propose a new type of documentation for dynamic machine learning (and reinforcement learning) systems!

Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems

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We detail why reinforcement learning systems pose a different type of (dynamic) risks to society. This paper outlines the different types of feedback present in RL systems, the risks they pose, and a path forward for policymakers.
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