Writing

I have been using writing as a way to distill my ideas, but it is also fantastic to share and communicate what I am pondering. My writing here is miscellaneous thoughts I deemed worth sharing. Here are some themes that you find I touch on:

  • Technology, machine learning specifically, and deep dives into learning new things.
  • Athletics, human optimization, and long-term health.
  • Mental health, happiness, the nature of being, meditation.
Logo for Democratizing Automation -- my blog.

Democratizing automation

On machine learning and robotics. The interface of systems and society.  The border between high-level and technical thinking.

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

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

The last reliable (available) path into AI

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

ML/RL & Microrobotics

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

Debugging Deep Model-based Reinforcement Learning Systems

Things I have learned in 3 years of a young, and generally tricky research field.

All grad students (should) study graphic design

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People judge your papers by their cover. You can trick them into believing your science with pretty pictures.

Exploitation Exploration (in MBRL)

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A few lessons from model-based reinforcement learning how exploration can happen through exploitation of some metric.

Lifelong Learning 2021

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What I have been learning from recently.

Reflecting on being a graduate student (in AI) in 2020

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Starting to build my guide and advice for graduate school.

Robot learning, model-based RL, and related optimization at NeurIPs 2020

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What I learned about deep RL and model-based learning at NeurIPs 2020.

Medium tries to save its writers

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Why Medium is not a website designed for the best writers.

A Different Intro to RL in 30 Minutes

A 30 minute conceptual intro to Markov decision processes, iterative updates, and reinforcement learning.