the Recurse Center: six weeks of learning

this fall, i'll be at the Recurse Center in New York City studying deep reinforcement learning + misinformation, democracy, and the integrity of our information ecosystem. i'm curious to see how this time will challenge me to be a better programmer, social scientist, technologist, and writer. i wrote about my motivation and what i'm hoping to learn here.

this is a tentative plan for my batch, with an understanding that it may change depending on momentum, inspiration, and learnings from my fellow Recursers. send me feedback via Twitter!

week 1

September 23 - 29

introduction to deep reinforcement learning

  • via Spinning Up in Deep RL & OpenAI Gym

    • implement solutions to these exercises

    • implement the vanilla policy gradient

    • implement proximal policy optimization

    • finish porting the lunar-lander gym environment to ocean-gym

      • ocean-gym is a fun side project where i port existing gym environments to be ocean-themed

      • in whale-lunar-lander, the agent will land a whale on the moon instead of a lunar lander

      • this has helped me to understand a bit more about what goes into an RL environment!

week 2

September 30 - October 6

applied deep reinforcement learning

last summer i researched smart cities with Sidewalk Labs, so i was very excited to speak with Kathy Jang, a researcher (and Recurser!) who works on traffic control with deep reinforcement learning via the Flow project out of Berkeley's Mobile Sensing Lab. i'm excited about real-world applications of deep reinforcement learning and plan to spend at least a week exploring what can be done in this space.

  • via the Flow project

    • work through these introductory tutorials

    • play with RLLib

    • build a small simulation

depending on the outcome of the first two weeks, i plan to either extend the exploration on deep RL into week 3 or transition into working on misinformation + NLP on Twitter immediately. either way, i will be spending 3-4 weeks studying misinformation.

weeks 3/4 - 6

October 7 - October 31

misinformation, natural language processing, and Twitter

  • via the Elections Integrity Hub datasets from Twitter

    • Russian troll bot vs. non-Russian troll bot

    • social network analysis + unsupervised learning on these actors to understand if + how they're linked together

    • test natural language embedding techniques to cluster tweets which are similar in content

    • scrape Twitter for other similar political data

    • train a classifier on this data to identify malicious actors and/or misleading tweets

  • test this out on today's context

    • test out the classifiers on current Twitter data in the context of the 2020 American election

    • test these methods on tweets surrounding the October 2019 Canadian federal election

  • aggregate relevant datasets for further research

i'm hopeful that this plan will serve as an ambitious-yet-malleable starting point for what will hopefully be six weeks of productive programming, research, and writing, among talented strangers who will turn out to be collaborators, mentors, and friends.

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