Cinjon Resnick

Cinjon Resnick

Welcome, the water's warm. Some writing below, check it out. If you like to circus, buzz me about our studio 1D1 in Brooklyn. If you want to jam on ideas in ML or crypto, I'm ears reach out.
 

Writing

Product

  • A study of Web3 Stablecoins, with a focus on the Collateral Ratio and how it affects the resulting Behavioral Economics.
  • A study of 0x completed jointly with Justin Cullen. We first analyze the 0x system and then synthesize it as a piece of the larger system that is Web3 Order Flow.
  • A thesis motivating Web3, with the perspective that Assets are Products. Result is a clearer understanding of what is and is not worth building on blockchains.
Hearn's RobotsHearn's Robots
  • A product idea at the intersection of machine learning and crypto.
  • Explore a world where autonomous agents can find and trade with humans as equals, contextualized in autonomous vehicles, art & NFTs, and gaming.
  • A machine learning product idea where we reclaim the world's communal spaces by turning all the walls into talking characters at the behest of street artists.
  • An abstraction of TikTok by equivocating the concept to photoshop vis a vis the layers.
  • Assuming shared ownership of all videos and extensive tools to manipulate the content, I describe eleven different ways to collaborate on a video and what that entails / engenders.

Life

  • I went on a retreat and found some kinetic magic worth talking about. Lots of hallucinating, no drugs involved.
  • A eulogy for a man who would been a best friend. Originally written for MIT's Tech Paper.

Machine Learning

  • A machine learning write-up assessing whether we could fix a spurious correlation in a dataset by just collecting some examples and fine-tuning on those examples. We also had interest in understanding what changed as we did this and whether the correlation became spurious in another way.
  • This could have been a paper, but is better as a blog post.
  • A machine learning write-up hypothesizing how to automatically yield background, character, and animation from a video.
  • A machine learning write-up hypothesizing how we can use humans in the loop to create unsupervised models with axes of variation about which we care greatly.
  • A machine learning write-up hypothesizing what is the difference between amateur movement and expert movement, with a focus on handstands.
  • A lengthy exploration into important historical papers in machine learning meets game theory. Of particular note is a deep dive into the origins of the Fermi distribution wrt its ubiquitous use as the underlying probability of whether agent A adapts agent B’s strategy. Everyone who used this approach, including Alpha-Rank most recently, can be traced to Blume '93.
  • Note: There are some latex issues to fix.
 

Built

  • Understanding research papers is hard. We tried a new way where you build the tree of concepts and then learn them going forward. We then shared the curricula and our experience with the world.
  • We ran a fellowship sponsored by Jane Street - much love - where we gave grants to students around the world to run learning groups and globally share the curricula.
  • Full compendium is on the website.
  • Research into multi-agent approaches, in particular in machine learning, is overwhelmingly for 1v1 zero-sum games. Pommerman is a response where we created a team cheap-talk 2v2 game based on the famous Bomberman game.
  • We ran three global competitions, including at NeurIPS 2018 and 2019.
Space of Motion
  • We built a service for helping athletes with their training videos via machine learning, e.g activity recognition and video search.
  • Focused on gymnastics and helping the current generation of USA olympic competitors.
OfficeHours
  • Answer questions on machine learning with chat. Was my best marketing hack yet. Thanks Intercom!

Work

Next - holdpls (2022 → Current) PhD CS NYU (Machine Learning), FAIR + NVidia (2017 → 2022) Google Brain: ML Research (2016 → 2017) Machine Learning Startups + SPC (2013 → 2015) Quora (2011 → 2013) Value Investing (2010 → 2011) MIT: Math + CS (2006 → 2010)