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.


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



Hearn's Robots (August 11th, 2021)Hearn's Robots (August 11th, 2021)
  • 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.


  • 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.