Gabriel Chuang

Gabriel Chuang

I am a first-year PhD student in Computer Science at Columbia University, where I am fortunate to be advised by Augustin Chaintreau and Cliff Stein. I am currently interested in the general realms of {causal inference, fairness, redistricting, and social networks}. Other things I think are cool include {sampling, spatial data, learning theory, online graph algorithms, electoral dynamics, and AI regulation}.

Prior to Columbia, I spent one year as a staff researcher at Duke Unviersity, working with Jonathan Mattingly and Greg Herschlag on using hierarchical MCMC methods to sample Congressional districting plans. I did my undergrad at Carnegie Mellon University (CMU), where I got concentrations in Algorithms & Complexity and Robotics and worked with Anupam Gupta on online perfect matchings with deadlines. Before that, I spent a summer at NASA's Ames Research Center and a few semesters working in the Biorobotics lab at CMU.

Outside of work, I enjoy drawing (I post my work on Instagram), hiking, and playing board games (recent favorites: Ark Nova, Concordia, Wingspan).

I am supported by the NSF Graduate Research Fellowship.

Publications, Preprints


I find great joy in teaching undergrad-level CS to students new to formal mathematics, especially topics that have to do with discrete math, proofs, and Functional Programming. I have TA'd in the following capacities:


gtc2117 [at] columbia [dot] edu, Google Scholar, LinkedIn