Assistant Professor and Faculty Fellow at the NYU Center for Data Science

Mom, you remember I’m a political scientist, right? Well, you know what, things have been changing really fast in our field these last few years because of generative AI and all these computational methods. I’m very optimistic about all the mess that’s going on around us! We can now revisit old theories with a much stronger microscope and pick up subtle patterns in political behavior that older approaches couldn’t see. I’m taking advantage of this brave new world to study how political identity organizes the way people make decisions and, the other way around, how you can take a person’s behavior and work backward to recover what their political identity actually is. My broader agenda is to “embed” political identity inside computer vision tools themselves, so that identity stops being something bolted on after the fact and becomes part of how the model sees behavior from the start. I believe this matters a great deal for computational political science. I’m on the job market this year, wish me luck!

I see my research as situated primarily within the following fields:

Computational Political Behavior (CPB)
Political Communication (PC)
American Politics (AP)

I am on the job market 2026-27

I am currently working on:

Reading Between the Eyes: A Framework for Recovering (Political) Identity from Eye‑Tracking Weak Signal. (AP) (PC) (CPB)

Job Market Paper

I build a classifier that uses computer vision and gaze data to learn political identity from a weak behavioral signal of eye tracking.

Human versus Computer Vision. (CPB)

I study what machines learn when they are trained to imitate human behavior, using visual attention as a case where their predictions can be compared directly with people. I show where that imitation fails across people and which of those failures machines can learn to correct.

Unpacking the Eye of the Beholder: Social Location, Identity, and the Moving Target of Political Perspectives. (AP) (CPB)

I develop and test a classifier for measuring (political) identity-specific visual sentiment.

Vision and Division: How Partisan Identity Patterns Visual Processing of Political Imagery. (AP) (CPB)

In a large-scale eye-tracking study, we show how motivated visual processing works on partisan divides.

Visual Political Polarization. (PAP) (AP) (PC) (CPB)

Abstract

Most theories of political polarization assume partisans see one thing and diverge in interpretation. I propose a theory of visual political polarization holding that this divergence begins partly in viewing itself, as opposing viewers examine images differently and those differences shape their evaluations. A pilot of 608 U.S. adults provides initial evidence. Under free viewing, opposing positions divide scanning and evaluations, and the gap widens on images viewed through shorter fixations. Interrupted political viewing, which directs the eye from outside, compresses the gap between opposing evaluations, while activated political identity, which leaves the viewer directing it, moves gaze but leaves the evaluative gap unchanged. Altogether, the main study provides the first systematic account of the visual component of political polarization in a large-N sample. The pilot supplies effect sizes for the preregistered study.

I develop and test a theory of visual political polarization, focusing on how people become polarized through the way they visually perceive the world.

The rest is in my CV.