About

Hi there! I’m a computational social psychologist, Gates Cambridge Scholar, and PhD candidate at the University of Cambridge, where I’m advised by Jason Rentfrow and David Stillwell. Previously, I’ve worked for Google Research, DeepMind, Nokia Bell Labs, and the U.S. National Science Foundation on a variety of quantitative social science, psychometrics, and AI research projects. I’ve appeared in BBC News, VICE, and USA Today, covering the social impacts of algorithmic bias and AI.

I leverage quantitative behavioral science and measurement theory to build more robust, responsible, and useful evaluations of generative AI, with a special interest in artificial social intelligence. As a Student Researcher at DeepMind, I’ve built psychometrics to quantify and responsibly shape (synthetic) personality profiles in large language models (LLMs) for safety, reliability, and usability, under the supervision of Aleksandra Faust and Maja Matarić. I’ve also co-developed new AI safety and social bias benchmarks and curated multilingual evaluation datasets as a PhD Research Intern with Google Research’s Responsible AI and Human-Centered Technology (RAI-HCT) team from October 2022 to January 2023. As an external consultant with Google, I’ve worked with Lora Aroyo and Christopher Homan to advance human computation methods and data excellence metrics for responsible AI, using Bayesian inference to model the importance of rater diversity in perceptions of conversational AI safety.

In the past, I’ve also been interested in the psychodemographic properties of Internet big data, personality psychology, mental health, and the geographic distribution of social traits. Before beginning my doctoral work, I conducted cross-cultural and clinical psychology assessment research under the mentorship of David Watson and Lee Anna Clark. From 2015 to 2019, I was a Glynn Scholar at the University of Notre Dame, where I completed my undergraduate degree in psychology with a minor in applied computer science.