About

I’m a postdoctoral fellow at Harvard University as part of the Harvard Data Science Initiative. I work on behavioral machine learning: developing tools to evaluate how AI models understand the world so we can create shared understanding between models and people. I’m also an affiliate with LIDS at MIT.

I completed my PhD in computer science at Columbia University in 2023, where I was advised by David Blei. During my PhD, I was an NSF GRFP Fellow and Cheung-Kong Innovation Doctoral Fellow. Upon graduating, I received the Morton B. Friedman Memorial Prize for excellence in engineering.

Here is my curriculum vitae.

Email: kvafa AT g.harvard.edu

Recent Papers

Here are some papers from the past year that are representative of what I work on. See my CV for a complete list.

What has a Foundation Model Found? Using Inductive Bias to Probe for World Models
K Vafa, P Chang, A Rambachan, S Mullainathan
International Conference on Machine Learning (ICML), 2025
[Paper] [Code]

Potemkin Understanding in Large Language Models
M Mancoridis, K Vafa, B Weeks, S Mullainathan
International Conference on Machine Learning (ICML), 2025
[Paper] [Code]

Estimating Wage Disparities Using Foundation Models
K Vafa, S Athey, D Blei
Proceedings of the National Academy of Sciences (PNAS), 2025
[PNAS] [arXiv] [Code]

Evaluating the World Model Implicit in a Generative Model
K Vafa, J Y Chen, A Rambachan, J Kleinberg, S Mullainathan
Neural Information Processing Systems (NeurIPS), 2024 [spotlight]
[Paper] [Code] [Twitter summary] Press: Nature, MIT News, Wall Street Journal

Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function
K Vafa, A Rambachan, S Mullainathan
International Conference on Machine Learning (ICML), 2024
[Paper] [Code] [MIT News]