About

I’m a postdoctoral fellow at Harvard University as part of the Harvard Data Science Initiative. My research focuses on developing machine learning methods to address economic questions along with using insights from the behavioral sciences to improve machine learning methods.

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

Selected Papers

Evaluating the World Model Implicit in a Generative Model
K Vafa, J Y Chen, J Kleinberg, S Mullainathan, A Rambachan
Preprint
[Paper] [Code] [Twitter summary]

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]

Decomposing Changes in the Gender Wage Gap over Worker Careers
K Vafa, S Athey, D Blei
Presented at NBER Summer Institute (Labor Studies), 2023
[Paper] [Slides]

CAREER: A Foundation Model for Labor Sequence Data
K Vafa, E Palikot, T Du, A Kanodia, S Athey, D Blei
Transactions of Machine Learning Research (TMLR), 2023
[Paper] [Code] [Podcast] [Video]

Revisiting Topic-Guided Language Models
C Zheng, K Vafa, D Blei
Transactions of Machine Learning Research (TMLR), 2023
[Paper]

An Invariant Learning Characterization of Controlled Text Generation
C Zheng, C Shi, K Vafa, A Feder, D Blei
Association for Computational Linguistics (ACL), 2023
[Paper] [Code]

Interpretable Machine Learning for the Social Sciences: Applications in Political Science and Labor Economics
K. Vafa
Ph.D. Thesis, 2023
[Paper]

Rationales for Sequential Predictions
K Vafa, Y Deng, D Blei, A Rush
Empirical Methods in Natural Language Processing (EMNLP), 2021
[Paper] [Code] [Colab Notebook] [Video]

Assessing the Effects of Friend-to-Friend Texting on Turnout in the 2018 U.S. Midterm Elections
A Schein, K Vafa, D Sridhar, V Veitch, J Quinn, J Moffet, D Blei, D Green
The Web Conference (WWW), 2021
[Paper]

Text-Based Ideal Points
K Vafa, S Naidu, D Blei
Association for Computational Linguistics (ACL), 2020
[Paper] [Code] [Colab Notebook] [Video] [Interactive Figures]

Discrete Flows: Invertible Generative Models of Discrete Data
D Tran, K Vafa, K Agrawal, L Dinh, B Poole
Neural Information Processing Systems (NeurIPS), 2019
[Paper]