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 help answer economic questions and also using insights from economics to improve machine learning models.

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. Prior to that, I received a BA in computer science and statistics from Harvard University. I have also interned in industry at Google Brain and Facebook AI Research.

Here is my curriculum vitae.

Email: kvafa AT g.harvard.edu

Recent News

Selected Papers

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

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

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

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

Training Deep Gaussian Processes with Sampling
K Vafa
NeurIPS Workshop on Approximate Bayesian Inference, 2016

Price Discrimination in The Princeton Review’s Online SAT Tutoring Service
K Vafa, C Haigh, A Leung, N Yonack
Journal of Technology Science