I am a computer science PhD student at Columbia University, where I am advised by David Blei. My research develops machine learning methodology to uncover insights into human behavior in labor economics and political science, among other fields in the social sciences. Methodologically, I focus on NLP, probabilistic modeling, and causal inference.

I am completing the final year of my PhD at Columbia University, where I have been 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.

I will be starting as a postdoctoral fellow with the Harvard Data Science Initiative in Fall 2023.

Here is my curriculum vitae.

Recent News

Selected Papers

CAREER: Transfer Learning for Economic Prediction of Labor Sequence Data
K Vafa, E Palikot, T Du, A Kanodia, S Athey, D Blei
In submission (appeared at NeurIPS 2022 Workshop on Distribution Shifts)
[Paper] [Code] [Podcast]

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