About
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. Prior to that, I received a BA in computer science and statistics from Harvard University. I also interned in industry at Google Brain and Facebook AI Research.
I am also a co-organizer of the Machine Learning in New York City speaker series. Each event features a New York City-based machine learning researcher presenting their work. We encourage anyone interested in machine learning research to attend.
Here is my curriculum vitae.
Recent News
- December 2022: I gave a spotlight talk about CAREER at the NeurIPS Workshop on Distribution Shifts.
- October 2022: I gave a talk about CAREER at the Federal Committee on Statistical Methodology Research and Policy Conference in Washington, D.C.
- September 2022: The Machine Learning in New York City speaker series, which I’m co-organizing, had its first session, featuring a talk from Kyunghyun Cho.
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]
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]
Training Deep Gaussian Processes with Sampling
K Vafa
NeurIPS Workshop on Approximate Bayesian Inference, 2016
[Paper]
Price Discrimination in The Princeton Review’s Online SAT Tutoring Service
K Vafa, C Haigh, A Leung, N Yonack
Journal of Technology Science
[Paper]