I am a Data Science Initiative postdoctoral fellow at Harvard University. 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 natural language processing, probabilistic modeling, and causal inference.
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
- July 2023: I gave a talk at NBER SI 2023 Labor Studies on decomposing changes in gender wage gaps for worker history.
- March 2023: I had the opportunity to appear on the Data Skeptic podcast to discuss CAREER. Listen to the episode here.
- 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.
Revisiting Topic-Guided Language Models
C Zheng, K Vafa, D Blei
Transactions of Machine Learning Research (TMLR), 2023 (accepted with minor revisions)
Interpretable Machine Learning for the Social Sciences: Applications in Political Science and Labor Economics
Ph.D. Thesis, 2023
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]
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
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
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