I am a Postdoctoral Research Scholar at Columbia
University in Andrew Gelman's applied statistics group.
I work on both applied statistics and developing efficient
numerical methods for statistical inference.
I got a PhD in Applied Mathematics in 2019 from Yale
where I worked with Vladimir Rokhlin. Here's a
Selected Publications and Pre-prints
Applied and computational statistics
Efficient deep learning
LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
(with H. Guo, E. P. Xing, Y. Kim)
Learning to grow pretrained models for efficient transformer training
(with P. Wang, R. Panda, L. T. Hennigen, L. Karlinsky, R. Feris, D. D. Cox, Z. Wang, Y. Kim)
Federated learning as variational inference: A scalable expectation propagation approach (with H. Guo, H. Wang, A. Gelman, E. Xing, Y. Kim)
pg2118 at columbia.edu