Rahul Ramesh

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I am a 5th year CIS Ph.D. student at the University of Pennsylvania advised by Pratik Chaudhari. I am interested in a principled data-centric view of deep learning. My research seeks to answer questions like: (1) What data should we train on, to generalize to a specific task? (2) What are properties of data that lead to successful representation learning (3) How does data change with time and how should models adapt?

I spent summer 2023 with NTT Research at the Harvard Center for Brain Science and worked on language models and in-context learning with Hidenori Tanaka. Previously, I interned at Amazon AI labs and worked on pre-trained models for image classification with Avinash Ravichandran and Aditya Deshpande.

I graduated from IIT Madras in 2019, at the top of my class, with a dual degree in computer science and a minor in physics. I was advised by Balaraman Ravindran and worked on building hierarchical representations for reinforcement learning using successor representations.

I am currently on the job market and am looking for research scientist and post-doc opportunities!

Select Publications

How Capable Can a Transformer Become? A Study on Synthetic, Interpretable Tasks
NeurIPS 23 workshop on Robustness of Few-shot Learning in Large Foundation Models

A Picture of the Space of Typical Learnable Tasks
ICML 2023

Model Zoo: A Growing Brain that Learns Continually
Rahul Ramesh, Pratik Chaudhari
ICLR 2022

Deep Reference Priors: What is the best way to pretrain a model?
Yansong Gao*, Rahul Ramesh*, Pratik Chaudhari
ICML 2022

The Value of Out-of-Distribution Data
ICML 2023