I am a Machine Learning PhD student at MIT CSAIL, where I am advised by Aleksander Mądry. Previously, I was a pre-doc at Microsoft Research, where I worked with Praneeth Netrapalli and Prateek Jain. Before that, I studied CS and Stats at UIUC.

I am broadly interested in developing tools to understand and steer how ML models make predictions. In particular, my recent work focuses on understanding how training data and learning algorithms shape model behavior at test-time.

Selected Papers (Show all)

ModelDiff: A Framework for Comparing Learning Algorithms
Do Input Gradients Highlight Discriminative Features?
The Pitfalls of Simplicity Bias in Neural Networks
Growing Attributed Networks through Local Processes
Number of Connected Components in a Graph: Estimation via Counting Patterns