Hi! I'm Varun Babbar, a PhD student in Machine Learning at Duke University, advised by Cynthia Rudin. Previously, I worked as an ML Researcher at JP Morgan London, where I developed machine learning methods to accelerate software development. This included methods to generate embeddings of repositories, designing systems based on large language models to automate code development, and code unit test quality assessment based on weakly supervised learning (no links here because these were internal projects).

I graduated from the University of Cambridge in 2022 with a BA + MEng in Information and Computer Engineering, ranking in the top 5% of my class. During my time at Cambridge, I've collaborated with Umang Bhatt and Adrian Weller on human-aligned uncertainty quantification and risk control, Sean Moran on federated learning for Covid-19 diagnosis, and Rafal Mantiuk on developing visual loss functions for image to image translation models.

I’m interested in building machine learning algorithms that are reliable, transparent, and grounded in real-world impact. My work spans both theory and application — from developing near-optimal models through discrete optimization (ICML Oral), to designing uncertainty quantification methods aligned with human judgment (IJCAI Oral, NeurIPS workshop), to developing a toolbox for analysing data distribution shift (JMLR). Earlier, I worked on improving perceptual quality in computer vision models (WACV). Across these projects, I’m drawn to problems that make ML more useful, robust, and understandable in practice.

Here are the cities I've lived in (in chronological order):
New Delhi     Mumbai     Singapore     Mumbai     Cambridge     London     Durham, North Carolina   New York

I love playing basketball and squash: I was part of Cambridge's Varsity squash team. We toured a lot in and around Cambridgeshire, playing against county clubs and university teams alike. I'm still active in Duke's squash and basketball communities, though I only play for recreation.