Research
My research interests lie broadly in trustworthy AI and human-machine collaboration. I am also excited by applications of learning algorithms in cost sensitive domains such as healthcare and criminal justice. Given constraints and feedback on a model provided by external stakeholders, I want to design algorithms that are maximally reliable / performant under the constraint set.
Conference Publications (Refereed and Archived)
Varun Babbar, Umang Bhatt, Adrian Weller
On the Utility of Prediction Sets in Human-AI Teams
International Joint Conference on Artificial Intelligence (IJCAI), 2022. (Oral)
Antonios Georgiadis*, Varun Babbar*, Fran Silavong, Sean Moran, Rob Otter
ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 segmentation
SPIE Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications
Aamir Mustafa, Aliaksei Mikhailiuk, Dan Andrei Iliescu, Varun Babbar, Rafal K Mantiuk
Training a Task-Specific Image Reconstruction Loss
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022
Workshop Publications (Refereed)
Varun Babbar, Umang Bhatt, Miri Zilka, Adrian Weller
Conformal Prediction for Resource Prioritisation in Predicting Rare and Dangerous Outcomes
NeurIPS Workshop on Human in the Loop Learning, 2022
Agathe Lherondelle, Varun Babbar, Yash Satsangi, Fran Silavong, Shaltiel Eloul, Sean Moran.
Topical: Learning Repository Embeddings from Source Code using Attention
In The 1st Workshop on Software Engineering Challenges in Financial Firms, International Conference on Software Engineering (ICSE) 2024
Papers Currently Under Review
- Varun Babbar*, Stark Guo *, Cynthia Rudin
What is different between these datasets?
Submitted to Journal of Machine Learning Research (JMLR), 2024.
Patents
Antonios Georgiadis, Fanny Silavong, Sean Moran, Rob Otter, Varun Babbar
Systems and Methods For Noise Agnostic Federated Learning
Another one in progess - relating to machine learning on source code