I am a PhD candidate in the CSE department at UCSD advised by Prof. Manmohan Chandrakar. My PhD research focuses on learning label-efficient representations from large-scale images and videos that are useful and transferable across a wide range of domains.

Recently, my focus is on studying the robustness and fairness properties of Generative AI and foundation models. I am fortunate to have interned at Google Research and Facebook AI Research during my PhD study. I also recieved the IPE PhD fellowship in 2021.

New standardized training framework for unsupervised adaptation with several practical insights for improving performance of domain adaptation methods.

Language guided transfer using natural text descriptions to improve unlabeled domain robustness in images and videos.

Synthetic data generation using text-to-image models for robust disaster assessment across geographies from aerial imagery using limited labeled data.

New large-scale dataset to study generalization ability of vision models across diverse geographies.

Open-world instance segmentation using a top-down framework assisted by bottom-up supervision.

Memory-based consistency losses to scale unsupervised domain adaptation to a large number of classes, including fine-grained datasets.

Selected as finalist for Best Paper Award
(Top 12 out of 641 papers, Top 2%).

Fast, accurate and flow-free video frame interpolation technique using space-time convolutions, capable of single shot multi frame prediction.

Proposes multi sample contrastive loss using instance level similarities across source and target domains for robust feature alignment and knowledge transfer.

Addresses geographical disparities between semantic segmentation datasets with minimum labeling overhead from each domain.