1. Paper 'Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning' under review (2025)
2. Paper 'Robust Offline Imitation Learning from Diverse Auxiliary Data' accepted at TMLR (2025)
3. Paper 'Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation' accepted at CVPR 2025 (2025)
4. Paper 'Graph-based Modelling of Superpixels for Identification of Empty Shelves' published in Pattern Recognition (2022)
5. Paper 'Deciphering Environmental Air Pollution with Large Scale City Data' presented at IJCAI 2022 (2022)
Research Experience
Worked as a data scientist for three years prior to doctoral studies.
Education
Ph.D.: Vision and Learning Group at the University of California, Riverside, under the supervision of Dr. Amit K. Roy-Chowdhury; Master's in Computer Science from the Indian Statistical Institute
Background
Research Interests: Developing sample-efficient algorithms for sequential decision-making agents, with particular emphasis on offline imitation learning and preference-based learning. Additionally, exploring robust uncertainty quantification methods and investigating how large foundation models can enhance planning capabilities in autonomous systems.