Model Parity Alignment
A label-free approach to train small vision-language models from larger teachers with disparity-aware supervision. Published at EMNLP 2025.
I am pursuing an MSc in Computing (Artificial Intelligence and Machine Learning) at Imperial College London, after completing a B.Tech in Computer Science and Engineering at IIT Jodhpur. My work focuses on multimodal AI, including vision-language models, audio-visual speech recognition, and speech tokenization. I have published at EMNLP and WACV, and I have experience with RL-based post-training, reproducible PyTorch pipelines, and applied model deployment. Before graduate study, I worked as a Research Engineer at MetaFusion, where I built and deployed vision-language systems for traffic analytics and automated monitoring.
MSc in Computing (AI & ML)
Built and deployed vision-language systems for traffic and surveillance applications
University of Ottawa research internship on imbalanced learning methods
B.Tech in Computer Science and Engineering
A label-free approach to train small vision-language models from larger teachers with disparity-aware supervision. Published at EMNLP 2025.
Dual-model framework for creating bias-conflicting samples without explicit bias annotations. Published at WACV 2024.
Concept-intervention training for improving the faithfulness of LLM reasoning traces under RL-based post-training.