I am a final year Ph.D. candidate advised by Prof. Saket Anand and Prof. Chetan Arora .

My research interests lie at the intersection of Computer Vision and Deep Learning, with a focus on Active Learning, Data Fairness, and Domain Adaptation. I am broadly interested in designing data-efficient learning systems that can perform effectively under limited supervision. Toward this goal, my work explores the contextual richness of visual data and leverages model uncertainty to guide sample selection and improve model generalization. A key emphasis of my research is on reducing the reliance on large-scale annotated datasets by identifying and utilizing the most informative data.

I am currently a Computer Vision Consultant at The Habitat Trust , where I collaborate closely with ecologists and conservation practitioners to co-develop tools for wildlife monitoring and data annotation. My work involves curating large-scale Indian species datasets, building context-aware vision models for species detection and classification, and designing Active Learning and Human-in-the-Loop (HITL) pipelines to address sparse and imbalanced ecological data. These efforts aim to accelerate annotation workflows while improving the adaptability and robustness of deployed models in real-world conservation settings.

I am actively engaged in solving novel research challenges at the intersection of biodiversity, representation learning, and data-efficient training. I envision building inclusive, human-centered AI systems that support environmental resilience and sustainability.

I am on the job market. Please reach out if you think I could be a good fit for your team