Research Interests
Computer Vision, Deep Learning, and Human Pose Estimation, with AI applications in healthcare, sports analytics, and agriculture. I work across skeleton-based pose modeling, quantum-classical hybrid architectures, and clinical data analysis, with a focus on data-efficient deep learning for medical imaging, agricultural imaging, and structured clinical data.
- Skeleton-based human pose estimation and action recognition
- Data-efficient deep learning: CNNs, Vision Transformers, and hybrid quantum-classical models
- Medical imaging, agricultural imaging, and plant disease detection
- Clinical NLP and relationship extraction from structured and unstructured health records
- Sports analytics and video-based action detection
Publications
Peer-Reviewed Journal Articles
Integrating Skeleton-Based Representations for Robust Yoga Pose Classification Using Deep Learning Models
Scientific Reports 16, 22144 (2026) · Nature Portfolio · JIF 3.9, 5-Year IF 4.3 · DOI: 10.1038/s41598-025-23726-0
Proposed a skeleton-based deep learning pipeline for robust human pose classification, benchmarked against standard image-based baselines, and released Yoga-16, a publicly available dataset supporting the study.