Computer Vision & ML researcher-in-training · Final-year B.Tech IT @ VSSUT Burla (2026) · Odisha, India
I build applied machine learning systems at the intersection of deep learning and classical feature engineering, with a focus on computer vision problems in healthcare and agriculture.
- Computer Vision & Deep Learning — MobileNetV2, YOLOv8, CNNs for classification and real-time detection
- Applied ML & Feature Engineering — XGBoost, GLCM texture descriptors, HSV histograms, scikit-learn
- Federated Learning & Agentic AI — independently studied at C-DOT, Government of India
- ML Deployment — Streamlit interfaces, Python desktop applications
| Organisation | Role | Period |
|---|---|---|
| C-DOT — Centre for Development of Telematics, GoI | ML Researcher Intern | May–Jul 2025 |
| LogicLens Solutions Pvt. Ltd. | ML Engineer Intern | Mar–Jul 2024 |
At C-DOT I worked directly under a Scientist 'E' (Speech Processing, ML, Optical Networks) on LLM-based telecom data analysis pipelines and independently studied federated learning architectures. At LogicLens I built real-time multi-object detection and tracking systems using YOLOv8 + OpenCV + SORT.
🌿 Sugarcane Leaf Disease Detection using Deep Feature Fusion Hybrid pipeline: MobileNetV2 deep embeddings (1,280-dim) fused with GLCM texture + HSV colour features (11-dim) → 1,291-dim vector → XGBoost classifier. 92.36% accuracy · 12 disease classes · ~12,000 images
Computer Vision · Deep Learning · Federated Learning · Agricultural AI · Pattern Recognition · Applied ML
Seeking Research Associate / Project Staff positions at IITs and NITs. Open to MS by Research and PhD pathways in Computer Vision and ML.