MCA Student @ NMAMIT • Building impactful computer vision & multi-algorithm AI systems • Research & Engineering

• 🎓 MCA student (NMAMIT, Karnataka) specializing in AI, Deep Learning, and Computer Vision.
• ⚙️ Designing and optimizing production pipelines from data collection to model deployment.
• 🔬 Conducting multi-algorithm experiments (CNNs, SVMs, ensembles) with reproducible evaluations.
• 🚀 Current work: video-based food detection (YOLO + tracking + deduplication) and offline-capable detection models.

• Applied research: Delivering reproducible, benchmarked, and deployment-ready code.
• Robust engineering: Ensuring reliability (offline-first, low-resource inference) and data quality (deduplication, labeling tools).
• End-to-end system mindset: Managing everything from dataset generation to API/extension integration.
• Effective communication: Research accepted to ICoICI 2025 (major revision) — balancing academic rigor and engineering excellence.

Focused on AI/ML and system-level delivery.

• Stack: YOLO + DeepSort + OpenCV, Python
• Overview: Extracts unique food images from videos using tracking-based deduplication. Generates dataset exports (COCO/PASCAL) and metadata.
• Impact: Improves dataset quality, streamlines manual labeling, and enhances efficiency.

• Stack: CNNs, classical ML (SVM), offline inference toolchains
• Overview: Comparative study of multiple algorithms for offline detection, including cross-validation and ablation analysis.
• Impact: Supports publication-quality research and deployable models.

Consistent contributions and expertise across Python, ML frameworks, and modern dev stacks.

• Vision Systems: Object detection, tracking, and dataset pipelines (YOLO, DeepSort).
• Modeling: CNNs, transfer learning, SVMs, and model compression for offline use.
• Engineering: Flask APIs, Docker, Vercel deployments, PostgreSQL & MongoDB integration.
• Cloud: AWS, Azure, GCP deployments and automation.
• DevOps: CI/CD pipelines, containerization, Linux, Bash scripting.
• Web & API: React, FastAPI, Node.js, TypeScript, GraphQL.
• Data: PowerBI, Tableau, Scikit-learn, Keras, OpenAI API.
• Research: Experiments, ablation studies, reproducible training & evaluation.

• A Learning Model on Customer Churn Forecasting for Telecom Providers — accepted (major revision) at ICoICI 2025.
• Ongoing research in offline-compatible object detection and dataset automation.

Thank you for visiting my profile. I am dedicated to delivering practical, production-ready AI solutions underpinned by rigorous research and robust engineering. With strong communication skills and a commitment to excellence, I am always open to collaboration, research discussions, or exploring new opportunities in AI and machine learning.