I'm a Computer Science graduate from Daffodil International University (CGPA 2.94/4.00 | Thesis Grade: A — 3.75/4.00) with deep passion for machine learning, sustainable AI, and data analytics. My thesis involved engineering a VGG-19 CNN-powered web app that detects paddy leaf diseases with ~89% accuracy — a real solution for real farmers. I've published at IEEE, interned globally with Excelerate (Dubai/USA), and I'm now pursuing an MSc abroad to grow from capable practitioner to independent researcher.
Full-stack Flask web app integrating a VGG-19 CNN model for real-time paddy leaf disease detection across 4 disease classes. ~89% classification accuracy. Thesis Grade A (3.75/4.00). Also proposed edge-deployment on Raspberry Pi for offline rural farming environments.
NLP-based analysis of Play Store user reviews to extract actionable software optimization insights. Published as IEEE conference paper (Paper ID: 204) at the IEEE CS BDC Summer Symposium 2023.
Built and validated 4+ Power BI and Tableau dashboards. Identified 3 recurring data-quality gaps; authored best-practice memos adopted team-wide. Zero misrepresented metrics flagged in final stakeholder reviews.
Open to MSc opportunities, research collaborations, and data-driven projects worldwide.