Hey, I'mFaysal Ahmmed
A motivated researcher and developer specializing in machine learning, backend systems, and agentic AI. I craft scalable Systems and contribute to AI research.
Technical Excellence through Rigorous Research
Building Scalable Systems & Researching AI
Zorg IT
Junior Backend Developer
- Developed a scalable Lead Management System using NestJS, PostgreSQL, Prisma, Redis, BullMQ, and Swagger.
- Designed and implemented a RAG-based architecture for efficiently retrieving relevant product data using hybrid search capabilities.
- Implemented Agentic AI systems to enable context-aware, decision-driven automated responses for complex business workflows.
Researcher (Part-Time)
- Developed and implemented high-performance code for various Computer Science research projects in medical AI.
- Served as a primary writer for academic research papers in reputable journals and conferences.
- Collaborated with faculty and peers to advance research objectives and guided other research members in paper writing.
Junior Web Developer (Intern)
- Developed backend REST APIs for a mental health application using ASP.NET and MySQL.
- Designed and managed database structures to support secure and efficient data handling for sensitive user records.
- Collaborated with the product team to integrate backend services with responsive React & Tailwind CSS frontend requirements.
Research contributions and academic work
GRANet-Lite: A Lightweight Ghost-Residual Attention Network for Breast Cancer Classification with Comprehensive Histopathological Preprocessing Analysis
Faysal Ahmmed, Resadus Salehin Rafsan, Airin Akther, Mohaimen-Bin-Noor
Developed GRANet-Lite, a lightweight ghost-residual attention CNN for accurate and explainable breast cancer classification, optimized with histopathological preprocessing strategies and validated with Grad-CAM++ and Score-CAM.
CBAM-Xception: An Attention-Guided Framework for Skin Cancer Classification
Faysal Ahmmed, Ajmy Alaly, Samanta Mehnaj, Asef Rahman Antik, Jakir Hossen, M. F. Mridha
Proposed CBAM-Xception, an attention-guided deep learning model for accurate and interpretable skin cancer classification.
BTdiagAI: A Web‑Deployed Hybrid Framework for Brain Tumor Classification Using Optimized MRI Preprocessing and Deep Learning Fusion
Faysal Ahmmed, Asef Rahman Antik, Ajmy Alaly, Samanta Mehnaj, Md Sadi Al Huda, Md Asraf Ali
A comprehensive framework for classification using optimized MRI and fusion.
Technical experiments and engineering feats.
DriveVault – Cloud Command Center
A smart storage routing engine and unified command center for managing dozens of Google Drive accounts as a single 'Virtual Hard Drive' with real-time analytics.
Skin Cancer Detection System with XAI
A novel Classification Model achieving 86% accuracy, featuring an interpretable Grad-CAM visualization with fine-tuned MED-GEMMA LLM explanation.
YouTube Downloader Pro – Multimedia Ecosystem
A sophisticated Python-based ecosystem featuring a high-fidelity Desktop GUI and a mobile-responsive FastAPI server. Supports multi-threaded 4K downloads and persistent cloud auth.
Thoughts & Technical Deep Dives
Research and Development Activity
267
CONTRIBUTIONS IN 2026
Get in Touch
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