I specialize in AI-driven software development, deep learning, and medical image processing, with a strong focus on explainable AI (XAI), feature selection, and scalable AI architectures. My expertise extends to bioinformatics, AI-powered IoT systems, and intelligent automation, where I build high-performance AI applications for real-world challenges.
Currently, I am working as an AI Software Engineer at DIU AI Research Lab, where I develop AI-powered solutions, manage research-driven projects, and optimize AI systems for scalability and efficiency. Additionally, I serve as a Research Assistant at the Centre for Advanced Machine Learning and Applications (CAMLAs), contributing to cutting-edge research in medical AI, computational biology, and feature selection techniques.
With a deep background in software engineering, I develop scalable AI-driven platforms using FastAPI, PyTorch, and React, optimizing AI inference pipelines, real-time data processing, and cloud-based AI systems. My work integrates computer vision, medical AI, and feature engineering to improve model interpretability and efficiency.
 Medical AI & Bioinformatics |
AI for oncology research, whole-slide image (WSI) processing, and AI-assisted diagnostics. |
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 Software Development & AI Systems |
Architecting scalable AI software solutions with FastAPI, React, and cloud services. |
 AI & IoT Integration |
Developing AI-powered IoT systems for smart automation and real-time analytics. |
| 🤖 AI/ML | 🔌 IoT | 🔬 Research | 💻 Development |
|---|---|---|---|
| Computer Vision | Embedded Systems | Medical Imaging | Full-stack Apps |
| Deep Learning | Sensor Networks | Bioinformatics | Cloud Solutions |
| Neural Networks | Edge Computing | Algorithm Design | Mobile Development |
| NLP | Smart Agriculture | Data Analysis | API Integration |
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People's Choice Award 3MT (3-minute thesis) competition organized by Daffodil International University |
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Cruze, F. R. D., Hosen, M. F., Ali, M. S., Mollah, M. S. H., Uddin, M. S., & Morshed, M. (2024, October). Unveiling Fake News with an Attention-Based CNN Model Using Hybrid Features. In 2024 2nd International Conference on Information and Communication Technology (ICICT) (pp. 41-45). IEEE. |
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