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Hello 👋
I am Jamal Hossain,
AI/ML Researcher and Computer Science graduate.
My research focuses on Healthcare AI, Computer Vision, Cybersecurity, and Explainable AI. I work on student mental health analysis, depression severity stratification, diabetic retinopathy detection, and interactive 3D visualization for network intrusion detection systems. My current work explores Federated Learning for privacy preserving, interpretable, and clinically reliable healthcare applications.
If you would like to connect via email: jamal37.ru.cse@gmail.com
ABOUT ME
I completed my B.Sc. in Computer Science and Engineering, conferred by the University of Rajshahi, with academic requirements completed at Imperial College of Engineering, Khulna. During my undergraduate years, I also taught Mathematics and ICT at the Higher Secondary level, which helped me develop stronger communication, mentoring, and academic support skills.
At the beginning of my final undergraduate year, I joined Research Den. I currently serve as a Lead Researcher there, contributing to collaborative AI research, manuscript development, and healthcare focused machine learning projects.
I also collaborate with AI research communities on vision driven medical imaging related work, with a particular interest in applying computer vision to clinically meaningful disease detection and severity assessment.
This work develops a context aware machine learning framework for student stress monitoring and practical mental health decision support.
Focuses on depression severity stratification, psychometric validation, and explainable machine learning for student mental health analysis.
Explores federated deep learning and explainable AI for privacy preserving diabetic retinopathy detection and severity grading.
Developed an interactive 3D decision space visualization framework for understanding model behavior and misclassification patterns in network intrusion detection systems.
Programming Languages: Python, Java, C#, C/C++, SQL, Bash
Machine Learning and Deep Learning: PyTorch, TensorFlow, Keras, Hugging Face Transformers, Scikit learn
Computer Vision and Medical Imaging: OpenCV, object detection, segmentation, medical image analysis
Generative AI and Retrieval Systems: LangChain, RAG, agentic AI, prompt and context engineering
Model Optimization: Quantization, pruning, model compression
Data Science and Analytics: Pandas, NumPy, Matplotlib, Seaborn, Tableau, Power BI
Tools and Platforms: AWS, Linux, Docker, Kubernetes, Git
Parallel and Distributed Computing: MPI, CUDA
Developed an OCR based system to extract medicine names, dosage, frequency, and instructions from Bangladeshi handwritten prescription images.
Built a computer vision workflow to identify suspected counterfeit currency from phone camera images captured under blur, low light, angle variation, clutter, and partial visibility.
Developed a 3D visualization system for analyzing model behavior and misclassification patterns in network intrusion detection datasets.
Apart from research, I enjoy exploring new technologies and solving problems through competitive programming. I also try to give back to the community by taking part in volunteer activities. In my free time, I like spending time with friends, watching movies and TV series, and visiting new places.