28+ Citations · 4,200+ Research Reads · IEEE Author
Developing Interpretable AI for U.S. Workforce, Healthcare, and Financial Systems
Advancing intelligent systems through explainable machine learning and scalable applied AI research.
Published & Indexed On
Research & Publications
Selected Peer Reviewed Research
Focused on developing interpretable and scalable AI systems across healthcare, workforce analytics, and financial forecasting. My research emphasizes transparency, real-world deployment, and measurable impact through peer-reviewed publications and indexed conference proceedings.
1. Interpretable Medical Imaging AI
MSRFF: CNN–Vision Transformer Framework
IEEE ICCIAA 2026 (Accepted)
Hybrid deep learning model for interpretable diabetic retinopathy detection in clinical settings.
2. Explainable Workforce Analytics
Stacking Ensemble for Employee Turnover Prediction
IEEE COMPAS 2026 | IEEE Xplore
SHAP-driven ensemble framework improving transparency in HR decision-making systems.
3. Financial Forecasting with ML
Stock Market Price Prediction Using ML Techniques
AIJSER Journal (2024)
Time-series modeling for stock forecasting with 28+ citations and 4,000+ research reads.
Applied Research & Impact
From Research to Real-World AI Systems
Translating peer-reviewed research into scalable and interpretable AI systems across healthcare, workforce analytics, and financial forecasting.
Interpretable AI Systems
Scalable & Explainable AI Architectures
Developing explainable machine learning frameworks using SHAP, ensemble methods, CNN–Vision Transformers, and time-series forecasting to improve transparency and reliability.
Certificates
Microsoft Applied Skills – AI Workflow Automation
Microsoft Applied Skills · 2026
Validates hands-on experience in AI workflow automation and enterprise productivity systems.
AI Workflow Automation · AI Chat Integration · Process Automation · Enterprise Systems
Kaggle – Intro to Machine Learning (2026)
Kaggle Certified · February 2026
Foundational training in supervised learning, model validation, decision trees, random forests, and evaluation metrics.
Machine Learning · Model Validation · Decision Trees · Random Forests · MAE
Frequently Asked Questions
Have Questions About My Work?
Here you’ll find answers about my research, technical expertise, and professional background in Machine Learning, AI, and Data Analytics.
Who is Mahfuz Islam Khan Jabed?
Mahfuz Islam Khan Jabed is a Machine Learning Engineer and researcher pursuing a Master’s in Information Technology in the United States. His work focuses on Artificial Intelligence, interpretable machine learning systems, and advanced data-driven technologies designed for real-world impact.
What is his research focus?
His research centers on developing transparent, scalable, and high-performance machine learning systems. He works on ensemble learning models, time-series forecasting architectures, and explainable AI frameworks that improve reliability and practical deployment of intelligent systems.
Why does he emphasize interpretable AI?
As AI systems become deeply integrated into financial services, enterprise analytics, and large-scale digital platforms, transparency and accountability are essential. His work prioritizes explainable modeling techniques that enhance trust, support regulatory awareness, and strengthen long-term system stability.
What real-world challenges does his work address?
His research supports decision-making in financial forecasting, workforce analytics, predictive monitoring systems, and intelligent data environments. The goal is to design AI solutions that are accurate, auditable, and capable of operating at scale.
How does his work contribute to modern digital infrastructure?
By developing interpretable and scalable AI frameworks, his research contributes to strengthening data-driven ecosystems that power financial analytics, intelligent monitoring systems, and enterprise-level forecasting platforms. His work aligns with the growing demand for responsible and resilient AI technologies in today’s digital economy.
Where are his research works and technical contributions indexed?
His publications and technical projects are indexed on platforms such as:
- Google Scholar
- ResearchGate
- IEEE Xplore
- Kaggle
His work has received academic citations, reflecting engagement within the international machine learning research community.
What technologies does he specialize in?
He specializes in:
- Machine Learning & Deep Learning
- Machine Learning & Deep Learning
- Explainable AI (SHAP, model interpretability)
- Time-Series Forecasting Systems
- Predictive Analytics & Scalable Data Systems
His work has received academic citations, reflecting engagement within the international machine learning research community.
What is his long-term professional vision?
He aims to build intelligent, interpretable, and high-impact AI systems that enhance financial resilience, strengthen analytical decision-making, and support secure, responsible digital ecosystems.
Let’s Build Intelligent Systems That Matter.
Interested in research collaboration, AI innovation, or advanced machine learning solutions?
I’m open to academic partnerships, technical discussions, and industry opportunities.