From multi-model AI to multi-agent industry tools
Two products, one thread: take capable AI and make it work in the Bangladeshi context — local languages, local payments, local industries.
Hello, I'm
I build machine learning systems and AI products for Bangladesh — from explainable medical-risk models to multi-agent platforms for the garment industry. Third-year CS at BRAC University, focused on making AI useful for underserved communities.
Co-founder of a Bangladesh-based tech company shipping AI products.
Machine Learning Engineer intern.
Beta Microsoft Learn Student Ambassador.
I'm Hasanul Banna Himel, a third-year Computer Science undergraduate at BRAC University and co-founder NorthAxis. I work as an ML Engineer intern at Ontik Technology, and previously interned at Headstarter SWE.
My work sits where machine learning meets real constraints: low-resource languages, CPU-only hardware, near-zero budgets, and local payment rails like bKash and Nagad. I care about rigorous evaluation — leakage-free splits, SHAP explanations, honest baselines — and about shipping things people in Bangladesh can actually use.
Make AI accessible to the people it usually leaves out.
Medical imaging and health-risk prediction — with leakage audits and explainability built in.
Plant-disease detection at 98% accuracy and an OCR pipeline that reads textile labels from photos.
A multi-model AI workspace with local payments and a multi-agent system for the RMG industry.
Community problem mapping, offline AI for rural areas, and tools built around local needs.
As co-founder, I lead the technical side of products built for Bangladeshi users and industries — AI-first, with local payments wired in from day one.
Two products, one thread: take capable AI and make it work in the Bangladeshi context — local languages, local payments, local industries.
Explainable risk-level classification for maternal health. An Extra Trees ensemble reaches a macro AUC of 0.9396, and SHAP shows which clinical factors drive each prediction.
A fraud-detection pipeline on an NBR customs dataset from Kaggle — engineered features, gradient-boosted models and SHAP explanations, with careful handling of target-encoding leakage.
Co-authored research with collaborators at BRAC University and beyond.
Machine learning, products, civic tech and apps. Open any card for the details.
The tools I reach for, grouped by the kind of problem they solve.
Open to AI / ML engineering roles, research collaborations, and partnerships with NorthAxis.