I build trustworthy AI frameworks, LLM ensemble benchmarks, and full-stack ML applications — combining machine learning research with software engineering discipline.
Final-year Computer Science student specializing in AI engineering and full-stack development, with a focus on intelligent systems, backend architecture, and interactive software.
Machine Learning Intern
Undergraduate Teaching Assistant
From automated news bias extraction engines to voice-first Bangla LLM ledgers and benchmark trust frameworks.
Readers have no easy way to see sentiment, political framing, or bias across how different global news outlets cover the same story.
Small Bangladeshi shop owners track credit sales (baki) by hand in paper ledgers; typing transactions into a digital system is friction they won't adopt.
Running a full ML pipeline (cleaning, model selection, evaluation) on a new dataset takes real setup time, even for simple use cases.
Turning handwritten or photographed notes into something actually study-ready (summaries, audio, quizzes) usually takes several separate tools.
Foundational web APIs, relational schema architecture, and Java systems built during coursework and product exploration.
Patients need faster, more accessible ways to get preliminary health guidance and manage appointments.
Peer-to-peer clothing rental needed reliable listing, rental request, and availability logic.
A battle-tested stack spanning machine learning engineering, core algorithms, and robust full-stack development.
Daily drivers for design, intelligent pair programming, environment management, and high-velocity shipping.
