Education & experience

I am pursuing a B.Sc. in Computer Science & Engineering at Atish Dipankar University of Science & Technology (ADUST). My studies have helped me build a foundation in programming, database management, software development, and core computing concepts.

Alongside my coursework, I apply this learning through Android applications and academic research. Projects such as SpendTracker have given me opportunities to connect classroom knowledge with practical development.

My academic journey also supports my interests in artificial intelligence, image analysis, and explainable machine learning. Through these areas, I continue to develop my ability to investigate problems, evaluate approaches, and communicate technical ideas clearly.

I have worked as a freelance Digital Marketer and SEO Specialist on Fiverr since 2020. My work involves understanding client requirements and helping businesses strengthen how they present themselves online.

My professional focus includes search engine optimization, email marketing, and lead generation. I approach projects by understanding the business, identifying its audience, and organizing the work around agreed goals and deliverables.

Freelancing has strengthened my ability to work independently, manage tasks, respond to feedback, and communicate with clients throughout a project. I value clear expectations, attention to detail, and following through on the work I undertake.

My Android development experience includes building SpendTracker, a personal finance application designed to help users organize their expenses. I developed its screens and application logic using Java and Android Studio.

The project includes Firebase Authentication for registration and login, Cloud Firestore for storing application data, and Firebase Storage for profile photo uploads. I also implemented profile editing, dark mode, currency selection, PIN lock, and CSV export.

Developing SpendTracker gave me practical experience in connecting user interactions with cloud services and organizing multiple features within one application. It also helped me strengthen my understanding of application structure, data handling, and debugging.

My academic research explores practical applications of machine learning, deep learning, and explainable artificial intelligence. Research topics include ECG arrhythmia classification, depression-related social media text classification, money plant leaf anomaly screening, and district-level crop production forecasting in Bangladesh.

My research contributions include papers accepted at BECITHCON 2026 and ICEFronT 2026, alongside submissions to ICEEICT 2027. These studies involve different approaches to classification, anomaly screening, forecasting, and model interpretation.

I also completed an online presentation of Paper ID 530 at IEEE BECITHCON 2026. This experience helped me develop my ability to explain research methods, discuss findings, and communicate technical work to an academic audience.