About Fuad Hasan

Learn about Fuad Hasan, his research focus, academic background, leadership, and work in machine learning and healthcare AI.

Fuad Hasan is a Bangladeshi undergraduate machine learning researcher and final-year B.Sc. student in Software Engineering at Daffodil International University. He serves as a Research Assistant in the university's Data Science Lab and as Research Secretary of the DIU Data Science Club. His work focuses on artificial intelligence, machine learning, explainable AI, Health AI, public-health data analytics and AI-supported academic learning systems.

I am interested in the point where software engineering, machine learning and meaningful human problems meet. My journey did not begin with a single perfect research question. It developed through coursework, experimentation, teamwork, leadership and a growing desire to understand how technology can support better decisions in healthcare, education and society.

Today, I approach research as both a technical and human responsibility. Building a model is important, but so are the questions behind it: Is the problem worth solving? Is the evidence reliable? Can the result be explained? Would the system remain useful outside the dataset on which it was developed? These questions continue to shape the researcher I am becoming.

Building My Foundation in Software Engineering

I began my B.Sc. in Software Engineering at Daffodil International University in June 2023. My degree gave me a practical foundation in programming, problem-solving, data and software development. It also taught me to think in terms of complete systems rather than isolated algorithms: how information is collected, how components interact, how users experience a product and how technical decisions affect reliability.

My academic record includes a CGPA of 3.60 out of 4.00 and a most recent semester GPA of 3.83. More important than any single number, however, has been the process of learning how to connect classroom knowledge with research and innovation. That connection gradually moved my interests toward artificial intelligence and machine learning.

Learning Research Through Practice

Since February 2025, I have worked as a Research Assistant in the Data Science Lab at Daffodil International University. This experience introduced me to the everyday work behind academic research: reviewing literature, organizing technical documentation, coordinating research activities, experimenting with datasets and discussing how models should be evaluated and reported.

Working in a research environment helped me understand that a paper is not simply a polished final document. It is the result of many smaller decisions, revisions and failed attempts. A credible result depends on a clear question, appropriate data, honest evaluation and evidence that another person can understand. This experience strengthened my interest in explainability, cross-validation, ROC-AUC, PR-AUC, geographic holdouts and subject-level evaluation.

My technical toolkit grew alongside this work. I use Python, Pandas and NumPy for data preparation and analysis, and Scikit-learn, XGBoost and LightGBM for machine learning. I use SHAP and permutation importance to examine model behavior, while Git, GitHub, Jupyter Notebook, Google Colab and LaTeX support reproducible experimentation and academic writing.

Research Interests with a Human Purpose

My research interests include artificial intelligence, machine learning, explainable AI, Health AI, public-health data analytics and AI-supported academic learning systems. Although these areas are technically diverse, they share one theme: using data and intelligent systems to make complex information more useful to people.

I am especially drawn to Health AI because healthcare decisions require more than a confident prediction. Models need to be evaluated carefully, interpreted responsibly and designed around the needs of patients and professionals. Public-health analytics expands this perspective from individual predictions to patterns affecting communities and populations.

I am also interested in retrieval-augmented generation, vector search, Supabase PGVector and evidence-card approaches. These tools can help organize knowledge and provide better context for AI-supported systems. My goal is not to treat AI as a replacement for human expertise, but to explore how it can support clearer, safer and more evidence-aware decisions.

Mom's Care AI: Technology for Maternal Health

One project that reflects this direction is Mom's Care AI, an AI-based maternal-health support system. I contributed to work involving multilingual interaction, medical-record organization, risk-awareness features and user-centered support for mothers and doctors.

The project showed me why accessibility matters. A technically capable system is not useful if people cannot communicate with it comfortably or understand its guidance. Maternal-health support also demands careful boundaries: an AI system should help users organize information, recognize possible risk and seek appropriate care without pretending to replace qualified medical professionals.

EcoRangers and the NASA Space Apps Challenge

A defining moment in my journey came through Team EcoRangers in the NASA International Space Apps Challenge 2024. As team leader, I coordinated task planning, idea development, communication, prototype presentation and the preparation of our final submission.

EcoRangers was designed as an environmental-learning project that connected innovation with accessible communication. Our team represented Daffodil International University and advanced to the global finalist stage, placing among the Top 40 teams worldwide.

Becoming a NASA Space Apps Challenge Global Finalist taught me that strong innovation depends on more than coding. A team needs a shared problem, clear responsibilities, the ability to explain an idea and the discipline to keep improving under time pressure. It also showed me how environmental data and educational technology can be combined to engage people with scientific information.

From Global Finalist to National Champion

In 2026, I became a National Champion in the AI Build-A-Thon. Achieving first place nationally required AI solution development, teamwork, problem-solving, and a clear presentation of the proposed system.

The experience strengthened skills that are valuable in both innovation and research. Technical ability matters, but so does the capacity to define the problem, work effectively with others, make decisions under constraints and communicate why a solution deserves attention. The achievement also encouraged me to continue developing AI systems with a practical purpose rather than treating technology as an end in itself.

Leadership Through Service

Leadership has been a continuous part of my university experience. Since November 2025, I have served as Research Secretary of the DIU Data Science Club. I support research seminars, technical discussions, collaborative initiatives and junior researchers who are beginning to explore data science and academic research.

I also serve as an Executive Member of the BASIS Student Forum, DIU Chapter, contributing to student engagement, event support and technology-focused campus activities. As a Class Representative in the Department of Software Engineering, I coordinate communication between faculty members and students regarding academic updates, schedules, course information and class-related concerns.

These roles have shaped my understanding of leadership. It is not only about directing a team or holding a position. It is about listening carefully, communicating clearly, helping people find the information they need and creating an environment where others can contribute.

Recognition and Responsibility

I have been recognized twice as a Best Student Achiever at Daffodil International University for my academic involvement, leadership, research engagement and contributions to innovation-focused work. Together with the NASA Global Finalist recognition and the AI Build-A-Thon national championship, these achievements represent important milestones in my development.

I see recognition as responsibility rather than completion. Each achievement creates a stronger reason to remain honest about what I know, continue learning what I do not know and use future opportunities to support other students and meaningful research.

The Researcher I Am Working to Become

My long-term goal is to build a research-oriented career in artificial intelligence and machine learning. I want to pursue advanced study and contribute to work in Health AI, explainable machine learning, public-health analytics and evidence-based AI systems.

Coming from Bangladesh also shapes this goal. Many widely used datasets and systems are developed in settings that do not fully represent the realities of lower-resource communities. Useful AI must consider local languages, available infrastructure, data limitations and the needs of the people expected to use it. I hope to contribute to research that is technically rigorous while remaining connected to these practical realities.

I remain open to research collaboration, graduate-study opportunities, research internships and interdisciplinary projects. I am particularly interested in connecting with researchers, faculty members, students and organizations working in machine learning, Health AI, explainable AI, public-health data and responsible AI-supported learning systems.

Fuad Hasan is based in Bangladesh and studies Software Engineering at Daffodil International University. His professional profiles, research activities, projects and achievements are documented through his official portfolio and connected academic profiles.