About

MSc Student in Computational Social Systems · Graz University of Technology

I am a Master\’s student in Computational Social Systems at the Graz University of Technology (TU Graz) in Graz, Austria. My academic work sits at the interface of computer science and the social sciences: I use computational methods — machine learning, natural language processing, agent-based simulation, and interactive visualization — to study how data, digital systems, and algorithmic processes shape and reflect social behavior.

My current research interests include the computational analysis of social phenomena, bias detection in textual data, agent-based simulation of group dynamics, and the design of interpretable visual analytics tools. I am particularly drawn to the methodological question of when and how computational tools can generate meaningful social insight — and when they risk simplifying or obscuring what they set out to study.

I hold a Bachelor\’s degree in Computer Science and Engineering from Daffodil International University (Bangladesh), where I was first introduced to applied research and empirical analysis. My undergraduate work led to two peer-reviewed publications in IEEE Xplore: one on an IoT-based system for monitoring water drainage overload in urban environments, and one on a convolutional neural network system for classifying citrus fruits. These projects gave me an early grounding in applying computational techniques to real-world problems and in the process of research from question to publication.

Before beginning my Master\’s studies in Graz, I spent several years working in data engineering and technical systems roles in Bangladesh. This experience gave me practical familiarity with data pipelines, structured documentation, error analysis, and applied Python programming — and deepened my interest in how computational systems behave and fail in operational contexts.

At TU Graz, I have had the opportunity to engage with a broad range of methods and research questions: reconstructing missing time-series sensor data using spatial interpolation; detecting gender bias in literary review corpora using NLP and statistical testing; modeling spatial conflict dynamics through agent-based simulation; building prediction pipelines through data mining; and constructing interactive visualizations of global energy trade flows. Together, these projects reflect both my developing methodological range and my consistent interest in understanding social systems through computation.

I am pursuing a research trajectory oriented toward doctoral study in computational social science or a closely related field. My goal is to contribute to research that is methodologically rigorous, socially grounded, and committed to transparency and reproducibility. I welcome academic exchange, collaboration opportunities, and conversations about shared research interests.

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Education

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Professional Background

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