Home

About Myself:

I am an enthusiastic, adaptive, and fast-learning researcher with a strong interest in emerging technologies and their practical applications. My academic journey began in Electrical and Electronics Engineering, where I gained hands-on experience with PIC and AVR microcontrollers as well as Programmable Logic Controllers. This engineering background strengthened my problem-solving skills and motivated me to explore intelligent and automated systems.

To deepen my analytical and theoretical foundation, I pursued an M.S. in Mathematics, which broadened my way of thinking and improved my ability to model complex problems. Over time, my interests expanded into data science, big data, information security, and machine learning. My current PhD work further integrates these areas, focusing on machine learning, hyperparameter optimization, computational modelling, and data-driven decision support. Through this interdisciplinary journey, I aim to develop intelligent, interpretable, and impactful AI-based solutions that can contribute to scientific research and real-world applications.

My current research focuses on developing interpretable machine-learning models for computational oncology, particularly in non-small cell lung cancer. I work with digital pathology, genomic/molecular data, clinical variables, and survival modelling to build patient-specific representations that can support risk prediction, biological interpretation, and future digital-twin-based decision support.