About Me

I began my professional journey in civil engineering as an associate engineer, where complex problems had to become practical solutions. Working with physical systems taught me to think about how components interact, respect real-world constraints, and ask whether a solution works beyond the drawing board.

My subsequent training in physics, mathematics, and computer science gave that practical instinct a new direction. Physics encouraged me to look for mechanisms, mathematics provided a language for structure and uncertainty, and computer science offered the tools to turn ideas into models.

Today, I bring these perspectives together as a Computer Science PhD researcher at the University of Southampton. I develop machine learning and statistical methods that integrate retinal imaging with molecular and clinical data to seek interpretable insights into human health. My aim is to build understanding through models whose assumptions, limitations, and biological meaning can be examined.

How this perspective connects to my publications

• Practical modelling: my work on diversity in CNN ensembles examines a central challenge in medical imaging: learning from scarce and imbalanced data.
• Connecting biological systems: the oculomics and lipidomics study characterises cross-sectional associations between retinal vascular traits and molecular profiles. It connects quantitative image measurements with questions about systemic biology.
• Mathematical structure under real data constraints: RetiSEM studies interpretable causal modelling with fragmented biomedical evidence, bringing statistical reasoning to the problem of incomplete measurements.

Together, these studies connect my interests in systems, quantitative reasoning, and computation. They also frame the problems I want to work on with academic and industry teams: interpretable models, biomedical data integration, and methods that remain useful under practical data constraints.

Education and motivation

Before beginning my doctoral studies, I completed a Master’s degree in Computer Science, with a strong focus on artificial intelligence for medical imaging. That training gave me practical experience in image analysis, computational modelling, and data-driven approaches to biomedical problems.

Prior to this, I earned a Bachelor’s degree with combined emphasis on physics, mathematics, and computer science, which provided a strong foundation in analytical reasoning, mathematical formulation, and algorithmic thinking. Earlier, I also completed a three-year diploma in civil engineering and accumulated more than twelve years of professional experience in that field. That engineering background continues to shape my research through systems thinking, pragmatism, and attention to real-world constraints.

My motivation to work on health-related problems is also rooted in personal experience. I come from Bajaur Agency, a remote tribal region in Pakistan near the Pakistan-Afghanistan border, where access to healthcare infrastructure, medical services, and medication has historically been limited. Growing up in that environment shaped my awareness of health inequities and strengthened my commitment to medically relevant research.

Although circumstances prevented me from pursuing a conventional medical career, the motivation remained. Over time, I came to see computational science, engineering, and data-centric methods as powerful ways to contribute to medical research and healthcare delivery. That realization gradually brought together my interests in physics, mathematics, computer science, engineering, and medical imaging into a single research trajectory.