I work at the intersection of engineering rigor, decision science, and financial analytics, applying principles developed in safety-critical systems to the design of robust decision-support frameworks.

My professional background is rooted in embedded systems and railway product engineering, where I have spent over two decades developing and validating high-reliability solutions for complex, safety-critical environments. This work has involved system architecture, safety-critical software development, hardware–software integration, verification and validation, and compliance with stringent engineering and safety standards. In such environments, decisions must be made with incomplete information, under uncertainty, and with clear accountability—conditions that closely mirror those found in financial markets.

Building on this foundation, I apply the same engineering discipline and systems thinking to the development of financial decision support systems. My work in this area focuses on combining quantitative modelling, AI/ML techniques, and structured decision frameworks to support investment and risk-management decisions. Rather than treating finance as a purely statistical exercise, I approach it as a complex system—where models, data quality, assumptions, and human judgment must be carefully integrated.

My academic background reflects this interdisciplinary approach. I hold a Ph.D. in Decision Support Systems and Applied Machine Learning, an engineering degree in Electronics, and an MBA in Quantitative Finance. Together, these inform a consistent methodology: designing decision systems that are explainable, stress-tested, and grounded in first-principles thinking.

Across both engineering and financial domains, my focus remains the same—creating decision-support systems that are technically sound, transparent in their assumptions, and useful in real-world decision-making contexts.