Bridging technical logic with public administration. Specializing in AI governance, sociotechnical systems, and disaster resilience — building tools that lower the administrative burden citizens face.
I started as a student of public administration — drawn to the question of how societies organize themselves, resolve conflict, and make collective decisions under uncertainty. The more I studied, the more I noticed something striking: social phenomena follow patterns disturbingly similar to natural ones. Public conflicts cascade like physical systems. Policy adoption curves mirror epidemic spread. Risk perception behaves like a force with its own inertia.
That observation changed everything. If social systems have structure, they can be modeled. And if they can be modeled, they can be studied with precision. That realization is what pushed me to learn software engineering — not to become a developer, but to gain a sharper lens for the phenomena I was already studying. Code became a research instrument, the same way a survey instrument or an institutional framework is.
This cross-disciplinary instinct has shaped my entire research trajectory. From KORAD-supported studies on nuclear waste siting — where I mapped the structural relationship between risk perception and public acceptance — to investigating AI ethics impact at KISDI, and analyzing undocumented migrant children's policy through Kingdon's Multiple Streams Framework, each project has been an attempt to treat social complexity with the rigor it deserves.
Underlying all of this is a deeper preoccupation: accidents and disasters. Not as isolated failures, but as symptoms of how modern societies are organized. As our world grows more interconnected and technologically layered, the problems we face are becoming less like puzzles with clean solutions and more like tangled, wicked problems — where causes are diffuse, consequences are systemic, and conventional governance frameworks fall short. I believe we are overdue for a fundamental paradigm shift in how societies anticipate, interpret, and respond to catastrophic risk. My long-term ambition is to be part of that shift — as a scholar who bridges sociotechnical theory, policy design, and empirical rigor to rethink how we govern complexity before it governs us.
My research philosophy is rooted in that same conviction: social science deserves better infrastructure. Too much insight is buried in fragmented literature, lost between disciplines, or inaccessible to researchers without deep technical training. That gap is what Galpi.AI, an AI research assistant I am currently building, is designed to close — a system meant not just to retrieve information, but to think in the logic of policy analysis and social inquiry.
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Always open to conversations on risk governance, sociotechnical systems, and disaster resilience — or anything that treats social complexity with the rigor it deserves.