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Staff Profile

Tom Komar

Machine Learning Operations Engineer

Background

I am an interdisciplinary researcher working across urban studies, environmental systems, and data-informed decision support. My research focuses on how people, infrastructure, and services interact across cities and regions, with attention to both everyday patterns and periods of disruption. I am interested in making complex systems more visible and more understandable so that evidence can better support planning, management, and public decision-making.

My work spans several connected themes, including urban mobility, the use and functioning of public spaces, environmental quality, the reliability of spatial information, and the integration of data for practical action. Across these areas, I focus on how continuous and fragmented observations can be turned into useful insight for monitoring, planning, and policy. I try to balance applied relevance with interpretation, validation, and an awareness of the limits of data.

I value research that is rigorous, useful, and grounded in real-world conditions. Much of my work sits between technical solutions, operations, and policy, where good analysis needs to remain sensitive to context, uncertainty, and public value. My aim is to contribute to more informed, responsive, and evidence-based approaches to urban and environmental challenges.

Publications