Good health and well-being are crucial for human survival, yet in the current world, public health and well-being are facing diverse pressures, including environmental pollution, climate change, and epidemics. My primary research revolves around understanding and predicting public health concerns in urban contexts from the viewpoint of environmental exposures, urban planning, and health system perspectives by applying diverse methodological approaches, including quantitative geographical information science, data science, and health impact assessment. The core of my work contributes theoretically to the fields of health geography, urban planning, and environmental epidemiology, with a critical emphasis on investigating spatial and societal disparities.
Currently, my research focuses on three tracks: (i) Exploring public health in diverse urban contexts encompassing cities in the global north and south; (ii) Developing and applying geographical methods and tools in studying health and modeling health impacts; (iii) Integrating data-driven methods and artificial intelligence models (e.g., Deep learning- image analysis, machine learning) in studying complex environmental, and social aspects of health using new forms of data.
Most of his research strives to follow open science protocol and encourage FAIR data and software usage. In several ongoing projects (more in the research tab), I collaborate with national and international researchers worldwide to generate groundbreaking insights on public health, health system analysis, and urban planning for healthy cities, combining multi and transdisciplinary research approaches. Over the years, I have published diverse scientific articles in various reputed journals and conferences and presented my work as invited talks. Additionally, I have created several open-source data analysis toolkits as part of my research lab "Spatial Data Science and Geo-Intelligence" outputs (e.g., R-package, Python toolkits).
Beyond research, teaching holds a pivotal role in my academic journey. I enjoy engaging with diverse students from various backgrounds at different levels of study. Currently, my teaching pedagogy generally follows active learning strategies, including problem-based learning. I coordinate undergraduate Health Geography course and co-coordinate Spatial statistics and machine learning courses in the applied data science master program. I am currently supervising and co-supervising two PhD students and two postdocs. I supervised over 10+ MSc students.
Current PhD Students
- Martina Bubalo, ECO-MIND project at Utrecht University, Role: daily primary supervisor, co-supervisors: Dr Karlijn van den Broek, Dr March Helbich
- Corin Staves, JIBE Project at University of Cambridge, Role: co-supervisor; other supervisors: Prof James Woodcock (University of Cambridge), Dr Rolf Moeckel (Technical University of Munich)
Future Students
I am always interested in hearing from motivated potential Ph.D. students willing to conduct research around my research tracks, as noted above. Please reach out to me by email with your CV and initial project ideas.
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