Professor João Porto de Albuquerque is Director of the Institute of Global Sustainable Development at University of Warwick, and a Turing Fellow at The Alan Turing Institute. He is a geographer and computer scientist with an interdisciplinary background and works in the fields of Digital Geography, Geographic Information Science and Global Sustainable Development. He develops innovative transdisciplinary research methods to improve our understanding of sociotechnical urban environments, particularly in the global South, with a view to produce transformations to sustainable development. His trandisciplinary research approach draws on social research, geospatial data science and geocomputational methods alongside methodological innovations to enable research co-production with non-academic stakeholders.
João is currently leading a research programme on how to empower vulnerable and deprived communities in the global South to produce citizen-generated data and improve resilience to health and environmental risks. He has secured competitive research funds to implement this research programme in excess of £8.5m (£2.5m as PI) from diverse national and international funding bodies, including major grants from diverse funding agencies (e.g. Global Challenges Research Fund, ESRC, EPSRC, Belmont Forum, NIHR, FAPESP, CAPES) in collaboration with academic and non-academic partners in several countries, including Brazil, Bangladesh, Colombia, Germany, Kenya, Nigeria, Pakistan, Poland, Romania, Sweden, and the United States.
At the Turing, Dr João Porto de Albuquerque will be dedicated to advance interdisciplinary methods in sociospatial data science with applications to improving urban sustainable development, with a particular focus on urban resilience and health. His approach bridges computational methods (machine learning, geocomputation and spatial data analysis) and social research in the fields of social computing, urban geography and critical data studies. He is currently working on the development of new methods to generate and visualize spatial data on human settlements needed to model urban development, combining citizen participation with spatial data analysis, these methods will improve understanding of urban spatial patterns and intra-urban inequalities.