Prof Hywel Williams
Professor of Environmental Data Science
Telephone: 01392 723777
Extension: (Streatham) 3777
Hywel is a computational scientist focused on problems that link social processes and environmental change. He is a faculty member in Computer Science, and affiliated to the Institute for Data Science & Artificial Intelligence and the Global Systems Institute, at University of Exeter. He is a Fellow of the Alan Turing Institute, the UK's premier facility for artificial intelligence and data science.
Hywel leads the SEDAlab, an active research group of postdoctoral fellows and PhD students, with diverse interests in computational social science and environmental data analysis. He is Director of the UKRI CDT in Environmental Intelligence and leads the Environmental Intelligence Research Network at Exeter. He has previously been Programme Lead for MSc Data Science and related programmes. He teaches courses and supervises student projects in data science and social network analysis. He has published >70 research papers in leading outlets (see Google Scholar here) and his research has been funded by EPSRC, ESRC, NERC, HEFCE, Leverhulme Trust and several commercial and philanthropic sponsors, amongst others.
Hywel's research career has applied complex systems thinking and computational methods to problems in social sciences, environmental science, evolutionary ecology and artificial intelligence. This interdisciplinary mix is unified by a methodological focus on simulation, network analysis and machine learning. Hywel received his PhD in Complex Systems from University of Leeds in 2006. Since then he has worked in the departments of Environmental Science and Computer Science at University of East Anglia, before moving to University of Exeter in 2011 to work first in Biosciences (2011-2017) and then Computer Science. In 2019 he was promoted to Associate Professor in Data Science. In 2022 he was promoted to Professor in Environmental Data Science.
Current research interests focus primarily on the analysis of complex data from the Web and social media, with a particular emphasis on environmental issues. This work includes network analysis and text mining to understand online political and environmental debates, exploring the dynamics of online media and news consumption, and using Web data to track the social impacts of natural hazards.