Prof Hywel Williams
Associate Professor in 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 SEDA lab, an active research group of postdoctoral fellows and PhD students, with diverse interests in computational social science and environmental data analysis. He is Deputy Director of the UKRI CDT in Environmental Intelligence. He is also 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 >60 research papers in leading outlets and his research has been funded by EPSRC, ESRC, NERC, HEFCE, Leverhulme Trust and several commercial sponsors, amongst others.
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.
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.
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, modelling/predicting collective attention in social media, understanding social biases in online news consumption, and using Web data to track natural hazards.