Michael Epitropakis is a Lecturer in Foundations of Data Science at Lancaster University. His research focuses on developing and analyzing novel stochastic search methodologies for solving complex real-life problems, that lie at the interface of Operational Research and Computer Science. These methods are interdisciplinary in character, adopting theory and practical methodologies from various scientific fields such as artificial intelligence, machine learning, and statistics. His work addresses both continuous (i.e. global) and discrete (i.e. combinatorial) problems. He is currently interested in applications of optimization to software engineering and to transportation. He has published more than 30 journal and conference papers. His research has been funded from both private and governmental funding agencies, including IEEE, Microsoft, European Union (ESF) and the Greek state research council.
Jo Knight is a Reader within the CHICAS research group in Lancaster Medical School and is theme lead for Health. Dr. Jo Knight undertook her PhD at Queen Mary University of London and two fellowships at Kings College London. She moved to Canada in 2012 to start a Statistical Genetics group at the Centre of Addiction and Mental Health in Toronto. She is involved in the psychiatric genomics consortium and many other international collaborations. She has experience in developing new methods for analyzing genetic data as well as experience in applying known techniques to a large variety of datasets. She has published in journals including Nature and Nature Genetics and has funding from organizations including the National Institutes of Health (NIH) and the Canadian Institute of Health Research (CIHR).View full profile
Bran Knowles is a Lecturer in Data Science, focusing on trust, privacy and ethical considerations surrounding data and data systems. Her background is multidisciplinary, spanning the psychology, sociology, anthropology, and design, and her PhD is in Digital Innovation. Her research explores different aspects of trust through ethnographic case studies, develops conceptual models of trust that help in understanding a research agenda for developing trusted data systems, and develops practitioner guidelines for creating trusted data systems. She approaches the development of socio-technical systems from a human perspective, applying an understanding of how people come to trust one another in the real world towards understanding how to design systems that people trust.
Amber Leeson is an Environmental Scientist and her main research interest is in climate change and the cryosphere (the frozen regions of our planet). She is particularly motivated to study the response of the Greenland and Antarctic Ice Sheets to global warming because of the leverage they posses to affect global sea level should they shrink or grow. Amber's work uses innovative geophysical modelling methods and novel ways of analysing in-situ and satellite observations to improve our understanding of ice-climate interactions and to make better predictions of future change.
Adam Sykulski is a Lecturer in Data Science, based at both the Data Science Institute and the Department of Mathematics and Statistics at Lancaster University. Adam's research focuses on the development of novel statistical methods for large-scale multivariate time series and spatiotemporal data. Much of this work is motivated by, and feeds back into, oceanographic data such as the Global Drifter Program, a large global database of satellite-tracked freely drifting instruments, maintained and managed by NOAA (the National Oceanic and Atmospheric Administration in the US). Adam was previously based at University College London, where he held a Marie Curie International Fellowship, a joint position with NorthWest Research Associates in Seattle, USA.