Senior Lecturer, University of Exeter
Ozgur Akman has a BSc in Mathematics and a MSc in Bioengineering from Imperial College London, and a PhD in Mathematics from the University of Manchester. His research interests lie in the interface between applied mathematics, computer science and biology, focusing on the development of computational methods to systematically construct and analyse quantitative models of biochemical and neural networks. His earlier research used nonlinear dynamics techniques to identify the molecular mechanisms underlying the development of neurobiological motor disorders. A particular recent area of interest is the use of evolutionary computing methods to optimise large-scale systems biology models to experimental time series data. This important real-word optimisation problem is characterised by intrinsic uncertainty in the design space - due to the potential presence of multiple optima yielding similar fitness scores - and also in the objective space - due to experimental noise.
Lecturer, University of Exeter
Khulood Alyahya is a lecturer in the Computer Science department at the University of Exeter. Prior to that, she was a research fellow at the same department working on a multidisciplinary project addressing key challenges in optimisation problems in the field of Computational Systems Biology. She has a PhD degree in Computer Science and an MSc degree in Intelligent Systems Engineering from the University of Birmingham where she was awarded the best student prize. Her main research interests include landscape analysis and optimisation under multiple sources of uncertainty with medical and biological applications.
Professor of Operational Research & Systems, University of Warwick
Juergen Branke has been active in the area of evolutionary algorithms applied to problems involving uncertainty for over 20 years, including problems that are dynamically changing over time, problems where the evaluation is uncertain (as in simulation-based optimisation), the search for robust solutions or uncertainty about user preferences. Juergen has published over 170 peer-reviewed papers in international peer-reviewed journals and conferences.
He is Area Editor of the Journal of Heuristics and the Journal on Multi-Criteria Decision Analysis, as well as Associate Editor of IEEE Transactions on Evolutionary Computation and the Evolutionary Computation Journal. He is also secretary of the ACM Special Interest Group on Evolutionary Computation (SIGEvo).
Professor in Computational Intelligence, University of Exeter
Jonathan Fieldsend has a degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has over 80 publications in the evolutionary computation and machine learning domains, and has been working on uncertain problems in evolutionary computation since 2005. This strand of work has mainly in multi-objective data-driven problems, although more recently has undertaken work on expensive uni-objective robust problems. He is a vice-chair of the IEEE Computational Intelligence Society (CIS) Task Force on Data-Driven Evolutionary Optimisation of Expensive Problems, and sits on the IEEE CIS Task Force on Multi-modal Optimisation and the IEEE CIS Task Force on Evolutionary Many-Objective Optimisation. He has also been a co-organiser of the VizGEC and SAEOpt workshops at GECCO for a number of years.
Research Fellow, University of Exeter
Kevin Doherty has a degree in Mathematics and an MSc and PhD in Applied Mathematics from the National University of Ireland, Galway. Following the award of his PhD in 2014, he held a position as a post doctoral researcher in the Nutrition Physiology and Ingestive Behaviour (PNCA) lab, a joint research group operated between the institutions of INRA and AgroParisTech, before moving to Exeter in 2016. He is interested in the mathematical modelling of biological systems and, more recently, is especially interested in applying evolutionary computation to the problem of parameter estimation of such models.