Organisers of the ECXAI Workshop Bios

John McCall

John McCall is Head of Research for the National Subsea Centre at Robert Gordon University. He has researched in machine learning, search and optimisation for 25 years, making novel contributions to a range of nature-inspired optimisation algorithms and predictive machine learning methods, including EDA, PSO, ACO and GA. He has 150+ peer-reviewed publications in books, international journals and conferences. These have received over 2400 citations with an h-index of 22. John and his research team specialise in industrially-applied optimization and decision support, working with major international companies including BT, BP, EDF, CNOOC and Equinor as well as a diverse range of SMEs. Major application areas for this research are: vehicle logistics, fleet planning and transport systems modelling; predictive modelling and maintenance in energy systems; and decision support in industrial operations management. John and his team attract direct industrial funding as well as grants from UK and European research funding councils and technology centres. John is a founding director and CEO of Celerum, which specialises in freight logistics. He is also a founding director and CTO of PlanSea Solutions, which focuses on marine logistics planning. John has served as a member of the IEEE Evolutionary Computing Technical Committee, an Associate Editor of IEEE Computational Intelligence Magazine and the IEEE Systems, Man and Cybernetics Journal, and he is currently an Editorial Board member for the journal Complex And Intelligent Systems. He frequently organises workshops and special sessions at leading international conferences, including several GECCO workshops in recent years.

Jaume Bacardit

Jaume Bacardit Jaume Bacardit is Reader in Machine Learning at Newcastle University in the UK. He has receiveda BEng, MEng in Computer Engineering and a PhD in Computer Science from Ramon Llull University, Spain in 1998, 2000 and 2004, respectively. Bacardit’s research interests include the development of machine learning methods for large-scale problems, the design of techniques to extract knowledge and improve the interpretability of machine learning algorithms, known currently as Explainable AI, and the application of these methods to a broad range of problems, mostly in biomedical domains. He leads/has led the data analytics efforts of several large interdisciplinary consortiums: D-BOARD (EU FP7, €6M, focusing on biomarker identification), APPROACH (EI-IMI €15M, focusing on disease phenotype identification) and PORTABOLOMICS (UK EPSRC £4.3M focusing on synthetic biology). Within GECCO he has organised several workshops (IWLCS 2007-2010, ECBDL’14), been co-chair of the EML track in 2009, 2013, 2014, 2020 and 2021, and Workshops co-chair in 2010 and 2011. He has 90+ peer-reviewed publications that have attracted 4600+ citations and a H-index of 31 (Google Scholar).

Alexander Brownlee

Alexander (Sandy) Brownlee is a Lecturer in the Division of Computing Science and Mathematics at the University of Stirling. His main topics of interest are in search-based optimisation methods and machine learning, with a focus on decision support tools, and applications in civil engineering, transportation and software engineering. He has published over 70 peer-reviewed papers on these topics. He has worked with several leading businesses including BT, KLM, and IES on industrial applications of optimisation and machine learning. He serves as a reviewer for several journals and conferences in evolutionary computation, civil engineering and transportation, and is currently an Editorial Board member for the journal Complex And Intelligent Systems. He has been an organiser of several workshops and tutorials at GECCO, CEC and PPSN on genetic improvement of software.

Stefano Cagnoni

Stefano Cagnoni graduated in electronic engineering and received a Ph.D. in bioengineering from the University of Florence. In 1994, he was a visiting scientist at the Whitaker College Biomedical Imaging and Computation Laboratory at MIT. Since 1997, he has been with the University of Parma, where he has been an Associate Professor since 2004. His main research interests are in the field of soft computing, with particular regard to evolutionary computation, with applications to problems in pattern recognition, image processing, complex system analysis, and natural language processing. Awardee of a grant from the EU Marie Sklodowska Curie Actions for a four-year ITN in “Medical Imaging using Bio-Inspired and Soft Computing.” (2009-2013). He is currently participating in two research projects financed under the Next Generation EU program. One deals with digital lifelong prevention (DARE), and the other with food analysis by innovative instrumentation and AI (METROFOOD). From 2005 to 2020, he co-chaired MedGEC, a workshop on medical applications of evolutionary computation held at GECCO, and co-edited several special issues on “Genetic and Evolutionary Computation for Image Analysis, Signal Processing, and Pattern Recognition” in international journals. Since 2022, he has co-chaired the ECXAI (“Evolutionary Computation for Explainable AI”) workshop at GECCO. Editor-in-chief of the “Journal of Artificial Evolution and Applications” (2007-2009). He received the “Evostar 2009 Award” from SPECIES for his outstanding contribution to Evolutionary Computation.

Giovanni Iacca

Giovanni Iacca is an Associate Professor in Information Engineering at the Department of Information Engineering and Computer Science of the University of Trento, Italy, where he founded the Distributed Intelligence and Optimization Lab (DIOL). Previously, he worked as a postdoctoral researcher in Germany (RWTH Aachen, 2017-2018), Switzerland (University of Lausanne and EPFL, 2013-2016), and The Netherlands (INCAS3, 2012-2016), as well as in industry in the areas of software engineering and industrial automation. He is co-PI of the PATHFINDER-CHALLENGE project “SUSTAIN” (2022-2026). Previously, he was co-PI of the FET-Open project “PHOENIX” (2015-2019). He has received two best paper awards (EvoApps 2017 and UKCI 2012). His research focuses on computational intelligence, distributed systems, explainable AI, and analysis of biomedical data. In these fields, he co-authored more than 150 peer-reviewed publications. He is actively involved in organizing tracks and workshops at some of the top conferences on computational intelligence, and he regularly serves as a reviewer for several journals and conference committees. He is an Associate Editor for IEEE Transactions on Evolutionary Computation, Applied Soft Computing, and Frontiers in Robotics and AI.

David Walker

David Walker is a Lecturer in Computer Science at the University of Plymouth. He obtained a PhD in Computer Science in 2013 for work on visualising solution sets in many-objective optimisation. His research focuses on developing new approaches to solving hard optimisation problems with Evolutionary Algorithms (EAs), as well as identifying ways in which the use of Evolutionary Computation can be expanded within industry, and he has published journal papers in all of these areas. His recent work considers the visualisation of algorithm operation, providing a mechanism for visualising algorithm performance to simplify the selection of EA parameters. While working as a postdoctoral research associate at the University of Exeter his work involved the development of hyper-heuristics and, more recently, investigating the use of interactive EAs in the water industry. Since joining Plymouth Dr Walker’s research group includes a number of PhD students working on optimisation and machine learning projects. He is active in the EC field, having run an annual workshop on visualisation within EC at GECCO since 2012 in addition to his work as a reviewer for journals such as IEEE Transactions on Evolutionary Computation, Applied Soft Computing, and the Journal of Hydroinformatics. He is a member of the IEEE Taskforce on Many-objective Optimisation. At the University of Plymouth he is a member of both the Centre for Robotics and Neural Systems (CRNS) and the Centre for Secure Communications and Networking.