Professor Ewa Luger is codirector of ‘Bridging Responsible AI Divides’ (BRAID), a national Arts and Humanities Research Council (AHRC) programme, integrating arts and humanities knowledge into responsible AI practice. She also codirects the Responsible Natural Language Processing (NLP) Centre for Doctoral Training. Ewa regularly advises industry, government and NGOs on matters of responsibility and usability, and is a member of both the DCMS college of experts, and the leadership council for the Future of Privacy Forum’s Centre for Artificial Intelligence. Her research explores social, ethical and interactional issues in the context of complex data-driven systems, such as AI, with a particular interest in media, the distribution of power, spheres of exclusion and consent.

Previously a fellow of the Alan Turing Institute, Researcher at Microsoft, Fellow of Corpus Christi College (University of Cambridge), and consultant ethicist for Microsoft Research (2016-2020), she also builds upon 15 years as digital inclusion expert and practitioner (NIACE, 1999-2014). Her recent book What do we Know and What Should we do About AI? (2023) is specifically targeted at non-academic audiences unfamiliar with current debates surrounding AI.

Gabriela Ochoa is a Professor of Computing Science at the University of Stirling in Scotland, UK. Her research lies in the foundations and methods of evolutionary algorithms and metaheuristics, with an emphasis on fitness landscape analysis, Gray-box optimisation, autonomous search, and cross-discipline applications in healthcare. She holds a PhD from the University of Sussex, UK, and has worked at the University Simon Bolivar, Venezuela, and the University of Nottingham, UK, before joining Stirling. Her Google Scholar h-index is 47 and her publications have gathered over 10,300 citations. Her work has been recognised with 7 best-paper awards and 12 other nominations. She was instrumental in creating and popularising the concepts of local optima networks (LONs) and search trajectory networks (STNs). She was the EiC for the Genetic and Evolutionary Computation Conference (GECCO) in 2017 and 2025; and is part of the editorial board of the Evolutionary Computation Journal (ECJ) and the ACM Transactions on Evolutionary Learning and Optimisation (TELO). She is a member of the executive boards of the ACM interest group in evolutionary computation (SIGEVO), where she edits the SIGEVOlution newsletter, and the SPECIES society. In 2020, she was recognised by European event on bio-inspired algorithms, EvoStar, for her outstanding contributions to the field.

Search Trajectories Illuminated

Many nature-inspired optimisation algorithms have been proposed over the years. It is unclear, however, to what extent recent algorithms are “new”, or how and why to select one of them to solve a given task. Search trajectory networks (STNs) are e a data-driven, graph-based modelling tool to analyse, visualise and contrast the behaviour of different types of optimisation algorithms. STNs offer a visual and intuitive fresh perspective to explain and interpret search and optimisation. This talk overviews our methodology including recent developments: applications to neuroevolution, multi-objective optimisation, STNWeb, and the use of generative AI to automate the analysis.

Martyn Amos is Professor of Computational Science and the interim Deputy Pro Vice-Chancellor for Research and Knowledge Exchange in the Faculty of Science and Environment at Northumbria University, UK. His research focuses on the power of collectives, from molecules in a test-tube, to living cells, social insects, and people in a crowd. He was one of the founders of the field of DNA computing, and his popular science book Genesis Machines described its early history. He gained his Ph.D. in 1997 (Warwick), was then awarded a Leverhulme Early Career Fellowship, and has held academic posts at Liverpool, Exeter, and Manchester Metropolitan. He is a Fellow of the British Computer Society.

Crowd-sourced insights into models of crowds

More than 60% of the world’s population will live in cities by 2030. The urban metabolism is a complex system in which human crowds play an integral role, and agent-based simulations are often used to understand the movement and collective behaviour of large numbers of people navigating through their daily lives. However, these simulations are often dominated by a physics-based model that excludes the nuances and quirks of real human behaviour. In this talk, I will describe our efforts to identify characteristics of real human crowds that are excluded from many models, in the hope that we can make future simulations more realistic. Using a “Turing test” approach with human participants, we uncovered some surprising insights into how people perceive crowds, and identified signature feature of real crowds that are often missing from their digital twins.