Ryan Heuser is an Assistant Professor in Digital Humanities at the University of Cambridge, specializing in computational approaches to literary and intellectual history, prosody, and artificial intelligence's impact on language. His work bridges data science, machine learning, and literary studies through digital methodologies. Doctoral training in Eighteenth-Century British Literature, Stanford University (2019) Founding member & Associate Research Director, Stanford Literary Lab Junior Research Fellow, King’s College Cambridge (2019-2022) Research interests span computational modeling of semantic revolutions, large-scale literary field analysis, and the intersection of digital methods with historical and intellectual studies. His book Explorations in the Digital History of Ideas (2023) co-edited with Peter de Bolla exemplifies this approach. Recent publications focus on historical semantics, metrical analysis, and digital mapping of emotions in literature, reflecting his interdisciplinary expertise in natural language processing, network theory, and literary data visualization. Currently leads teaching initiatives at Cambridge Digital Humanities and contributes to computational projects exploring textual rhythms and large language models.
Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.
Erik Ketzan is a Lecturer in Digital Humanities and Cultural Computation at King's College London. He holds a PhD in English/Digital Humanities from Birkbeck, University of London, and has held postdoctoral roles at the University of Cologne and Trinity College Dublin, where he coordinated the MA in Digital Humanities and Culture. His research focuses on computational literary studies, legal/ethical issues in digital research, and corpus stylistics. He has published widely on topics such as Thomas Pynchon’s computational stylistics, legal frameworks for digital humanities research, and gender bias in historical texts. His work includes developing tools like Coleto for textual variant analysis and co-authoring the book *Thomas Pynchon and the Digital Humanities* (2022). Ketzan actively engages in policy advocacy, advising on copyright and data protection in DH, and has contributed to projects like CLARIN and the Leibniz Institute for the German Language. Education: PhD in English/Digital Humanities, Birkbeck, University of London Postdoctoral Researcher, University of Cologne (EncycNet Project) Postdoctoral Research and Teaching Fellow, Trinity College Dublin Research Interests: Computational approaches to literary analysis, legal challenges in data-driven research, corpus stylistics, and gender studies in historical corpora. Teaching: Courses include Python for the humanities, computational literary studies, and legal issues in digital humanities.
Andrew Zisserman is a Royal Society Research Professor at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). His research focuses on computer vision, artificial intelligence, and neural networks, with significant contributions to multimodal learning, video understanding, and 3D scene analysis. He leads projects exploring visual-language models, audio-visual synchronization, and clinical imaging applications. Key research areas include: Video analysis and temporal modeling Multimodal systems for sign language translation and action recognition 3D shape estimation and physical property inference Foundation models and cross-modal retrieval Recent work highlights: Developed Flamingo and Tapir models for video-language tasks Advancements in spinal MRI analysis and clinical imaging Leadership in EGO4D and VoxCeleb challenges Honors include Fellowship of the Royal Society (FRS) and the ISSLS Prize in Clinical Science 2023 for spinal analysis innovations. His lab collaborates globally, emphasizing real-world applications in healthcare and autonomous systems.
Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML
David Stillwell serves as Professor of Computational Social Science at Cambridge Judge Business School and Academic Director of The Psychometrics Centre, University of Cambridge. His research leverages big data to understand human psychology and behavior, with significant contributions in personality prediction from digital footprints and personalized advertising applications. His educational background includes: BSc in Psychology from the University of Nottingham (2007) MSc in Research Methods from the University of Nottingham (2008) PhD in Decision Making from the University of Nottingham (2012) Dr. Stillwell's research centers on computational social science and psychometrics, pioneering the myPersonality Facebook application that collected data from over 6 million users. His work demonstrates computers can predict personality as accurately as spouses, reveals psychological targeting's advertising effectiveness, and establishes links between personality-matched spending and life satisfaction. He also explores linguistic honesty markers through profanity analysis and personality-based dating patterns. His recent publications (2019-2025) reveal a strong trajectory toward AI evaluation using psychometric frameworks, particularly in medical and general-purpose AI assessment. Key themes include fairness metrics in language models, crisis emotional responses through social media, and computational personality recognition - demonstrating consistent innovation at the psychology-technology intersection. He has received significant recognition: Top 10 most influential papers of 2013 by Altmetric Nieman Journalism Lab highlight of 2013 Named in 'top 30 thinkers under 30' by Pacific Standard Magazine Dr. Stillwell maintains active industry engagement through consultancy with Barclays, Hilton, and Ubisoft on projects spanning computer-adaptive testing systems to interactive experiences like Predictive World for Watch Dogs 2. His policy impact is substantial, with citations by the European Data Protection Supervisor, World Bank, and multiple national governments, leading to speaking engagements at the European Parliament and Bank of England. As Academic Director of The Psychometrics Centre, he leads a global hub for advancing psychological assessment through innovative methods including Concerto open-source software, collaborating with organizations ranging from the European Commission to major corporations on psychometric applications in people analytics and digital behavior.
Almut Sophia Koepke is a junior research group leader at the Technical University of Munich and University of Tübingen, focusing on multimodal learning problems integrating sound, vision, and text. Her work bridges foundational research in audio-visual understanding with practical applications in few-shot learning, zero-shot translation, and cross-modal attention mechanisms.
Dr. Oana Cocarascu is a Senior Lecturer in Artificial Intelligence at the Department of Informatics, Faculty of Natural, Mathematical & Engineering Sciences, King's College London. She holds a PhD and MEng in Computing (Artificial Intelligence) from Imperial College London and conducts applied research focusing on how artificial intelligence can be deployed to support real-world applications, with machine learning and natural language processing as core components of her work. Her research interests span argument mining, explainable AI, machine learning, natural language processing, and symbolic reasoning. She is particularly focused on developing AI systems that can provide transparent, accountable, and ethical decision-making processes. Her work addresses critical challenges in bias mitigation, fairness metrics, fact verification systems, and argumentation-based explanations for complex AI decisions. Analysis of her recent publications (2023-2025) reveals a strong focus on fairness in AI systems, with particular attention to individual fairness metrics and nuanced evaluation frameworks. She has made significant contributions to fact verification systems, especially in multimodal contexts involving charts and tabular data. Her work increasingly integrates argumentation theory with natural language processing to create explainable AI systems that can justify their decisions through structured reasoning. Dr. Cocarascu leads an EPSRC-funded project titled 'A framework for evaluating and explaining the robustness of NLP models' (2024-2027) and is actively involved in the Natural Language Processing Group at KCL. Her research fingerprint shows strong activity in argumentation (100%), decision-making (74%), artificial intelligence (50%), explainable AI (39%), and bias mitigation (35%). She has contributed to numerous high-impact publications in top venues including ACL, EMNLP, AAAI, and the Journal of Artificial Intelligence Research, with a particular focus on making AI systems more transparent, accountable, and aligned with human values. Her work intersects with UN Sustainable Development Goals, particularly those related to reducing inequality and building resilient infrastructure.
Timothy M. Hospedales is a Professor of Artificial Intelligence at the Institute of Perception, Action and Behaviour within the School of Informatics at the University of Edinburgh . He also serves as VP AI and Head of Samsung AI Research Centre Europe . His research focuses on efficient and robust AI , emphasizing meta-learning , lifelong transfer-learning , and domain adaptation in both probabilistic and deep learning frameworks. Applications span computer vision , vision and language , reinforcement learning for robotics , and finance . Professor at University of Edinburgh (2020–present) ELLIS Fellow (2021) Head of Samsung AI Research Europe (2020–present) Founding Director of Applied Machine Learning Lab at QMUL (2012–2016) His work includes pioneering contributions to meta-learning , few-shot learning , and self-supervised methods , with notable awards such as the Best Paper Prize at ICML AutoML 2018 and Best Student Paper at ICPR 2018 . He has co-authored 15+ recent papers on topics like Vision-Language Models , Medical AI Fairness , and Diffusion Model Optimization . He served as Program Co-Chair for BMVC 2018 and AAAI 2022 , and authored a book on Visual Adaptation in the Deep Learning Era (2022). Co-Chair, BMVC 2018 Guest Editor, IET CV Special Issue (2016) Keynote Speaker at TASK-CV Workshop (ECCV 2016) Special Issue on Fewer Labels (IEEE PAMI 2020) His leadership extends to organizing workshops like the Learning-to-Learn Workshop at ICLR 2021 , Meta-Learning Workshop at NeurIPS 2020 , and Domain Generalisation Workshop at ICLR 2023 . Current projects include Meta-Omnium (CVPR 2023) for general-purpose meta-learning and MetaAudio (ICANN 2022) for few-shot audio classification benchmarks.
Johnny Golding is a Professor at the Royal College of Art (RCA), School of Arts & Humanities. His career spans roles such as Director of the Centre for Fine Art Research at Birmingham City University, Professor of Philosophy in the Visual Arts & Communication Technologies (2000–2012), and honorary Professorship in Philosophy and Imaging at Dundee School of Art (2009). Born in New York, he holds a PhD from the Universities of Toronto and Cambridge. Education : - PhD in Philosophy from the Universities of Toronto and Cambridge. - Earlier studies in Toronto and New York. Research Interests : Golding’s work explores post-Newtonian analytics, new materialisms, and the erotics of sense as 'radical matter.' His interdisciplinary approach bridges art, philosophy, and 'wild sciences' like AI, robotics, and quantum theory. Key themes include data proliferation's societal impact, emergent systems, and the interplay between art and technology. Recent Articles & Projects : Focus on Data Loam (2020), exploring future knowledge systems through art and science collaborations. Recent works address radical empathy, distributed intelligence, and the ethics of AI. His 2018 opera Entanglement: The Opera and installations like Of The Thick and the Raw exemplify his practice-led philosophy. Grants & Leadership : - Led the Data Loam project (FWF-PEEK funded), involving over 20 artists and scholars. - PI for AiDesign Labs on generative AI at RCA. Labs & Collaborations : - Radical Matter Lab (2017–2018), a pedagogical experiment at RCA. - Collaborations with institutions like the University of Applied Arts Vienna and the British Library.
London School of Economics and Political Science (LSE)United Kingdom
Dr Alison Powell is an Associate Professor in the Department of Media and Communications at the London School of Economics and Political Science (LSE), serving as Programme Director for the MSc Media and Communications (Data and Society). Her research focuses on technology design, public sector technology, and digital futures. She is known for her book Undoing Optimization: Civic Action in Smart Cities (2021) and her participatory research method, data walking. From 2019–2023, she directed the JUST AI network, supported by the Ada Lovelace Institute and UKRI, advancing AI ethics research. Her work integrates critical theory, participatory methods, and interdisciplinary collaboration. Education: PhD in Communication from Concordia University (Canada), MA in Media Studies from Toronto Metropolitan University, and postdoctoral research at the Oxford Internet Institute. Prior to academia, she worked in the film and TV industry. Research interests include civic engagement in data-driven governance, ethical AI, and participatory urbanism. She has advised projects like Africa Just AI and Operationalising Ethics for AI, funded by Horizon Europe, AHRC, and the Open Society Foundations. Her articles address topics such as AI governance, urban light ecology, feminist research praxis, and the ethical implications of technology for marginalized communities. She contributes to blogs like Media@LSE and leads initiatives at Polis and the LSE’s Digital Futures for Children project. Grants and collaborations include work with Projects by IF, Open Rights Group, and the Human Data Interactions Network (EPSRC). She explores intersections of technology, justice, and sustainability, emphasizing equitable and inclusive innovation.
Dr Andrew Rhead is a Senior Lecturer in the Department of Mechanical Engineering at the University of Bath, specializing in aerospace composites and damage tolerance analysis. His research focuses on impact damage detection, failure mechanism modeling, and Non-Destructive Evaluation (NDE) techniques for composite structures. MSci in Mathematical Sciences (Dynamical Systems) - University of Bristol (2006) PhD in Composite Damage Tolerance - University of Bath (2009) His work develops computationally efficient analytical models for compression after impact (CAI) strength prediction in composite laminates, surpassing traditional finite element methods. Key projects include hydrogen storage systems for aircraft, cryogenic composite testing, and steered fiber manufacturing optimization. Active in 10 projects including ASPIRE and HyFIVE Collaborates with Airbus, GKN Aerospace, and EPSRC Research trends show emphasis on sustainable aviation materials, structural battery integration, and advanced testing methodologies. Current affiliations include the Institute for Mathematical Innovation (IMI) and Centre for Integrated Materials, Processes & Structures (IMPS).
Dr. Mo Adda is a Principal Lecturer at the University of Portsmouth's School of Computing, part of the Faculty of Technology. He holds a PhD in Distributed Systems and Parallel Processing from the University of Surrey. His research focuses on network security, distributed systems, wireless networks, and cybercrime prevention. He leads projects in the Centre for Cybercrime and Economic Crime, exploring fault management in networks, blockchain applications, and IoT forensics. With 16 supervised theses, he advises on topics like energy-efficient cloud systems and machine learning for environmental modeling. His work bridges academia and industry, addressing challenges in software-defined networks, traffic control, and secure data sharing in social networks. Education: PhD in Distributed Systems (University of Surrey) Affiliations: Centre of Excellence in Defence, Risk & Resilience; Portsmouth Centre for Advanced Materials and Manufacturing Research Interests Dr. Adda's research spans: Network security and fault detection mechanisms Blockchain applications in IoT and forensics Energy-efficient cloud data center optimization Machine learning for climate modeling Self-organizing network protocols Grants & Collaborations His projects include collaborations with industry partners on secure data leakage detection in cloud systems and resilient wireless protocols for harsh environments. He has pioneered fault classification systems using clustering algorithms and fuzzy logic. Labs & Teams He contributes to the Centre for Cybercrime and Economic Crime, focusing on digital forensics and network intrusion analysis. His team develops frameworks for privacy management in social networks and proactive routing in software-defined networks.
Dr Mobarak Hoque serves as a Senior Lecturer in the Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester. He concurrently holds an external position as Honorary Senior Research Fellow at University College London (UCL) from July 2025 to June 2028. Academic Background: Doctor of Philosophy in Representation Learning in Multimodal Spatiotemporal Image-Guided Medical Procedures, National University of Singapore (NUS), awarded 2019 Research Expertise: Dr Hoque's work centers on Biomedical Data Science and Artificial Intelligence with specialization in Multimodal Large Language Models , Vision-Language Models , and Safe and Responsible AI . His research directly addresses UN Sustainable Development Goal 3 (Good Health and Well-being) through innovations in surgical robotics, medical imaging, and endoscopic procedures including polyp segmentation and low-light enhancement. Publication Trends: Recent publications (2023-2025) reveal a cohesive research trajectory focused on robust AI for surgical applications. Key methodologies include adversarial feature disentanglement, semi-supervised learning frameworks, and multimodal integration of vision-language models. His work consistently targets real-world clinical challenges like instrument segmentation, surgical workflow recognition, and low-light endoscopy enhancement using advanced techniques such as wavelet attention and diffusion models. Academic Supervision: Dr Hoque actively mentors PhD candidates in Biomedical Data Science and AI, particularly welcoming students interested in multimodal learning and responsible AI deployment for healthcare applications.
Dr Haitao Shi is a Researcher affiliated with the School of Social and Political Science at the University of Edinburgh. His research focuses on criminal justice systems, drug policing in China, and the intersection of technology with crime. He employs advanced research methods including quantitative tools (R, Python, SPSS), qualitative software (Nvivo, MAXQDA), and data visualization techniques (Gephi, QGIS). His current projects include studying Chinese students' experiences at the University of Edinburgh and analyzing rhizomatic networks in online drug trade funded by the Polish Ministry of Science. Haitao's work emphasizes cross-cultural comparisons, particularly in drug trade dynamics across Finnish, Polish, and English-speaking contexts. He explores topics like police professionalization in China, guanxi networks, and quota-driven policing strategies. His methodologies span cloud computing (Azure, AWS), database systems (MySQL), and version control (Git), reflecting a technologically adept approach to criminology research. Notable research themes include the role of marketing in darknet drug trade, generational conflicts in police culture, and community-based drug rehabilitation policies. He collaborates with international teams and utilizes platforms like Microsoft Azure for large-scale data analysis.
