Yee Whye Teh is a Professor at the Department of Statistics, University of Oxford, and a research scientist at DeepMind. His work focuses on statistical machine learning, including probabilistic learning, Bayesian nonparametrics, deep learning, and Monte Carlo methods. He co-directs the ELLIS programme on Robust Machine Learning and has held roles such as Programme Co-chair for ICML 2017. Teh has delivered keynotes at UAI 2019, an IMS Medallion Lecture at JSM 2019, and the Breiman Lecture in 2017. His research emphasizes scalable inference algorithms, hierarchical models, and applications in genetics and natural language processing. Teh's educational background includes a PhD from the University of Toronto (2003) and a Master's from the same institution (2000). He has contributed to widely used software tools like the Sequence Memoizer and has been recognized for his work through prestigious lectureships. Research interests span Bayesian nonparametric models, MCMC methods, and their applications in genetics and data compression. His lab collaborates on projects like fragmentation-coagulation processes for genetic variation modeling and Mondrian forests for online learning. Teh advises students through Oxford's graduate programs, though he notes high demand for mentorship. His work often bridges theory and practice, addressing challenges in big data learning and small data problems.
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.
Mark Steedman is a Professor in the School of Informatics at the University of Edinburgh, where he conducts research in Artificial Intelligence, Computational Cognitive and Social Science, and Natural Language and Speech Processing. He is affiliated with the Institute for Language, Cognition and Computation (ILCC), the Centre for Speech Technology Research (CSTR), and the Human Communications Research Center (HCRC). He also holds an adjunct professorship in Computer and Information Science at the University of Pennsylvania. His research focuses on Combinatory Categorial Grammar (CCG) , computational linguistics , prosody and intonation , temporal semantics , gesture in communication , and computational music analysis . He has authored foundational books including Surface Structure and Interpretation , The Syntactic Process , and Taking Scope . The recent publications reflect a strong trend toward integrating formal grammatical frameworks like CCG with modern neural and distributional models, particularly in semantic parsing, entailment reasoning, and cognitive modeling. His work bridges symbolic and statistical approaches in NLP, often focusing on robust, wide-coverage parsing and semantic interpretation. Best Paper Award at AACL/IJCNLP 2023 for 'Smoothing Entailment Graphs with Language Models' Best Paper Award at ACL 2023 for 'Extrinsic Evaluation of Machine Translation Metrics' Influential Paper Award 2017 from IFAAMAS for 'Animated Conversation' Mark Steedman has supervised numerous PhD students and collaborated widely across institutions. He leads research in formal grammar applications to cognitive modeling, dialogue, and multimodal communication. His lab contributes to CCG software and semantic parsing tools, and he continues to be actively involved in advancing the integration of symbolic and neural AI.
John Holmes is Professor of Victorian Literature and Culture at the University of Birmingham, affiliated with the Birmingham Institute of Forest Research (BIFoR). His work bridges literary studies and scientific inquiry, focusing on interdisciplinary connections from 1800 to the present through extensive collaborations with institutions like the Royal Society and major natural history museums across Europe. His academic credentials include a D.Phil. in English Literature from Oxford (2001), an M.A. in Renaissance Studies from Queen Mary and Westfield College, London (1997), and a B.A. in English Language and Literature from Oxford (1994). D.Phil. English Literature, University of Oxford, 2001 M.A. Renaissance Studies, Queen Mary and Westfield College, University of London, 1997 B.A. English Language and Literature, University of Oxford, 1994 Professor Holmes specializes in literature-science intersections, particularly Victorian and modern poetry, Pre-Raphaelite studies, and natural history museums. His current research examines arts architecture within museum spaces and mythic literature's dialogue with evolutionary science. He has authored foundational works on Darwinism in poetry and Pre-Raphaelite scientific engagements while directing major collaborative projects with scientific institutions. Recent publications reveal consistent exploration of poetic form within scientific contexts, with growing emphasis on digital humanities applications and environmental education. His 2023-2024 outputs demonstrate expanded interdisciplinary scope, integrating virtual reality tools for forest education and deepening analysis of myth-science intersections in canonical poetic works. His distinguished honors include: AHRC Collaborative Doctoral Award (2011) British Society for Literature and Science Book Prize (2018) Chair of the British Society for Literature and Science (2012) Honorary Associate of Oxford University Museum of Natural History (2016) President of the Commission on Science and Literature (2023) Professor Holmes leads significant research initiatives including the AHRC-funded Interdisciplinary Forest Education project (2024) and the long-term 'Building the Book of Nature' study (2015-2023). As an active PhD supervisor, he mentors students in literature-science intersections while maintaining robust public engagement through museum collaborations, media appearances, and educational outreach programs that translate academic research for broader audiences. He directs BIFoR-affiliated initiatives connecting literary scholarship with forest research, partnering with natural history museums in Oxford, London, Berlin, and Vienna to develop interdisciplinary frameworks examining how literary traditions shape environmental understanding and scientific communication.
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.
Professor Lizelle Bisschoff is a Professor of Film Studies at the University of Glasgow’s School of Culture & Creative Arts. She specializes in African cinema with particular focus on gender representation, digital technologies, and film festival curation. Her research bridges academic scholarship and practical initiatives, such as co-founding the Africa in Motion Film Festival (AiM). She holds a PhD from the University of Stirling and has held prestigious fellowships including the Leverhulme Trust (2010-2012) and Lord Kelvin Adam Smith (2012-2015). Education: PhD in African Cinema (University of Stirling, 2009) MSc in Cultural Studies (University of Edinburgh, 2005) Research Interests: Gender dynamics in African filmmaking Emerging digital film industries across sub-Saharan Africa Postcolonial cinema and modernist aesthetics Her work emphasizes decolonizing film studies through projects like AiM Festival and edited volumes such as Women in African Cinema . Recent research explores African science fiction and feminist archives. She has secured £115k in grants for film restoration and educational outreach programs. Awards: Leverhulme Trust Postdoctoral Fellowship AHRC Follow-on Funding for Impact (£90k) British Federation of Women Graduates Scholarship Teaching: Convenes MSc Film Curation program Teaches Introduction to African Cinemas and Race on Screen Key Projects: Lead curator for Africa’s Lost Classics restoration initiative Organized international symposia on African art and activism
Sam Staton is a Professor of Computer Science at the University of Oxford and Senior Research Fellow at Jesus College. He holds a Royal Society University Research Fellowship and leads the ERC-funded BLaSt project on probabilistic programming. His research focuses on programming language theory, particularly probabilistic and quantum programming, and category theory. Staton earned his PhD from the University of Cambridge in 2007, with prior roles as a lecturer and researcher at Cambridge, Paris, and Nijmegen. Research Interests: His work explores foundational aspects of programming languages, including semantics, algebraic effects, and applications to quantum computing and statistical modeling. Recent grants include the ARIA Safeguarded AI initiative and an AFOSR award. Education: PhD in Computer Science (2007), BA from Cambridge (2002). Students & Collaborators: Supervises multiple PhD students and postdocs, including those funded through his grants. Notable advisees include Swaraj Dash (now at Heriot-Watt) and Mathieu Huot (postdoc at MIT). Awards & Grants: Royal Society Fellowship, ERC Consolidator Grant (BLaSt), EATCS Best Paper Award, and Facebook Research Award. Labs & Teams: Leads the BLaSt project and collaborates on quantum programming via algebraic effects. Engaged in editorial roles for ACM Transactions on Quantum Computing and program committees for major conferences like POPL and LICS.
Tom Rainforth is an Associate Professor of Statistical Machine Learning at the University of Oxford's Department of Statistics, leading the RainML Research Lab (rainml.uk). He holds a Tutorial Fellowship at Mansfield College and is Principal Investigator of the ERC Starting Grant 'Data-Driven Algorithms for Data Acquisition' (2024–2029). Previously, he held roles including a postdoc under Yee Whye Teh (2017–2019), Junior Research Fellow at Christ Church College (2019–2019), and Florence Nightingale Bicentennial Fellow (2020–2024). He earned his MEng in Mechanical Engineering from the University of Cambridge and his D.Phil from Oxford under Frank Wood and Michael Osborne, focusing on probabilistic programming and Monte Carlo methods. He briefly worked in Ferrari's Formula 1 team. Research Interests : Bayesian experimental design, probabilistic and data-efficient machine learning, active learning, deep learning (with a focus on probabilistic approaches), probabilistic programming, and Monte Carlo methods. His work emphasizes statistical efficiency and adaptive algorithms. Publications : Recent contributions span modern Bayesian experimental design, adaptive importance sampling (Daisee), and applications of probabilistic methods in LLMs and generative models. His research bridges theory and practice, addressing challenges in scalability and robustness. Awards : ERC Starting Grant (2024–2029), highlighting his leadership in foundational AI research. Advising & Grants : Supervises 15 graduate students, including work on Bayesian neural networks, generative flows, and experimental design. His ERC grant supports cutting-edge data-driven algorithm development. Labs & Teams : Directs the RainML Lab, which develops scalable Bayesian methods and probabilistic AI systems.
Professor Helen Abbott is a Professor of Modern Languages at the University of Birmingham, serving as Pro-Vice-Chancellor and Head of the College of Arts and Law. Her research focuses on the intersection of French poetry, music, and digital methods, particularly the work of Baudelaire. She has led major grants, including the AHRC-funded Baudelaire Song Project (2015-2019), exploring musical adaptations of Baudelaire's poetry. Abbott holds a BA from Cambridge, MA and PhD from King’s College London. She has supervised numerous PhD students and authored books such as Baudelaire in Song: 1880-1930 (2017). Awards include AHRC Standard Grant (£594,000) and an Early Career Fellowship. Her work bridges academia and practice, collaborating with musicians and digital researchers. She advises on doctoral programs and cultural initiatives, emphasizing interdisciplinary and public engagement.
Professor Sara Baker is a Professor of Developmental Psychology and Education at the University of Cambridge's Faculty of Education and a Fellow at Darwin College, where she served as Vice Master from 2021-2024. She is also a 2022 Senior Fellow in the Science of Learning with UNESCO-IBE/IBRO. Her work bridges cognitive science and educational practice, focusing on how children develop executive functions and self-regulation skills. Baker leads the Early Years Library project, co-founded the Research Centre for Play in Education, Development and Learning (PEDAL), and is a founding member of the Global Executive Functions Initiative. Sara Baker specializes in the science of learning, with research aimed at improving children's lives by identifying factors at home and school that support their agency over learning. Her work emphasizes developing executive functions and self-regulation through playful learning approaches. She uses lab-based experiments and collaborates with educators to translate cognitive science research into educational contexts. Her research spans multiple countries including the UK, USA, Mexico, Denmark, Slovakia, South Korea, Ghana, Rwanda, Nigeria, Kenya, South Africa, and more, reflecting a strong commitment to culturally relevant and globally applicable educational practices. Analysis of Professor Baker's recent publications reveals a strong focus on executive functions, self-regulation, and playful learning across diverse cultural contexts. Her work increasingly addresses cultural adaptation of assessment tools and educational practices, particularly for Global Majority countries. There's a clear trajectory from basic cognitive research toward practical applications in educational settings, with growing emphasis on teacher training, culturally relevant assessment, and the role of play in early childhood development. Her interdisciplinary approach connects developmental psychology, educational theory, and practical classroom applications. 2022 Senior Fellow in the Science of Learning with UNESCO-IBE/IBRO Professor Baker leads the doctoral program in the Faculty of Education and serves as an Academic Project Director in the School of Humanities and Social Sciences. She is the Cambridge academic lead on the Close the Gap partnership between Cambridge and Oxford, focused on postgraduate widening participation. Her research has been funded by prestigious organizations including the Newton Trust, Cambridge Humanities Research Grant, Economic and Social Research Council, LEGO Foundation, Nuffield Foundation, and Office for Students/Research England. She actively supervises doctoral students and seeks highly motivated candidates for October 2026 entry whose research aligns with her focus areas. Professor Baker co-founded and is actively involved with the Research Centre for Play in Education, Development and Learning (PEDAL), which investigates how children's play affects their development. She leads the Early Years Library project, a curated collection of evidence-based practices for early years educators, and is a founding member of the Global Executive Functions Initiative. Her work with the Connections international professional learning network focuses on self-regulation, while the Close the Gap project addresses widening participation in postgraduate education between Oxford and Cambridge.
Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.
Gordon Plotkin is a Professor at the School of Informatics, University of Edinburgh, where he is affiliated with the Laboratory for Foundations of Computer Science (LFCS). His research lies at the intersection of theoretical computer science and programming language semantics, with a profound influence on the formal understanding of computation. His research interests include Programming Language Theory, Semantics of Programming Languages, Domain Theory, Operational Semantics, Lambda Calculus, Type Theory, Concurrency Theory, and Algebraic Effects. His seminal work on structural operational semantics and domain theory has laid the foundation for modern semantics of programming languages. His publications span over five decades, showing a sustained and evolving research trajectory from foundational work in lambda calculus and domain theory to recent contributions in algebraic effects, probabilistic computation, and biochemical systems modeling. The articles demonstrate a consistent focus on formal methods, mathematical rigor, and the algebraic structure of computational effects. He has collaborated with leading researchers including Martín Abadi, John Power, Glynn Winskel, and John Reynolds. His work continues to influence both theoretical and practical developments in programming languages and systems. Gordon Plotkin has made foundational contributions to computer science, particularly through his development of structural operational semantics and domain-theoretic models of computation. He has advised numerous researchers and supervised many influential PhD theses, though specific student names are not listed in the provided text. His work has been supported by long-standing affiliations with the Laboratory for Foundations of Computer Science and the University of Edinburgh, and he has contributed to major collaborative projects in programming language design and verification. He is associated with several research groups and labs, most notably the Laboratory for Foundations of Computer Science (LFCS), which serves as a hub for theoretical research in programming languages, semantics, and logic at the University of Edinburgh.
Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.
Dr. Hong Ge serves as a Senior Research Fellow in the Department of Engineering at the University of Cambridge and holds a Fellowship at Darwin College. He maintains a dual affiliation with The Alan Turing Institute where his research centers on probabilistic programming. His work develops foundational methodologies for machine learning and intelligence with emphasis on Bayesian inference and decision-making under uncertainty. His academic background includes: MSc at the University of Edinburgh supervised by Chris Williams PhD at the University of Cambridge supervised by Zoubin Ghahramani Dr. Ge specializes in Bayesian nonparametrics, probabilistic programming, and neural networks. His research advances computational frameworks for uncertainty quantification and large-scale probabilistic modeling, with applications spanning decision theory and Gaussian processes. He bridges theoretical machine learning with practical implementations through open-source software development. He has mentored 12 researchers including current advisees Neel Alex (co-supervised with David Krueger) and Wenlin Chen (co-supervised with Miguel Hernández-Lobato and Bernhard Schölkopf), and former members such as Alexander Terenin (now at Cornell University) and Yongchao Huang (now Lecturer at Aberdeen University). His group contributed to UK government pandemic response through the Turing-RSS Health Data Lab. Dr. Ge leads the Turing.jl team developing probabilistic programming tools including AdvancedHMC.jl for Hamiltonian Monte Carlo, NestedSamplers.jl, and AbstractGPs.jl for Gaussian processes. He co-organized the CAPP Workshop on Probabilistic Programming and ACMLL Workshop on Machine Learning Languages.
Dr. Rasmus Ibsen-Jensen is a Lecturer in Computer Science at the University of Liverpool. Previously, he held a Postdoctoral position at IST Austria under Krishnendu Chatterjee and completed his PhD under Peter Bro Miltersen. Research Focus: Algorithmic game theory, strategy complexity in two-player zero-sum games, control flow graph algorithms, edit distance for automata, and theoretical biology applications. Teaching: Module Coordinator for second-year courses in database development (COMP207), C++ programming (COMP282), and industrial placement (COMP299). His work bridges computational game theory and formal verification, with recent publications exploring memory constraints in partial-information games, algebraic path properties in concurrent systems, and evolutionary spatial dynamics. While no scientific awards are explicitly mentioned in the provided text, his contributions to algorithmic complexity and interdisciplinary research (e.g., theoretical biology) highlight his academic impact.
