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.
Tomaso Aste is a Professor of Complexity Science at the Department of Computer Science, University College London (UCL). He founded the Financial Computing and Analytics group and co-founded the UCL Centre for Blockchain Technologies. His work bridges complex systems, data science, and finance, with applications in blockchain, fintech, and market modeling. Education: PhD in Physics (Politecnico di Milano, 1994); Laurea in Physics (University of Genoa, 1990) Prior Appointments: Reader at University of Kent's School of Physics; Associate Professor at Australian National University's Applied Mathematics His research focuses on data-driven modeling of complex systems , particularly financial systems, complex networks, and statistical physics. He has pioneered information filtering networks and topological machine learning methods for financial applications, including portfolio optimization, risk assessment, and cryptocurrency analysis. Recent publications highlight his expertise in financial time-series analysis, blockchain technology, and AI-driven modeling. Articles explore topics like limit order books, cryptocurrency market fragility, and topological neural networks. His work has influenced regulatory technology (RegTech) frameworks and digital economy strategies. Scientific Awards : Marie Curie Individual Fellowship University of Genoa graduate study specialization Fellowship Bacheflor Boncompagni-Ludovisi Foundation Fellowship Awarded fellowships from European Commission and academic foundations support his interdisciplinary research. He has held editorial roles at journals like Philosophical Magazine and Granular Matter , and contributed to professional societies including American Physical Society and Australian Research Council panels. Teaching & Academic Leadership : Co-created four UCL Master's programs: Financial Risk Management, Computational Finance, Financial Technologies, Emerging Digital Technologies Coordinates executive training on AI, blockchain, fintech, and regtech for regulators and private firms Teaches graduate-level courses in Data-Driven Modeling, Data Science, and Advanced AI
Vaishak Belle is a Reader in Logic and Learning at the School of Informatics, University of Edinburgh, and serves as Director of Research and Innovation at the Bayes Centre (40% position). His academic career focuses on the critical intersection of artificial intelligence, formal logic, and machine learning systems. His research spans multiple domains within AI: Neurosymbolic AI integration approaches Logic-based machine learning frameworks Ethics, fairness, and responsibility in AI systems Generative AI and its societal impact Robotics with advanced reasoning capabilities Causal modeling and probabilistic reasoning Belle leads a research laboratory dedicated to advancing neurosymbolic AI, which combines the explainability of symbolic systems with the learning capabilities of neural networks. His recent work has explored abduction in logical frameworks, formal languages for AI safety, and the relevance of logic for general-purpose AI systems. He has made significant contributions to bridging the gap between theoretical AI foundations and practical applications. His publications demonstrate consistent focus on creating AI systems that can reason formally while learning from data, with applications ranging from robotics to ethical decision-making frameworks. His research addresses fundamental challenges in developing trustworthy, explainable AI that can operate safely in complex environments. Beyond technical research, Belle is actively engaged in AI education through executive programs on AI leadership and ethics, and has contributed to public understanding through his children's book "The girl and the robot" which explores human-robot relationships and emotional projection onto artificial agents. He maintains an active presence in the international AI research community, regularly participating in major workshops including Dagstuhl seminars on neurosymbolic AI, and collaborating with researchers across disciplines from computer science to philosophy.
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. 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.
Andrew Rice is a Professor of Computer Science at the University of Cambridge's Department of Computer Science and Technology, and holds the Hassabis Fellowship in Computer Science. He is also the Director of Studies in Computer Science at Queens' College. His research focuses on programming languages, software engineering, and machine learning applications in software development. He leads projects like Isaac Computer Science and ALTA (Automated Language Teaching and Assessment), advancing adaptive learning technologies. His work includes static analysis tools such as Error Prone at Google, energy efficiency studies in computing infrastructure, and contributions to the Computing for the Future of the Planet initiative. His teaching emphasizes practical skill development through flipped classrooms and video lectures, earning him the 2014 Pilkington Prize for teaching excellence. He has held visiting roles at Google and collaborated on energy consumption research for mobile devices and data centers. His research spans systems, networking, and natural language processing, with a strong focus on applying computational methods to real-world challenges. Key Projects: Isaac Physics/Computer Science, ALTA, Error Prone Static Analysis Research Themes: Programming Languages, Machine Learning, Energy Efficiency Awards: Pilkington Prize (2014)
Anil Madhavapeddy serves as Professor of Planetary Computing at the University of Cambridge's Department of Computer Science and Technology and directs the Cambridge Centre for Carbon Credits (4C). A Fellow of Pembroke College, he integrates systems research with environmental conservation through the Computer Laboratory's Environment and Energy Group. His career spans industry leadership (NetApp, Citrix, Intel), academic appointments (Cambridge, Imperial, UCLA), and entrepreneurial ventures (XenSource, Unikernel Systems, Docker). Madhavapeddy earned his PhD at Cambridge's Computer Laboratory in 2006. His research bridges computational systems and planetary-scale environmental challenges, with deep expertise in open-source development (OCaml, Xen, Docker, OpenBSD) and technology strategy advising for organizations including Zededa, Tezos Foundation, and Tarides. His work centers on environmental computing and climate informatics, leveraging distributed systems and functional programming to develop sensing infrastructure for conservation. Recent projects focus on carbon credit systems, AI-driven biodiversity monitoring, and sustainable computing architectures that minimize ecological footprints while maximizing analytical capability. Analysis of his 2025 publications reveals a concentrated effort on AI-integrated conservation tools, privacy-preserving carbon accounting, and energy-efficient computing. Key themes include spatial networking for ecological data, LLM-enhanced evidence retrieval in conservation science, and novel metrics for extinction risk assessment—demonstrating computational innovation applied to urgent planetary boundaries. No scientific awards were documented in the source material. Madhavapeddy advises multiple technology firms on strategic development while leading the Cambridge Centre for Carbon Credits, though specific grant funding details remain unreported. He actively contributes to the Environment and Energy Group at Cambridge's Computer Laboratory and directs the interdisciplinary Cambridge Centre for Carbon Credits (4C). His open-source leadership spans critical infrastructure projects including OCaml, Xen, and Docker, fostering collaborative development communities that underpin modern cloud and container technologies.
Professor Alessandra Russo leads the Structured and Probabilistic Knowledge Engineering (SPIKE) research group at Imperial College London's Department of Computing. With expertise spanning computational logic, symbolic machine learning, and neuro-symbolic AI, she develops foundational AI techniques applied to security, network management, healthcare, and adaptive systems. Professor Russo holds a PhD in Computing from Imperial College London and an MSc in Computer Science from Ionian University. Her research pioneers logic-based learning systems for intelligent adaptive technologies, with projects including declarative networking for security management, privacy-preserving federated learning, and hybrid neuro-symbolic approaches for robust reasoning. Her current work focuses on developing interpretable AI systems through neuro-symbolic integration, creating frameworks that combine neural networks with symbolic reasoning for explainable decision-making. Recent publications explore rule learning from knowledge graphs, transformer-based world models, and formal methods for representation learning. Professor Russo teaches courses on Logic-Based Learning and AI Applications, and has received the Google PhD Fellowship for her research contributions. She mentors numerous PhD students in areas spanning theoretical foundations and practical applications of computational logic and machine learning.
Axel Gandy is a Professor of Statistics at the Department of Mathematics, Imperial College London. He serves as Director of the EPSRC CDT in Modern Statistics and Statistical Machine Learning , overseeing PhD supervision and advanced statistical training.
Reiko Heckel is a Professor of Software Engineering at the University of Leicester, serving as Director of Postgraduate Teaching for Computing degrees and Data Analytics Lead at the Leicester Innovation Hub. She previously held academic roles at the Technical Universities of Dresden and Berlin before joining Leicester in 2004. Her research focuses on graph transformation systems, model-based development, stochastic modeling, and formal methods in software engineering. She earned her PhD (Dr.-Ing.) in Computer Science from TU Berlin in 1998. Her research interests span software engineering pedagogy, formal specification techniques, and applications of graph grammars in system modeling. Recent work explores stochastic graph transformations for social networks, transparency engineering in AI systems, and blockchain-based smart contract frameworks. Her contributions bridge theoretical foundations with practical applications in cybersecurity, data integration, and human-centric systems design. Key contributions include advancements in automated test case generation via graph transformations, visual contracts for software reverse engineering, and formal methods for complex system analysis. Her work frequently intersects with industry through collaborations via the Leicester Innovation Hub, emphasizing data analytics and technology transfer. Education: MSc Computer Science, Technical University of Dresden PhD (Dr.-Ing.), Computer Science, TU Berlin (1998) Leadership Roles: Head of Department (2014-2018) Director of Postgraduate Teaching (Ongoing) Research Themes: Model-Based Development Stochastic Systems Analysis Graph Neural Networks Trustworthy AI Her publications reflect a focus on formal methods, with recent trends in applying graph transformation techniques to social network modeling, blockchain smart contracts, and educational pedagogy.
Marcin Jurdzinski is an Associate Professor (Reader) in the Department of Computer Science at the University of Warwick , UK. He has been a faculty member since 2004 and is a core member of the Foundations of Computer Science and Discrete Mathematics and its Applications research groups. University: University of Warwick School: Faculty of Science Department: Department of Computer Science Position: Associate Professor (Reader) Email: Marcin.Jurdzinski@warwick.ac.uk Office: CS2.19 His research lies at the intersection of algorithms, game theory, automata, and logic , with a strong emphasis on formal verification , model checking , and theoretical computer science . He is best known for his foundational work on parity games , including the development of small progress measures and discrete strategy improvement algorithms. The recent publications reveal a consistent focus on computational complexity in games and verification. Key themes include stochastic games, timed automata, bisimilarity, and quantitative analysis . His work often bridges theoretical insights with practical verification challenges, especially in real-time and probabilistic systems. He has supervised several PhD students and hosted postdoctoral researchers such as Laure Daviaud and Alexander Kozachinskiy. He has led EPSRC-funded projects including Solving Parity Games in Theory and Practice and Counter Automata: Verification and Synthesis . PhD Students: Aditya Prakash, Thejaswini K. S., Michail Fasoulakis, John Fearnley, Michal Rutkowski, Ashutosh Trivedi Postdocs: Laure Daviaud, Alexander Kozachinskiy He is actively involved in the academic community, serving on the steering committee of the Highlights of Logic, Games and Automata conference and on program committees for major venues such as CONCUR, ICALP, and LICS. He has also organized workshops including FORMATS and ICALP co-located events.
Dr. Frederick Li is an Associate Professor in the Department of Computer Science at Durham University, UK. He holds editorial roles as Associate Editor of Frontiers in Education (Digital Education) and Editorial Board Member of Virtual Reality & Intelligent Hardware. His research focuses on Computer Graphics, Machine Learning, Geometric Modelling, Collaborative Virtual Environments, Visual Aesthetics, and Educational Technologies. He earned his B.A. (Hons) and M.Phil. from The Hong Kong Polytechnic University and his Ph.D. in Computer Graphics from City University of Hong Kong. Prior roles include Assistant Professor at HK PolyU and project manager of a Hong Kong Government ITF-funded project. **Education**: B.A. (Computing Studies) and M.Phil. from HK PolyU; Ph.D. in Computer Graphics (CityU Hong Kong). **Research Interests**: His work spans mesh saliency detection, human-object interaction recognition, cloud modeling, face beautification, and educational technology. Recent achievements include awards for papers (e.g., Best Paper at ITiCSE 2014) and recognition such as EPSRC Peer Review College membership. He leads Durham's Undergraduate Board of Examiners and has been an external examiner at Northumbria University. **Awards**: Best Paper (ACM ITiCSE 2014), Outstanding Paper (ICALT 2013), EPSRC Peer Review College (2024), Outstanding BMVC 2024 Reviewer. **Grants & Labs**: His research is supported by grants from EPSRC and others. He collaborates with the Centre for Vision and Visual Cognition, VIViD, and AIHS group at Durham.
