Andrea Vedaldi is a Professor of Computer Vision and Machine Learning at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). He specializes in unsupervised methods for understanding images and videos, focusing on 3D geometry and semantics. His research bridges foundational AI and practical applications, with contributions to generative models, neural fields, and self-supervised learning. Education: PhD in Computer Science (2008), University of California, Los Angeles MSc in Computer Science (2005), UCLA BSc in Information Engineering (2003), University of Padua Research Interests: Unsupervised learning, 3D perception, generative AI, neural rendering, and scalable vision systems. His work emphasizes ethical, responsible AI aligned with ERC-funded projects like UNION (ERC Consolidator Grant). Key Contributions: Co-developer of VLFeat and MatConvNet libraries Leader in 3D reconstruction and diffusion models (e.g., CatFree3D) Recipient of the PAMI Thomas S. Huang Prize and multiple best paper awards Grants & Service: Principal Investigator on £2.3M ERC Consolidator Grant (UNION) Co-organizer of major conferences (ECCV 2020 Program Chair, CVPR 2023 Area Chair) Reviewer for top journals/conferences (PAMI, CVPR, NeurIPS) Labs & Teams: VGG Group at Oxford, collaborating on projects like Meta 3D Gen and Common Objects in 3D (CO3D).
Prof. Ingmar Posner is a leading figure in applied artificial intelligence at the University of Oxford, where he serves as Principal Investigator for the Applied Artificial Intelligence Lab (A2I) and founding Director of the Oxford Robotics Institute. His work focuses on enabling robots to operate effectively in complex real-world environments through experience-driven learning. Key research areas: robot learning, scene interpretation, data-efficient learning, and transfer learning Applications in manipulation, autonomous driving, logistics, and space exploration His team has produced groundbreaking work in world models, sim-to-real transfer, and constraint-based manipulation systems (e.g., COMBO-Grasp). Notable contributions include the TWIST distillation framework and foundational research in tactile data generation (TactGen). He has received multiple best paper awards at top robotics venues. Publications reveal evolving research themes: 2025 work emphasizes language-conditioned learning (Lumos) and multi-agent decision-making, while 2024 focused on diffusion models for locomotion and differentiable simulators. Earlier work spans from urban scene analysis to physically plausible scene synthesis (RELATE).
Professor Andrew Davison holds the position of Professor of Robot Vision at Imperial College London's Department of Computing. He leads the Dyson Robotics Laboratory and the Robot Vision Research Group, focusing on advancing SLAM (Simultaneous Localization and Mapping) and Spatial AI. His groundbreaking work includes the MonoSLAM algorithm (2003), enabling real-time 3D vision for robotics and AR/VR. Current research emphasizes scalable, semantic-rich Spatial AI systems, as outlined in his FutureMapping papers (2018–2019). Education: BA in Physics (Oxford, 1994), D.Phil. (Oxford, 1998). Postdoctoral work at AIST, Japan (1998–2000), followed by a lectureship at Imperial (2002–present). Industrial collaborations include SLAMcore, a Spatial AI startup, and Dyson Robotics Lab. Over 18 PhD students supervised, many now leading roles at Meta, NVIDIA, SLAMcore, and academia. Notable contributions include DTAM, KinectFusion, and Event Camera SLAM. Recognized for software tools like SceneLib and contributions to robotics benchmarks (SLAMBench). Active on Twitter (@AjdDavison) for research updates.
Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.
Professor Simon Godsill MA PhD FIET FIEEE is a University Professor of Statistical Signal Processing in the Department of Engineering at the University of Cambridge. He heads a research team specializing in statistical signal processing, digital audio restoration, and Bayesian inference. His work addresses the processing and analysis of digital speech, audio, tracking systems, and financial datasets, with a focus on probabilistic modeling and computational methods. Research interests include statistical signal processing , degraded signal restoration , and Bayesian computational methods . Recent publications emphasize Gaussian processes, variational inference, and multi-object tracking for applications in audio enhancement and financial data analysis. He co-founded the audio remastering company CEDAR Audio Ltd in 1988. Scientific awards: Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Institute of Electrical and Electronics Engineers (FIEEE) Outside academia, he enjoys singing, cricket, piano/organ playing, and running. His team at Cambridge's Engineering department focuses on robust tracking algorithms and signal enhancement techniques.
Goran Strbac is a Professor of Energy Systems at Imperial College London's Faculty of Engineering, holding the Chair in Electrical Energy Systems. He leads the Department of Electrical and Electronic Engineering and directs the joint Imperial-Tsinghua Research Centre on Intelligent Power and Energy Systems. His roles include IPCC WG 3 Leading Author, OFGEM RIIO-2 Challenging Group Member, and member of multiple EU energy platforms. Strbac's research focuses on multi-energy systems integration, energy market design, renewable integration, distributed energy resources, and grid resilience. Notable contributions include whole-energy system modeling across operation and investment scales, market mechanisms for flexibility services, and security assessments for low-carbon infrastructure. His work emphasizes resilience and decarbonization, with over 500 publications and 4 co-authored books. He advises governments and regulatory bodies on energy policy, including contributions to UK Smart System Forum and European Technology and Innovation Platforms. His recent articles explore AI-driven grid optimization, hydrogen integration, and transactive energy systems. Strbac leads major initiatives like the UK Centre for Grid Scale Energy Storage and chairs resilience-focused research clusters. His research bridges academia and industry, informing policy through rigorous systems analysis and innovative modeling frameworks.
Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk
Perla Maiolino serves as an Associate Professor in Engineering Science at the University of Oxford and Principal Investigator of the Soft Robotics Lab (SRL) within the Oxford Robotics Institute. Her academic foundation includes BEng, MEng, and PhD degrees in Robotics and Automation from the University of Genoa, where she pioneered CySkin technology for distributed tactile sensing in robots—later exhibited at the Science Museum in London. She expanded her expertise during a 2017-2018 postdoctoral fellowship at Cambridge University's Biologically Inspired Robotics Lab, focusing on soft robotics and tactile perception. Dr. Maiolino's research centers on developing artificial skin systems, soft robotic actuators, and distributed sensing architectures. Her work bridges biological inspiration with engineering innovation to create robots capable of safe human interaction and dexterous manipulation in unstructured environments. Key contributions include compliant beaded-string jamming mechanisms for anthropomorphic fingers, monolithic 3D-printed soft pneumatic arms (JAMMit!), and distributed time-of-flight sensor networks for robotic self-awareness. Recent publications (2024-2025) reveal a strong convergence of tactile sensing with machine learning, featuring optical flow for gesture recognition, diffusion models for artificial skin simulation, and zero-shot sim-to-real transfer techniques. Her team has made significant advances in multi-modal sensing integration, variable stiffness actuation, and scene flow estimation for robots operating in dynamic surroundings. Scientific Awards No specific awards were documented in the provided institutional materials. Advising and Grants While her leadership of the Soft Robotics Lab implies active student supervision and grant management, detailed information about advisees or funded projects was not included in the source documentation. Labs and Teams As Principal Investigator of the Soft Robotics Lab at Oxford Robotics Institute, Dr. Maiolino directs research on tactile perception systems, soft actuation mechanisms, and sensor-integrated robotic structures. The lab's work focuses on applications requiring safe physical interaction, including healthcare robotics and human-robot collaboration scenarios, with emphasis on multi-material 3D printing and embedded sensing technologies.
Professor Haijiang Li is a Chair in BIM for Smart Engineering at Cardiff University's School of Engineering. His roles include leading the Computational Mechanics and Engineering AI Research Group, directing the BIM for Smart Engineering Centre, and overseeing the BIM MSc programme. He holds editorial roles for journals like Construction Innovation and Automation in Construction , and chairs the European Group of Intelligent Computing in Engineering (EG-ICE). Research focuses on smart computational engineering platforms integrating BIM, AI, and big data for sustainable infrastructure. Key areas include digital twins, disaster management, and resilient urban systems. He has secured £40M in research funding, including £9M as PI, and led over 70 research staff and students. Prof. Li is a Standards Committee Technical Executive at buildingSMART, driving international BIM standards. His work includes co-authoring a book on BIM standards across China, the US, and the UK. Awards include Fellowships from the British Computer Society (FBCS) and the Higher Education Academy (FHEA). His research outputs span over 250 publications, covering topics like AI-driven bridge maintenance, ontology-based decision-making, and energy-efficient urban systems. Collaborations with industry and global partners emphasize practical applications of BIM and smart technologies.
Professor Gabriel Brostow is a faculty member in the Department of Computer Science at University College London (UCL), where he leads research in Computer Vision and Human-Computer Interaction. He also serves as Chief Research Scientist and Senior Director of the R&D Team at Niantic, the company behind Pokémon GO. His work bridges academic research and industry applications, focusing on developing AI systems that enhance human capabilities through what he terms 'Human in the Loop AI'—now commonly referred to as Human-Centered AI. Brostow completed his BS in Electrical Engineering at UT Austin, followed by a PhD with Irfan Essa at Georgia Tech. He then pursued postdoctoral research with Roberto Cipolla's Computer Vision & Robotics Group at Cambridge University as a Marshall Sherfield Fellow, and with Marc Pollefeys in ETH Zurich's CVG Group. His research explores how AI, particularly Computer Vision, can serve as 'super-tools' for professionals across various domains including filmmaking, architecture, robotics, and scientific research. Specific interests include assistive technology for everyday life, authoring systems that maximize user effort, 3D reconstruction, depth estimation, and vision-language models. His work often involves creating systems that are validated through real-world human interaction to ensure practical utility. Analysis of his recent publications reveals a strong focus on practical applications of Computer Vision that directly interact with humans. His research spans 3D scene understanding, depth estimation, sketch-based interfaces, and multimodal AI systems. There's a clear emphasis on creating benchmarks and tools that facilitate human-AI collaboration, with applications in assistive technology, urban planning, filmmaking, and biodiversity monitoring. His work frequently appears at top conferences including CVPR, NeurIPS, ECCV, and CHI. Marshall Sherfield Fellowship Brostow actively mentors PhD students, with current advisees including Ross Murphy, Skanda Koppula, Gizem Unlu, Omiros Pantazis, and Jamie Watson. His alumni include numerous PhD graduates and MSc students who have gone on to successful careers in academia and industry. He emphasizes selecting students based on passion and potential rather than just academic credentials, valuing traits like helpfulness, drive, and hunger to learn. His research is supported through collaborations with major institutions and companies including DeepMind, MIT, and the University of Edinburgh. He leads a research group at UCL that collaborates closely with Niantic's R&D team, creating a unique bridge between academic research and industry application. His team's work frequently involves developing novel Computer Vision techniques that are validated through real-world human interaction, ensuring practical utility alongside technical innovation. The group explores blue-sky research problems with applications ranging from assistive technology to professional tools for filmmakers, architects, and scientists studying diverse environments.
Professor Daniel Rueckert is a leading academic in Artificial Intelligence and Medical Imaging, holding dual positions at Imperial College London (as Professor of Visual Information Processing) and Technical University of Munich (Alexander von Humboldt Professor for AI in Medicine and Healthcare). He obtained his MSc from Technical University Berlin (1993) and PhD from Imperial College London (1997), followed by postdoctoral work at King’s College London. At Imperial, he led the Department of Computing (2016–2020) and founded the Biomedical Image Analysis group. His research focuses on AI-driven medical image analysis, including algorithms for image reconstruction, registration, and clinical decision support. His research interests span AI applications in healthcare, machine learning for medical imaging, and computational methods for clinical diagnostics. Notable contributions include over 500 publications and 60+ PhD graduates, with key works in federated learning, cardiac motion analysis, and biomarker development. Awards include the Leibniz Prize (2025), Royal Academy of Engineering Fellowship (2015), and multiple ERC grants. He leads the BioMedIA research group and is an editorial board member of Medical Image Analysis . Recent publications highlight advancements in AI-driven medical imaging, such as secure federated learning frameworks and deep learning models for disease prediction. His work bridges academic and industrial sectors through initiatives like IXICO, an Imperial spin-out. Current affiliations include roles at both Imperial and TUM, emphasizing interdisciplinary collaboration in healthcare technology. Advising and grants: Supervised over 60 PhD students and 40 post-docs. Secured grants including ERC Synergy (2013) and ERC Advanced (2020). Active in labs focused on biomedical image computing and AI in healthcare systems. Collaborative efforts include the BioMedIA group and TUM’s AI initiatives. Labs/teams: Leads the Biomedical Image Analysis group at Imperial and the TUM AI in Medicine team. Collaborates extensively on projects like cardiac imaging analysis and federated learning for healthcare.
Dr Nursen Aydin is an Associate Professor of Operational Research at Warwick Business School (WBS), affiliated with the ISM-Analytics (ISMA) Group. She holds a PhD in Industrial Engineering from Sabanci University, Turkey, with a prior visit to Cornell University for airline revenue management research. Her career includes roles as a Marie Curie Research Fellow at Brunel Business School and as an assistant revenue management analyst at Pegasus Airlines. Research Interests: Revenue Management and Pricing Stochastic Dynamic Programming Sustainable Transport Planning Large-Scale Applications Awards and Fellowships: Turing Fellow, Alan Turing Institute (2021–2023) Marie Curie Research Fellowship, Brunel Business School (2015–2016) Teaching: She teaches Business Analytics (IB1220) across multiple BSc programs and Analytics in Practice (IB9BW0) at the MSc level. Research Group: Active member of the ISM-Analytics (ISMA) Group, focusing on advanced analytics and operational research methodologies.
Peter Pietzuch is a Professor in the Department of Computing at Imperial College London, where he leads the Large-Scale Data & Systems (LSDS) group. He also serves as the Director of Research and is a Visiting Researcher at Microsoft Research Cambridge. Pietzuch holds a Ph.D. from the University of Cambridge and a B.A. from Girton College. His research spans distributed systems, cloud computing, big data processing, and systems security. Key interests include: Scalable architectures for cloud-native applications Efficient stream processing and machine learning systems Trusted execution environments and secure cloud infrastructure Optimization of serverless computing and distributed databases His recent publications focus on adaptive machine learning frameworks, secure cloud resource management, and high-performance stream processing systems. Trends show strong emphasis on hardware-software co-design, confidential computing, and fault-tolerant architectures. Awards include: Best Paper Award at Middleware'03 He actively advises PhD students and secures grants for projects like Faasm (serverless computing) and Teechain (blockchain security). His LSDS group collaborates with industry partners including Microsoft Research. Pietzuch teaches undergraduate and graduate courses including Scalable Systems for the Cloud and Operating Systems . He co-founded the ACM DEBS conference and serves on steering committees for EuroSys and Middleware.
Professor Paul Goulart is a full Professor of Engineering Science at the University of Oxford and Tutorial Fellow at St Edmund Hall, positions he has held since 2014. He leads research and teaching in robust optimization, control systems, and high-speed numerical methods, with applications spanning fluid flows, traffic networks, and economics. Education SB & MSc, Aeronautics and Astronautics – Massachusetts Institute of Technology (MIT) PhD, Control Engineering – University of Cambridge (Gates Scholar, 2007) Research Interests Professor Goulart’s work lies at the intersection of control engineering and optimization . His core expertise includes: Robust and high-speed convex optimization Model predictive control (MPC) and control barrier functions Neural-network-based control and system identification Optimization over traffic and economic networks Real-time and embedded optimization solvers These interests are reflected in prolific publication output and active supervision of doctoral researchers. Publications & Trends From 2020 to 2025 Professor Goulart has co-authored more than thirty papers. A dominant theme is the development of fast, reliable algorithms for conic optimization and robust control , often leveraging machine-learning techniques to enhance scalability and real-time performance. Recent works emphasize safety certificates, GPU-accelerated solvers, and neural-network controllers for uncertain systems. Awards & Honors Gates Cambridge Scholar (2003) Advising & Grants Professor Goulart actively seeks DPhil students in control engineering and optimization . He leads the Control Group within the Department of Engineering Science and has been involved in multiple industrially funded projects, although specific grant identifiers are not provided in the supplied text. Laboratory & Teams He is a member of the Control Group , Department of Engineering Science, University of Oxford, and serves as Secretary to the Governing Body of St Edmund Hall (Michaelmas Term 2024).
Professor Dylan Jones is a Professor of Operational Research at the University of Portsmouth within the School of Mathematics and Physics. He holds dual affiliations with the Centre for Operational Research and Logistics and the Centre of Excellence in Defence, Risk & Resilience. His academic journey includes a BSc (Hons) in Mathematics with Operational Research from the University of Southampton and a PhD in Operational Research from the University of Portsmouth. Specializing in Multi-Criteria Decision Making (MCDM), his research spans logistics, healthcare, renewable energy, and defense applications. He has led over 18 PhD theses and secured EU funding for projects focused on offshore wind energy and sustainable logistics. Professor Jones is also the Director of the Centre for Operational Research and Logistics, emphasizing strategic port development and disaster risk reduction. His work integrates advanced methodologies like goal programming and mixed modeling to address complex real-world challenges. Recent contributions include frameworks for offshore wind farm logistics, sustainable port selection, and resilience-based maintenance strategies. Education: BSc (Hons) in Mathematics with Operational Research, University of Southampton PhD in Operational Research, University of Portsmouth Research interests revolve around applying operational research principles to solve multi-objective problems in logistics, healthcare systems, and renewable energy sectors. His work emphasizes sustainability, decision-making under uncertainty, and optimizing resource allocation. Key projects include developing methodologies for offshore wind energy infrastructure and analyzing risk in maritime logistics. His research outputs (109+ publications) focus on advancing operational research techniques, with notable contributions to goal programming, logistics optimization, and multi-criteria decision analysis. He collaborates internationally, particularly in Brazil, France, Spain, and Portugal, to address global challenges in sustainable energy and infrastructure. Labs/Teams: Centre for Operational Research and Logistics Centre of Excellence in Defence, Risk & Resilience
