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.
Changjian Li is an Assistant Professor in the School of Informatics at the University of Edinburgh. He leads the GraphViX Group (Graphics, Vision and X) and is a member of the Institute of Perception, Action and Behaviour (IPAB). His research spans computer graphics, computer vision, and human-computer interaction with a focus on 3D generation and analysis. Education: Bachelor's Degree from Shandong University (2014) Ph.D. from the University of Hong Kong (2019) under Prof. Wenping Wang Postdoc at University College London (UCL) with Prof. Niloy Mitra Starting Researcher position at Inria with Dr. Adrien Bousseau Research Interests: Changjian's research focuses on sketch-based 3D modeling, CAD modeling, point cloud processing, and medical imaging applications. He develops systems that bridge intuitive sketching with precise CAD workflows, enhances 3D animation pipelines, and applies neural methods to sparse medical data reconstruction. Scientific Recognition: Best Paper Honorable Mention Award (MICCAI 2021) CADTalk selected as Highlight (CVPR 2024 top 10%) ACM SIGGRAPH Asia 2018 cover image selection ACM SIGGRAPH Asia 2015 technical paper highlight CVPR 2019 poster highlighted in 'Computer Vision News' Advising & Collaborations: He mentors postdocs and PhD students including Duolikun Danier, Haocheng Yuan, Ankan Bhunia, and Lei Zhong. Former advisees include Salvatore Esposito (now at Edinburgh), Guangshun Wei (Shandong University), and Mingjun Yang (University of Melbourne). Collaborates with Oisin Mac Aodha, Hakan Bilen, and Niloy Mitra. Professional Service: Currently serves as Associate Editor for IEEE TVCG and participates in program committees for SIGGRAPH Asia, SIGGRAPH, EuroGraphics, and Geometry Design and Computing (GDC) conferences.
Iro Laina is a Departmental Lecturer in Computer Vision at the University of Oxford's Visual Geometry Group. She holds a PhD (Dr. rer. nat.) from the Technical University of Munich (TUM), where her dissertation earned the ECVA PhD Award. Her research focuses on unsupervised and language-supervised learning for 3D scene understanding, image/video perception systems, and geometric reconstruction. Education: PhD in Computer Science (TUM), MSc in Biomedical Computing (TUM), Diploma in Electrical & Computer Engineering (NTUA). Research Interests: 3D Reconstruction and Generation Unsupervised Learning Multi-View and Video Analysis Generative Diffusion Models Geometry-Aware Networks Her recent work emphasizes scalable 3D scene synthesis, training-free methods, and cross-modal fusion with LLMs. Over 15+ publications since 2021 reflect her leadership in geometric deep learning. Awards: ECVA PhD Award (2020), Recognized in multiple international conferences. Advising: Mentors DPhil students in creative AI applications (e.g., gameplay design). Active in Oxford's Robotics and Biomedical Engineering networks. Labs/Tech: Core member of the Visual Geometry Group, collaborating on projects like IMAD2025 with the ZERO Institute.
Dr. Chuanxia Zheng is a Marie Skłodowska-Curie Actions (MSCA) Fellow and Research Fellow at the Visual Geometry Group (VGG) within the Department of Engineering Science at the University of Oxford, working with Professors Andrea Vedaldi and Andrew Zisserman. He holds a PhD from Nanyang Technological University (NTU), where his thesis on 'Synthesizing Photorealistic Images' earned the NTU Outstanding PhD Thesis Award in 2022. Starting Fall 2025, he will assume the position of Nanyang Assistant Professor at NTU's College of Computing and Data Science, leading the Physical Visual Group. His research focuses on Creative AI , emphasizing systems that perceive, reconstruct, and interact with the physical world. Key areas include 3D/4D reconstruction, generative models, and digital twins integrating geometric, dynamic, and physical properties. He has pioneered methods like feed-forward 3D reconstruction (Flash3D), amodal completion (Amodal3R), and physics-aware generative models (DSO). Notable awards include the Singapore NRF Fellowship (2025), DAAD Ainet Fellowship (2024), and MSCA Fellowship (2024). His work spans over 30 peer-reviewed publications in top venues like CVPR, ECCV, NeurIPS, and ICCV, with contributions to open-source projects like CVQ-VAE on GitHub. Current openings include PhD, postdoc, and research assistant positions focused on advancing physical-aware AI systems and generative modeling.
Professor Adrian Hilton is a distinguished faculty member at the University of Surrey, serving as Director of the Centre for Vision, Speech and Signal Processing (CVSSP) and Director of the Surrey Institute for People-Centred AI. He is affiliated with the School of Computer Science and Electronic Engineering and leads the Visual Media Research Lab (V-Lab). His research focuses on pioneering next-generation 4D computer vision technologies that enable machines to understand and model dynamic real-world scenes. Key areas include 3D/4D shape capture, computer vision, machine learning, graphics, and animation for applications in sports analysis, film/TV production, virtual reality, and medical imaging. His work bridges the gap between real and computer-generated imagery, with notable contributions in volumetric capture, motion capture, and free-viewpoint video. Hilton's recent publications demonstrate a strong trend toward multimodal integration, particularly combining audio and visual processing for spatial audio applications, while advancing 4D reconstruction techniques for human performance capture. His work increasingly incorporates transformer architectures and neural rendering techniques for improved illumination estimation, shadow modeling, and multi-view consistency. Scientific Awards and Recognition Two EU IST Innovation Prizes Manufacturing Industry Achievement Award Royal Society Industry Fellowship (2008-2011) Royal Society Wolfson Research Merit Award in 4D Vision (2013-2018) Fellow of the Royal Academy of Engineering (FREng) Fellow of the International Association for Pattern Recognition (FIAPR) Fellow of the Institution of Engineering and Technology (FIET) Hilton actively mentors PhD and post-doctoral researchers through his leadership of CVSSP, which has a grant portfolio exceeding £31M and comprises 170 researchers. He has successfully commercialized several technologies, including systems used by the BBC for sports commentary visualization. His research collaborations span major industry partners including BBC, BT, Sony, Framestore, and The Foundry. He co-founded the G3 Games forum and the CVMP Conference on Visual Media Production, demonstrating strong engagement with the creative industries. Current research projects include the S3A Programme Grant in Future Spatial Audio and InnovateUK's ALIVE project for 360 video reconstruction.
Dr. Armin Mustafa is an Associate Professor in Computer Vision and AI at the University of Surrey, where he holds a prestigious Royal Academy of Engineering Research Fellow position. He is affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP), the School of Computer Science and Electronic Engineering, and the Surrey Institute for People-Centred Artificial Intelligence (PAI). His research focuses on developing AI systems for visual understanding of complex dynamic scenes, with applications in entertainment, autonomous systems, and augmented/virtual reality. Dr. Mustafa completed his PhD in general dynamic scene reconstruction from multi-view videos in 2016 from the University of Surrey under the supervision of Prof. Adrian Hilton. Prior to his doctoral studies, he worked for three years (2010-2013) at Samsung Research Institute in Bangalore, India, in the field of Computer Vision. His research expertise spans Computer Vision, Scene Understanding, 3D/4D Vision, Virtual Reality, Light Fields, Machine Learning, Video Captioning, Augmented Reality, Artificial Intelligence, and Audio-visual Video Understanding. Dr. Mustafa has pioneered advances in 4D vision, NLP, and Scene Understanding over the past decade, with a particular focus on enabling machines to model and interpret real-world environments for socially beneficial applications. His work bridges theoretical advances in computer vision with practical applications in media production, virtual reality, and autonomous systems. Analysis of Dr. Mustafa's recent publications reveals a strong focus on multimodal learning, particularly the integration of audio and visual information for scene understanding. His work spans diverse areas including shadow detection and removal, audio event classification, video captioning, person image generation, and dynamic scene reconstruction. A notable trend is his exploration of transformer architectures for both vision and audio tasks, as well as the application of self-supervised learning techniques to reduce dependency on labeled data. Dr. Mustafa has received numerous prestigious awards: 2018 - Research Fellowship, The Royal Academy of Engineering, UK 2017 - Young Researcher award, CVPR 2016 - Doctoral Consortium grant, CVPR 2015 - BMVA travel grant for ICCV 2014 - Set-Squared Research to Innovator grant 2013 - Overseas Research Scholarship, FEPS, The University of Surrey 2010 - Cadence Silver Medal, Indian Institute of Technology, Kanpur As a dedicated mentor, Dr. Mustafa supervises several PhD students working on cutting-edge topics including multi-person reconstruction, audio-visual scene understanding, and automatic storyboard generation. His research is supported by significant grants including a £15 million UKRI Prosperity Partnership with the BBC (AI4ME), a 5-year Royal Academy of Engineering fellowship (4D Vision for Perceptive Machines), and multiple projects with industry partners such as Figment Productions and Foundry. Dr. Mustafa is an active member of the Centre for Vision, Speech and Signal Processing (CVSSP), one of the world's leading research centers in vision, speech, and signal processing. He also contributes to the Surrey Institute for People-Centred Artificial Intelligence (PAI), where he serves as a Surrey AI Fellow. His work often involves collaboration with industry partners and other academic institutions across Europe.
Dr Pengpeng Hu is a Senior Lecturer in Fashion Technology at the Department of Materials, The University of Manchester, UK. His research focuses on geometric deep learning, 3D human body reconstruction, point cloud processing, and smart textiles, bridging fashion technology with biomedical and engineering applications. Associate Editor: IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Automation Science and Engineering Academic Editor: PLOS ONE Editorial Board Member: Scientific Reports Programme Chair: 25th UK Workshop on Computational Intelligence Area Chair: 35th British Machine Vision Conference His work advances vision-based measurement systems, wearable technology, and 3D scanning for clothing and healthcare. Recent publications include innovations in MXene-based electronic textiles, 4D hand measurement extraction, and anthropometric analysis from depth images. Recipient of the Emerald Literati Award for an outstanding paper in 2019 Dr Hu accepts self-funded PhD students in areas like 3D human reconstruction, point cloud processing, and smart textiles. His editorial roles and conference leadership highlight his influence in computational intelligence and machine vision communities.
Dr. Christopher Gilliam is an Assistant Professor in Applied Signal Processing at the University of Birmingham's Department of Electronic, Electrical and Systems Engineering. He holds an MEng (1st Class Hons) in Electrical & Electronic Engineering (2008) and a Ph.D. in Signal Processing (2013), both from Imperial College London. Prior to joining Birmingham in 2022, he was a Postdoctoral Fellow at The Chinese University of Hong Kong (2013–2017) and a Research Fellow at RMIT University, Australia (2017–2022). Research Interests: Sensor signal processing, radar imaging, sampling theory, motion estimation, quantum navigation, and medical imaging. Labs: Microwave Integrated Systems Laboratory (MISL). Committees: Member of IEEE Signal Processing Society and APSIPA Technical Committees. His work focuses on advancing signal processing techniques for radar systems, navigation, and medical imaging. Recent research highlights include drone-based SAR imaging, motion correction in MRI, and fusion of classical/quantum sensors for inertial navigation. He is actively supervising PhD students and contributes to projects sponsored by DSTG. Publications span radar SLAM, probabilistic navigation algorithms, and deep learning-driven medical imaging solutions. His research bridges theoretical signal processing with practical applications in autonomous systems and healthcare.
Tobias Ritschel is a Professor of Computer Graphics at University College London . His research spans advanced rendering techniques, perceptual modeling, and data-driven graphics, with a focus on bridging physical accuracy and artistic flexibility in visual computing. Key research themes include: Interactive Global Illumination : Real-time simulation of complex lighting effects on GPUs Perceptual Graphics : Human vision-driven rendering and display optimization Non-physical Graphics : Beyond-photorealistic techniques for artistic expression Data-driven Graphics : Leveraging large datasets for novel rendering and modeling approaches His recent work emphasizes neural rendering , differentiable graphics , and X-ray tomography , with applications in 3D reconstruction , NeRF manipulation , and holographic imaging . Notable scientific achievements include the Eurographics Young Researcher Award 2014 and Eurographics Thesis Award 2011 . He has advised multiple PhD students including Philipp Henzler (EG PhD Award 2024) and Thomas Leimkühler (Otto Hahn Medal 2019), while actively contributing to conference leadership as co-chair for EGSR 2024 and Pacific Graphics 2024 . His team collaborates on X-ray reconstruction with Pablo Villanueva-Perez and works on 3D perception with Anthony Steed.
Dr. Polly Smith is a research-focused academic affiliated with the Department of Mathematics and Statistics at the University of Reading, within the School of Mathematical, Physical and Computational Sciences. She has been actively publishing since 2007, with a strong emphasis on data assimilation techniques applied to environmental and geophysical systems. Her research interests center on data assimilation , parameter estimation , and model predictability in complex dynamical systems. These include sea-ice models, fluvial inundation forecasting, morphodynamic modeling of coastal systems, and strongly coupled atmosphere-ocean models. Her work combines advanced numerical methods with real-world environmental data to improve forecasting accuracy and model reliability. The recent publications show a trend toward interdisciplinary applications, integrating satellite remote sensing, image-based monitoring, and hybrid variational-ensemble data assimilation methods. Her work spans climate science, hydrology, and coastal engineering, demonstrating a consistent focus on improving predictive capabilities in Earth system modeling. Scientific Awards: No awards explicitly mentioned in the provided text. Dr. Smith has collaborated extensively with leading researchers such as Sarah L. Dance, Nancy K. Nichols, and Andrew S. Lawless. While no formal students or advising roles are listed, her frequent first-author status and technical reports suggest a leadership role in research projects. There is no mention of specific grants, but her work aligns with major environmental modeling initiatives. She has contributed to both peer-reviewed journals and conference proceedings, including the International Conference on Coastal Engineering. Dr. Smith's research is supported by the computational and mathematical infrastructure at the University of Reading. Her work is part of a larger effort in environmental prediction, likely involving collaborations within the university’s meteorology and climate research groups. While no dedicated lab is named, her research falls within the scope of data-driven environmental modeling teams at Reading.
Dr. Madhu Murthy is a Lecturer in the Department of Civil, Maritime and Environmental Engineering within the Faculty of Engineering and Physical Sciences at the University of Southampton. His research focuses on advanced geotechnical investigations using cutting-edge imaging technologies and experimental methods to address complex engineering challenges in transportation infrastructure and marine environments. Research Interests Dr. Murthy's research spans multiple domains within geotechnical engineering, with particular expertise in: Advanced characterization of soil and granular materials using X-ray micro-computed tomography Mechanical behavior of railway ballast and development of sustainable reuse strategies Marine sediment mechanics and methane hydrate formation processes Unsaturated soil mechanics and water retention characteristics Gas migration phenomena in porous media His work integrates experimental techniques with computational analysis to provide fundamental insights into material behavior under various loading and environmental conditions. Dr. Murthy has established collaborations with international research teams and industry partners to address pressing infrastructure and energy challenges. Publication Trends Dr. Murthy's recent publications demonstrate a strong focus on applying advanced imaging techniques, particularly X-ray computed tomography, to solve complex geotechnical problems. His research spans from fundamental soil mechanics investigations to practical railway engineering applications. A notable trend is the integration of synchrotron imaging with geomechanical testing to visualize and quantify material behavior at the micro-scale. His work on methane hydrates bridges geotechnical engineering with energy resource exploration, while his railway ballast research addresses sustainability challenges in transportation infrastructure. Academic Supervision Dr. Murthy currently supervises PhD students working on cutting-edge geotechnical research topics: Rashid Salum Abeid: Investigating railway ballast behavior under various loading conditions Komeil Valipourian: Researching advanced geotechnical characterization methods His supervisory approach emphasizes both theoretical understanding and practical application, preparing students for successful careers in academia and industry.
Professor Ahmed H. Elsheikh is a faculty member at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, within the Institute for GeoEnergy Engineering. He holds a Professor title since 2021, previously serving as Associate (2017-2021) and Assistant Professor (2013-2017). His educational background includes a Ph.D. (2010) and MASc (2002) from McMaster University, and a BASc from Al-Azhar University (1999). Research Interests: Fluid control via AI, predictive machine learning, generative modeling, data assimilation, Bayesian uncertainty quantification, and subsurface engineering. His work addresses challenges in reservoir modeling, CO2 sequestration, seismic inversion, and geophysical data analysis. Publications span 2004-2025 with a focus on machine learning applications in energy systems, stochastic field generation, and subsurface flow modeling. Notable contributions include AI-driven seismic inversion, deep learning for reactive transport, and ensemble-based history matching. Key Achievements: Developed novel methodologies for uncertainty quantification, GAN applications in geological modeling, and real-time reservoir monitoring systems. His work contributes to UN Sustainable Development Goals related to climate action (SDG 13) and affordable clean energy (SDG 7).
Marco Volino is a Senior Lecturer in Computer Vision and Graphics at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP). He holds a PhD and MEng in Electronic Engineering from the University of Surrey. His research focuses on advancing visual media production through interdisciplinary work in computer vision, graphics, and machine learning, with applications in film, broadcast, gaming, and immersive technologies like AR/VR. He has led projects such as the Polymersive and ALIVE initiatives, and developed hardware/software systems for volumetric video and photogrammetry. Volino has secured funding including an Epic MegaGrant and InnovateUK projects, and serves on the editorial boards of multiple conferences. Education: PhD Computer Vision and Graphics (2016, University of Surrey), MEng Electronic Engineering (2011, University of Surrey), BTec National Diploma in Electrical/Electronic Engineering (2006). Professional experience includes roles at BBC R&D, USC Institute for Creative Technologies, and Sony Broadcast Labs. He teaches modules like AR/VR and the Metaverse, and supervises MSc and PhD students in topics such as volumetric capture and digital humans. Research Highlights: Volumetric video compression, 3D human reconstruction, real-time motion capture, and immersive media production. Key Contributions: Developed 64-camera photogrammetry systems, WebGL-based renderers, and tools for Unreal Engine integration. Leadership: Co-chair of European Conference on Visual Media Production (CVMP) 2023-2024 and Area Chair for BMVC 2021-2024.
Etienne Memin is a Research Director (equivalent to Full Professor) at Inria and a Visiting Professor at Imperial College London. He leads the Odyssey research group, affiliated with Inria, University of Rennes, and other institutions. His work focuses on stochastic modeling of geophysical flows, fluid dynamics, and data assimilation. Memin is the Principal Investigator (PI) of the ERC-funded STUOD project, which explores stochastic transport in upper ocean dynamics. His research bridges disciplines such as applied mathematics, computer vision, and geophysics. Education: HDR (Habilitation à Diriger des Recherches) from University Rennes I in 2003. Previous roles include Research Director at Inria since 2008 and collaborations with institutions like IFREMER, Imperial College, and Zhejiang University. Research Interests: Stochastic modeling of fluid dynamics, data assimilation frameworks, and uncertainty quantification. His work includes developing ensemble techniques for data assimilation and stochastic representations of turbulence. Recent projects involve stochastic shell models and wave solutions in shallow water dynamics. Collaborations: Active partnerships with Imperial College, IFREMER, MétéoFrance, and others on topics like stochastic parameterization and error modeling in weather prediction. Key collaborations include the STUOD project and Royal Society-funded studies on stochastic large-eddy simulations. Students and Grants: Supervised over 20 PhD students, including current advisees Francesco Tucciarone and Antoine Moneyron. His grants include the ERC STUOD and multiple ANR projects. He leads the Odyssey group, fostering interdisciplinary research in fluid dynamics and geophysics. Labs/Teams: Odyssey group at Inria, collaborating with IMT Atlantique, IRMAR, and international partners. His work emphasizes computational fluid dynamics and real-world applications like flood modeling and wind engineering.
Professor Vitaliy Kurlin is a full professor at the University of Liverpool's School of Electrical Engineering, Electronics, and Computer Science, leading the Data Science Theory and Applications group since 2017. His work bridges Geometric Data Science with applications in Materials Innovation Factory , focusing on crystallography, structural biology, and computer vision. PhD in Geometry and Topology (Moscow State University, 2003) Developed Geometric Data Science for continuous parametrization of periodic crystals and protein structures Organizer of MACSMIN annual conference and LMS network since 2020 His research includes complete isometry invariants for point clouds, enabling polynomial-time algorithms for crystal structure prediction and protein backbone analysis. Key projects involve UKRI New Horizons grants , Royal Society APEX fellowships , and collaborations with Mila , Cambridge Crystallographic Data Centre , and Lawrence Berkeley National Lab . Scientific awards include the Royal Society APEX fellowship (2023), Royal Academy of Engineering Industry Fellowship (2021-2023), and Lecturer of the Year (Durham Student's Union, 2013). He has mentored 15+ PhD students, including Dr. Dan Widdowson and Dr. Olga Anosova, with joint publications in CVPR , NeurIPS , and Scientific Reports . His group's geometric invariants resolve data ambiguity in crystallography and structural biology, supported by grants from EPSRC , NERC , and Intel . Current projects include building geographic-style maps of the material space and organizing workshops at ICERM and SIAM meetings.
