4D Computer Vision in the wild Classical Computer Vision Computer Vision in Robotics and SLAM Contrastive Language-Image Pretraining (CLIP) for Video Analytics Efficient Models for Computer Vision Egocentric Vision - Making Sense of the First-Person Perspective Graph and Geometric Computer Vision Human Vs Machine Vision Hyperbolic and Hyperspherical Learning Medical Imaging Multimodal Behaviour Understanding and Generation for Human-Robot Interaction Python (Pytorch) for Computer Vision Self-Supervised Learning Structured Generative models for Computer Vision Uncertainty and Evaluation in Vision Video Understanding
Session Topic: Human Vs Machine Vision
Iris Groen is an Assistant Professor (tenured) and MacGillavry Fellow at the Video & Image Sense Lab at the Informatics Institute at the University of Amsterdam (UvA). She is also affiliated with the Department of Brain and Cognition at the Psychology Research Institute at the UvA. Iris studies vision in the human brain using brain imaging techniques such as EEG, fMRI and ECoG, in combination with computational models, including deep neural networks. The goal of her research is to find out how the human brain perceives and understands real-world images and videos.
Session Topic: Egocentric Vision - Making Sense of the First-Person Perspective
Dr Michael Wray is a lecturer/Assistant Professor of Computer Vision at the School of Computer Science at the University of Bristol. Dr Wray's research interests are in multi-modal video understanding, particularly for egocentric videos — focusing on how both vision and language can be tied together towards tasks such as cross-modal retrieval, grounding and captioning. Dr Wray is part of MaVi and ViLab.
Session Topic: Contrastive Language-Image Pretraining (CLIP) for Video Analytics
Prof. Victor Sanchez is the Head of the Signal and Information Processing (SIP) Lab of The University of Warwick. He received an M.Sc. degree from the University of Alberta, Canada, in 2003, and a Ph.D. degree from The University of British Columbia, Canada, in 2010. From 2011 to 2012, he was with the Video and Image Processing Laboratory, at the University of California at Berkeley, as a Postdoctoral Researcher. In 2012, he was a Visiting Lecturer with the Group on Interactive Coding of Images, Universitat Autònoma de Barcelona. From 2018 to 2019, he was a Visiting Scholar with the School of Electrical and Information Engineering, The University of Sydney, Australia. His research interests include computer vision with applications to multimedia analysis, biometrics, forensics, and security. He has authored several technical articles and book chapters in these areas. His research has been funded by the Newton Fund; the Natural Sciences and Engineering Research Council of Canada; the Canadian Institutes of Health Research; the FP7 and the H2020 Programs of the European Union; the Engineering and Physical Sciences Research Council, U.K; Ford Motor Company, USA, the Defence and Security Accelerator, U.K., and Research England. He is the Chair of the Technical Committee on Computational Forensics under the auspices of the International Association for Pattern Recognition (IAPR). He currently serves as an associate editor of IEEE Signal Processing Letters, IEEE Access, and ACM Computing Surveys.
Session Topic: Video Understanding
Dr Andrew Gilbert is an Associate Professor at the University of Surrey. His academic pursuits are primarily focused on video understanding and Generative Models. His research portfolio comprises over 65 articles published in the leading international vision conferences and journals, and he co-leads the C-CATS research group at Surrey. Dr Gilbert's extensive research work ranges from intelligent creative arts, such as fine-grained style search, movie trailer genre understanding, and 4D performance capture, to enabling computers to perceive and understand their complex and cluttered surroundings using multiple training techniques, including self-supervised and multiple data modes. Previously, his research work encompassed 3D human pose estimation and complex real-world activity recognition, with early work on tracking people on vast surveillance networks. Moreover, Dr Gilbert is an active British Machine Vision Association (BMVA) Executive Committee member and coordinates the national BMVA technical meetings. These meetings offer a forum for key experts from industry and academia to discuss and identify solutions to current problems in specialist areas of computer vision and machine learning.
Session Topic: Self-Supervised Learning
Dr Oisin Mac Aodha is a Reader (aka Associate Professor) in Machine Learning in the School of Informatics at the University of Edinburgh (UoE). He was a Turing Fellow from 2021 to 2024, currently am an ELLIS Scholar, and a founder of the Turing interest group on biodiversity monitoring and forecasting. Oisin’s current research interests are in the areas of computer vision and machine learning, with an specific emphasis on 3D understanding, human-in-the-loop methods, and AI for conservation and biodiversity monitoring. From 2016-2019 Oisin was fortunate to be a postdoc in Prof. Pietro Perona's Computational Vision Lab at Caltech working with the Visipedia team. Previous to Caltech, he spent three great years (2013-2016) as a postdoc in the Department of Computer Science at University College London (UCL) with Prof. Gabriel Brostow and Prof. Kate Jones. There Oisin worked on interactive machine learning, where our goal was to design algorithms to enable non-programming scientists to semi-automatically explore events of interest in vast quantities of audio and visual data.
Session Topic: Uncertainty and Evaluation in Vision
Before moving to Bath Prof. Neill Campbell was a Research Associate in the Virtual Environments and Computer Graphics Group at University College London working with Jan Kautz and Simon Prince on synthesizing and editing photorealistic visual objects funded by the EPSRC.
Previously Prof. Campbell was a Research Associate in the Computer Vision Group of the Machine Intelligence Laboratory, in the Department of Engineering at the University of Cambridge working on the EU Hydrosys Project led by Ed Rosten.
Prof. Neill Campbell completed his PhD, in the Computer Vision Group at the University of Cambridge, under the supervision of Roberto Cipolla and the guidance of George Vogiatzis and Carlos Hernández. Prof. Neill Campbell was funded by a Schiff Foundation Scholarship and Toshiba Research Europe.
Session Topic: Python (Pytorch) for Computer Vision
Before moving to Bath Prof. Neill Campbell was a Research Associate in the Virtual Environments and Computer Graphics Group at University College London working with Jan Kautz and Simon Prince on synthesizing and editing photorealistic visual objects funded by the EPSRC.
Previously Prof. Campbell was a Research Associate in the Computer Vision Group of the Machine Intelligence Laboratory, in the Department of Engineering at the University of Cambridge working on the EU Hydrosys Project led by Ed Rosten.
Prof. Neill Campbell completed his PhD, in the Computer Vision Group at the University of Cambridge, under the supervision of Roberto Cipolla and the guidance of George Vogiatzis and Carlos Hernández. Prof. Neill Campbell was funded by a Schiff Foundation Scholarship and Toshiba Research Europe.
Session Topic: Structured Generative models for Computer Vision
Dr Paul Henderson is a Lecturer (aka Assistant Professor) in Machine Learning at the University of Glasgow. His research focuses on building machines that understand the visual world with minimal supervision, learning aspects of its structure such as 3D geometry and decomposition into objects. This work draws on techniques from machine learning (particularly deep generative models), computer vision, and computer graphics.
Session Topic: Efficient Models for Computer Vision
Dr Elliot J. Crowley is a Senior Lecturer (Associate Professor) at the School of Engineering, University of Edinburgh. He co-leads the Bayesian and Neural Systems research group and is broadly interested in simple, efficient, and automated machine learning algorithms. Dr Crowley holds an EPSRC New Investigator Award and is an investigator on the DAIEdge Horizon Network. He obtained his Doctorate in Computer Vision from the University of Oxford.
Session Topic: Computer Vision in Robotics and SLAM
Dr Mike Mangan is currently VP of Research at Opteran – a UK deep-tech pioneering the application of natural intelligence solutions to create machine minds that are both effective and efficient allowing deployment across markets. Dr Mangan’s is recipient of a UKRI Future Leader Fellowship, and his commercial research is also supported by external partners including the European Space Agency. Dr Mangan also holds an academic position at the University of Sheffield, UK where he is Senior Lecturer in Machine Learning and Robotics, and member of Sheffield Robotics. Dr Mangan has received formal training in engineering (MEng in Avoinics, University of Glasgow, 2004), data science (MSc in Neuroinformatics, University of Edinburgh, 2006), biorobotics (PhD in Biorobotics, University of Edinburgh, 2011), and his research has been published across disciplines from neuroscience (Current Biology) to robotics (Science Robotics).
Session Topic: 4D Computer Vision in the wild
I am an Associate Professor in Computer Vision and AI at CVSSP, University of Surrey. Previously I held a Royal Academy of Engineering Research Fellowship, working in 4D Vision for perceptive machines. The emergence of machines that interact with their environment has led to an increasing demand for automatic visual understanding of real-world scenes. My research exploits Artificial Intelligence (AI) to better understand complex scenes so that machines can efficiently model and interpret real-world for a range of socially beneficial applications including autonomous systems, augmented reality and healthcare.
Session Topic: Graph and Geometric Computer Vision
Dr Tolga Birdal is an Assistant Professor at Imperial College London. UKRI Future Leaders Fellow. Previously StanfordAILab and TUMunich. Building an artificial visual cortex for spatial intelligence through geometry and topology by day. Indulging in music and philosophy by night. As a computer scientist and PI of the CIRCLE group, Tolga strives to understand visual perception and build machines that can see. To do so, he leverages tools from differential geometry and algebraic topology, in conjunction with deep neural networks. My research focuses on: 3D Computer Vision; 3D/4D Generative Priors; Geometric/Topological Deep Learning; Statistical and Topological Learning Theory; Quantum Computer Vision. As a science advocate, Tolga is committed to breaking down barriers to education, making knowledge accessible to all—especially those from underrepresented and underprivileged communities. Through initiatives like Bilimler Koyu, Nesin Maths Village, Arkhé and Poedat, he actively contributes to this mission. He envisions the possibility of an alternative, transformative academia. As an entrepreneur, Tolga has co-founded multiple startups, including BeFunky, an online photo editing platform that empowers creativity worldwide.
Session Topic: Medical Imaging
Dr. Binod Bhattarai is a Lecturer (US equivalent: Assistant Professor) at the University of Aberdeen, UK, and an Honorary Lecturer at University College London, specializing in machine learning, computer vision, and medical image analysis. His research focuses on developing advanced algorithms for processing multimodal data, with applications in healthcare and beyond. Before this, Dr. Bhattarai gained extensive post-doctoral experience as a Senior Research Fellow at University College London and a Postdoctoral Research Associate at Imperial College London,UK. He earned his PhD from Université de Caen, France. He is actively involved in the academic community, serving as Program Chair for MIUA 2025, Program Chair for BMVC 2023, and Chair of DEMI Workshop at MICCAI 2023 and 2024. His publications span top-tier conferences and journals, and he has received numerous awards, including the Google Cloud Research Innovator Award, Outstanding Reviewer at BMVC 2019 and BMVC 2021, Winner of MICCAI 2021 Challenge, and Best Paper and Runner-Up awards.
Session Topic: Hyperbolic and Hyperspherical Learning
Dr Pascal Mettes is an Assistant Professor at the University of Amsterdam. Pascal and his team focus on deep learning in hyperbolic space. Within deep learning, we tend to question all aspects of training neural networks, from architectures and optimization to data and tricks. The most fundamental assumption, namely to operate in Euclidean space, is however rarely questioned. We believe that opening our scope beyond Euclid opens an entirely new worlds for deep learning. Specifically, Pascal focuses on hyperbolic deep learning. Learning in hyperbolic space enables us to learn hierarchical representations, with stronger robustness (with respect to OOD and adversarial samples), more compactness, and with the possibility of incorporating prior knowledge. His team also considers it the native space for vision-language models. In Pascal’s lab, they are therefore spearheading hyperbolic deep learning through algorithmic advances, open-source developments, and international workshops, tutorials, and talks.
Session Topic: Multimodal Behaviour Understanding and Generation for Human-Robot Interaction
Dr Oya Celiktutan is a Reader in AI & Robotics at the Centre for Robotics Research in the Department of Engineering and leads the Social AI & Robotics Laboratory. She received a BSc degree in Electronics Engineering from Uludag University, and an MSc and PhD degree in Electrical and Electronics Engineering from Bogazici University, Turkiye. During her doctoral studies, she was a visiting researcher at the National Institute of Applied Sciences of Lyon, France. After completing her PhD, she moved to the United Kingdom and worked on several projects as a postdoctoral researcher at Queen Mary University London, the University of Cambridge, and Imperial College London, respectively. Oya’s research focuses on multimodal machine learning to develop autonomous agents, such as robots and virtual agents, capable of seamlessly interacting with humans. This encompasses tackling challenges in multimodal perception, understanding and forecasting human behaviour, as well as advancing the navigation, manipulation, and social awareness skills of these agents. Her work has been supported by EPSRC, The Royal Society, and the EU Horizon, as well as through industrial collaborations. She received the EPSRC New Investigator Award in 2020. Her team’s research has been recognised with several awards, including the Best Paper Award at IEEE Ro-Man 2022, NVIDIA CCS Best Student Paper Award Runner Up at IEEE FG 2021, First Place Award and Honourable Mention Award at ICCV UDIVA Challenge 2021.