3D Reconstruction and Understanding 4D Machine Perception for Real-World Applications From Detection to Use: Unlocking the Potential of Synthetic Images in Computer Vision From Pixels to Pursuit: Multi-Sensor Vision for Small Aerial Threats Full-Stack Robotics with VLAs: From Data Collection and Training to Edge Deployment Human Vision Multimodal Computer Vision Multimodal Foundation Models Multimodal Understanding of Social Interactions Self-Supervised Representation Learning Statistical Models of Shape and Appearance Uncertainty and Evaluation Video Understanding Vision for Robotics: Manipulation and Decentralised Learning
Session Topic: Human Vision
Dr Ulrik Beierholm is an Associate Professor in the Psychology Department at Durham University, working in Computational Neuroscience. His work crosses the border between neuroscience and computer science, by examining how computational models from machine learning can be used to build models of human perception, decision making, and action.
Session Topic: Statistical Models of Shape and Appearance
After completing a degree in Maths and Physics at Exeter University, and a PhD in Civil Engineering (studying a storm sewer overflow) at Sheffield City Polytechnic, Prof. Tim Cootes joined the University of Manchester in 1991. Prof. Tim Cootes began as an RA, working with Prof Chris Taylor on modelling industrial components. Prof. Tim Cootes was awarded an SERC Postgraduate fellowship in 1993, and an EPSRC Advanced Fellowship in 1995. Prof. Tim Cootes became a Lecturer in ISBE in June 2001, and was promoted to Senior Lecturer in October 2002. Prof. Tim Cootes became a Reader in Computer Vision in August 2005, and was appointed as a Professorial Research Fellow in August 2006. Prof. Cootes research has concentrated on constructing statistical models of the shape and appearance of objects in images, and in developing algorithms to match such models to new images. We have applied these models to many problems in the industrial and medical domains, and to the interpretation of facial images.
Session Topic: Self-Supervised Representation 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: From Detection to Use: Unlocking the Potential of Synthetic Images in Computer Vision
Prof. Victor Sanchez is the Head of the Signal and Information Processing (SIP) Lab of The University of Warwick and a Distinguished Investigator at Universitat Autonoma de Barcelona, Spain. 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 a Postdoctoral Researcher in the Video and Image Processing Laboratory at the University of California, Berkeley. 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 several agencies in North America, the EU, the UK, including the Defence and Security Accelerator, UK, 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 a Senior Editor of IEEE Signal Processing Letters and IEEE Transactions on Information Forensics and Security.
Session Topic: Multimodal Understanding of Social Interactions
Dr Tanaya Guha is a Senior Lecturer of Computing Science at University of Glasgow, where she is a founding member of the Social AI group. Her research focuses on developing socially intelligent systems that can recognize, model and generate human behaviour in social context combining Deep Learning, Computer Vision, and Signal/Speech Processing. She has published over 80 research articles, mostly in leading venues. She serves in the Editorial Boards of IEEE Transactions on Multimedia, Nature Scientific Reports and IEEE Pervasive Computing. She was a Program Chair for British Machine Vision Conference (BMVC) 2024 and ACM International Conference on Multimodal Interaction (ICMI) 2025.
Session Topic: 3D Reconstruction and Understanding
Krystian Mikolajczyk did his undergraduate study at the University of Science and Technology (AGH) in Krakow, Poland. He completed his PhD degree at the Institute National Polytechnique de Grenoble, France. He then worked as a research assistant in INRIA, University of Oxford and Technical University of Darmstadt (Germany), before joining the University of Surrey as a Lecturer, and Imperial College London as a Reader in 2015. His main area of expertise is in image and video recognition, in particular in problems related to matching, representation and learning. He participated in a number of EU and UK projects in the area of image and video analysis. He publishes in computer vision, pattern recognition and machine learning forums. He has served in various roles at major international conferences co-chairing British Machine Vision Conference 2012, 2017 and IEEE International Conference on Advanced Video and Signal-Based Surveillance 2013. In 2014 he received Longuet-Higgins Prize awarded by the Technical Committee on Pattern Analysis and Machine Intelligence of the IEEE Computer Society.
Session Topic: Uncertainty and Evaluation
Before moving back to UCL, Prof. Neill Campbell was a Professor in the Department of Computer Science at the University of Bath and a member of the Visual Computing and Artifical Intelligence and Machine Learning Research Groups. Prior to Bath, he 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 he 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. He completed my 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. He was funded by a Schiff Foundation Scholarship and Toshiba Research Europe.
Session Topic: Multimodal Computer Vision
Dr. Deblina Bhattacharjee is an interdisciplinary AI researcher, currently an Assistant Professor at the University of Bath. Her work bridges Computer Vision with Arts, developing AI-driven solutions for creative domains. Her research focuses on generative models, 3D reconstruction, multimodal large language models, depth estimation, visual saliency, and multitask learning, aiming to push the boundaries of AI integration into society. Beyond research, she is passionate about Cognitive Systems & AI Ethics, exploring how AI interacts with human cognition and understanding its ethical and societal impacts. Her goal is to develop AI systems that are responsible and inclusive. She has recently been awarded three prestigious fellowships: Perplexity AI Business Fellowship (international), Elevate Leadership Fellowship (UK-wide recognition) and Impact and Knowledge Exchange Fellowship.
Session Topic: Vision for Robotics: Manipulation and Decentralised Learning
Professor Xianghua Xie is currently leading a research team on Computer Vision and Machine Learning (http://csvision.swan.ac.uk) in the Department of Computer Science, Swansea University. He was a recipient of an RCUK Academic Fellowship (tenure-track research focused lectureship) between September 2007 and March 2012. He was appointed as a Senior Lecturer from October 2012, then an Associate Professor in April 2013, and a full Professor from March 2019. Prior to his position at Swansea, He was a Research Associate at the Computer Vision Group, Department of Computer Science, University of Bristol, where he completed both his PhD (2006) and MSc (2002) degrees. Professor Xie has strong research interests in the areas of Pattern Recognition and Machine Intelligence and their applications to real-world problems. He has been an investigator on several research projects funded by external bodies, such as EPSRC, Leverhulme, NISCHR, and WORD. Among his research works, those of significant importance include detecting abnormal patterns in complex visual and medical data, assisted diagnosis using automated image analysis, fully automated volumetric image segmentation, registration, and motion analysis, machine understanding of human action, efficient deep learning, and deep learning on irregular domains. By 2020, he has published over 150 fully refereed research publications and (co-)edited several conference proceedings. He is an associate editor of IET Computer Vision and an editorial member of a number of other international journals and has chaired and co-chaired several international conferences, e.g. BMVC2015 and BMVC2019.
Session Topic: Video Understanding
Dr. Andrew Gilbert is an Associate Professor at the University of Surrey, specializing in computer vision and machine learning. He has over 15 years of experience leading research on self-supervised video understanding, 3D human pose estimation, and human-centric generative AI, and has authored more than 60 papers in venues such as CVPR, ICCV, ECCV, and IJCV. His recent work focuses on interpretable human-inspired video understanding, long-form video captioning, and human image generation. Andrew is passionate about mentoring the next generation of researchers and fostering interdisciplinary collaborations across creative arts, robotics, and AI.
Session Topic: 4D Machine Perception for Real-World Applications
Dr Armin Mustafa is an Associate Professor in Computer Vision and AI at CVSSP, University of Surrey and an AI Fellow and Department of Science. Innovation and Technology (DSIT), UK. Previously she 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. Her 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: From Pixels to Pursuit: Multi-Sensor Vision for Small Aerial Threats
Dr. Oliver Hamilton is the Head of Intelligence and Autonomy at OpenWorks Engineering, where he focuses on pioneering technologies for counter-unmanned aerial systems (C-UAS) and air defense. His expertise lies in developing innovative solutions that enable real-time detection, tracking, and effective mitigation of emerging aerial threats. Before joining OpenWorks, Dr. Hamilton held the role of Product Owner for Geti at Intel, overseeing its evolution and development. Earlier in his career, he co-founded COSMONiO, where he played a pivotal role in building the initial version of the NOUS platform. Through Intel's acquisition of COSMONiO, the NOUS platform was further refined and evolved into Geti.
Session Topic: Full-Stack Robotics with VLAs: From Data Collection and Training to Edge Deployment
Dr Samet Akcay is an AI Research Engineer/Scientist at Intel, specifically working on self-supervised anomaly detection and localization for industrial, medical and security applications. His primary research interests are real-time image classification, detection, anomaly detection, and unsupervised feature learning via deep/machine learning algorithms. We recently open-sourced anomalib, one of the largest anomaly detection libraries in the field. Prior to joining Intel, he worked as a Deep Learning Engineer at COSMONiO where hedesigned and developed the self-supervised anomaly classification, detection, and segmentation in NOUS, world's first interactive deep learning box for subject matter experts.
Session Topic: Multimodal Foundation Models
Dr. Cheng Zhang is a Director of Research at the Ellison Institute of Technology (EIT), focusing on multimodal foundation models for a range of applications with social impact. She has over a decade of experience in GenAI and machine learning, spanning cutting-edge research and research-to-product translation in collaboration with cross-functional teams. Before joining EIT, Dr. Zhang served as a Research Manager in Meta’s Superintelligence Lab (formerly the GenAI/LLaMA team), leading the EMEA team on reasoning and agent research. Prior to that, she held Technical Lead Research Manager role at Microsoft Research Cambridge for over 6.5 years and conducted research at Disney Research, Carnegie Mellon University, before that.