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.
Catherine Lai is a Reader (~Associate Professor) in the Department of Linguistics and English Language at the University of Edinburgh, with strong affiliations to the Centre for Speech Technology Research (CSTR) and the Institute for Language, Cognition and Computation (ILCC) in the School of Informatics. She is based in the School of Philosophy, Psychology and Language Sciences and is actively involved in research, teaching, and academic service. Department: Department of Linguistics and English Language School: School of Philosophy, Psychology and Language Sciences Research Institutes: Centre for Speech Technology Research, Institute for Language, Cognition and Computation Email: C.Lai@ed.ac.uk Her research centers on the role of prosody—non-lexical aspects of speech—in spoken communication. She investigates how prosody contributes to discourse structure, information structure, and affect in dialogue, using interdisciplinary methods from linguistics and machine learning. Her work bridges theoretical linguistics and practical speech technology, aiming to improve spoken language understanding and synthesis systems. She is particularly interested in how prosody shapes listener expectations and how affect and topic are expressed and perceived in conversation. Her recent publications reflect a strong focus on self-supervised learning in speech models, emotion recognition, ASR error correction using large language models, cognitive state classification, and ethical considerations in language technology. She explores topics such as the uncanny valley in synthetic speech, gender expression through voice, and community-centered development of language technologies. Prize from Scopus Profile Catherine Lai has supervised several PhD students, including Leimin Tian and Yuanchao Li, and has been involved in significant research projects, such as a Toyota-funded initiative on spoken dialogue for robot companions. She has secured multiple grants and leads a research agenda that integrates theoretical inquiry with real-world applications in assistive technologies and social science. Her academic service includes organizing major conferences like Interspeech and UK and Ireland Speech. She is a key member of research teams at CSTR and ILCC, collaborating across disciplines to advance the understanding of spoken communication and the development of robust, ethical speech technologies.
Timothy M. Hospedales is a Professor of Artificial Intelligence at the Institute of Perception, Action and Behaviour within the School of Informatics at the University of Edinburgh . He also serves as VP AI and Head of Samsung AI Research Centre Europe . His research focuses on efficient and robust AI , emphasizing meta-learning , lifelong transfer-learning , and domain adaptation in both probabilistic and deep learning frameworks. Applications span computer vision , vision and language , reinforcement learning for robotics , and finance . Professor at University of Edinburgh (2020–present) ELLIS Fellow (2021) Head of Samsung AI Research Europe (2020–present) Founding Director of Applied Machine Learning Lab at QMUL (2012–2016) His work includes pioneering contributions to meta-learning , few-shot learning , and self-supervised methods , with notable awards such as the Best Paper Prize at ICML AutoML 2018 and Best Student Paper at ICPR 2018 . He has co-authored 15+ recent papers on topics like Vision-Language Models , Medical AI Fairness , and Diffusion Model Optimization . He served as Program Co-Chair for BMVC 2018 and AAAI 2022 , and authored a book on Visual Adaptation in the Deep Learning Era (2022). Co-Chair, BMVC 2018 Guest Editor, IET CV Special Issue (2016) Keynote Speaker at TASK-CV Workshop (ECCV 2016) Special Issue on Fewer Labels (IEEE PAMI 2020) His leadership extends to organizing workshops like the Learning-to-Learn Workshop at ICLR 2021 , Meta-Learning Workshop at NeurIPS 2020 , and Domain Generalisation Workshop at ICLR 2023 . Current projects include Meta-Omnium (CVPR 2023) for general-purpose meta-learning and MetaAudio (ICANN 2022) for few-shot audio classification benchmarks.
Dr. Heather Inwood serves as University Associate Professor in Modern Chinese Literature and Culture within the Faculty of Asian and Middle Eastern Studies at the University of Cambridge. She is also a Fellow and Director of Studies at Trinity Hall, maintaining dual institutional affiliations that support her teaching and research activities across undergraduate and postgraduate programs. Her educational background includes a BA in Chinese Studies from the University of Cambridge (Trinity Hall), followed by advanced studies at Tsinghua University and Peking University, culminating in a PhD in modern Chinese literature from SOAS, University of London in 2008. Prior academic appointments include Assistant Professor at The Ohio State University (2008-2013) and Lecturer in Chinese Cultural Studies at the University of Manchester before returning to Cambridge in 2016. Professor Inwood's research program investigates the dynamic interactions between digital media and literary production in contemporary China, with particular focus on poetry communities, genre fiction, and transmedia storytelling. Her work bridges traditional literary analysis with digital humanities approaches, examining how online platforms transform authorship, reception, and cultural value. She has pioneered scholarship on internet poetry scenes, demonstrating their vitality despite public perceptions of poetry's decline in modern China. Her publication trajectory reveals a clear evolution from early work on poetry communities ( Verse Going Viral: China's New Media Scenes , 2014) toward broader investigations of digital narrative forms, game studies, and Sinophone cyberspace. Recent publications increasingly engage with science fiction, transmedia storytelling, and the algorithmic structures shaping literary production, reflecting both the changing digital landscape and her expanding methodological toolkit. Professor Inwood actively supervises five PhD students working on diverse projects spanning Misty poetry, queer narratives, gender studies, and poetic animality, demonstrating her commitment to nurturing next-generation scholarship in Chinese literary studies. Her research has been supported by British Academy Small Grants and similar funding mechanisms that enable sustained fieldwork and publication. Her teaching responsibilities include undergraduate courses on East Asian media and popular culture, Modern Chinese texts, and Modern Chinese literature, connecting classroom instruction directly to her research expertise. She also maintains public engagement through Chinese-language columns for BBC China and other media outlets, bridging academic and public discourse on contemporary Chinese culture.
Oya Celiktutan is a Reader (Associate Professor) at King’s College London in the Department of Engineering, leading the Social AI & Robotics (SAIR) Lab within the Centre for Robotics Research. She holds a BSc in Electronics Engineering from Uludag University, and an MSc and PhD in Electrical and Electronics Engineering from Bogazici University, Turkey. Her doctoral work included a visiting research period at the National Institute of Applied Sciences in Lyon, France. She has held postdoctoral positions at Queen Mary University London, the University of Cambridge, and Imperial College London before joining King’s College in 2018. Her research focuses on multimodal machine learning for autonomous agents, addressing challenges in human-robot interaction, social awareness, and navigation. Key areas include human behavior analysis, generative models, and continual learning. Her work is supported by EPSRC, The Royal Society, EU Horizon, and industry partners like Toyota and NVIDIA. Notable awards include the EPSRC New Investigator Award (2021) and a Best Paper Award at IEEE Ro-Man 2022. Dr. Celiktutan’s lab explores socially assistive robotics, extended reality systems, and ethical AI. Recent projects include the LISI initiative to enable robots to learn social interactions from human-human data and the CL-HRI project advancing continual learning in human-centric robotics. Her research has led to datasets like MHHRI and RICA, and collaborations with companies like SoftBank Robotics. She teaches modules on Sensing and Perception, and Sensors and Actuators, emphasizing practical applications in robotics and mechatronics. Current research interests span socially-aware navigation, algorithmic fairness, and multimodal data fusion for robotics in healthcare and urban environments.
Zhenhong Li is a Lecturer in Robotics and Control at the University of Manchester, holding an EPSRC Fellowship in physical human-robot interaction. He earned his B.Eng. from Huazhong University of Science and Technology (2013), and M.Sc. and Ph.D. in Control Engineering from the University of Manchester (2014 and 2019). Before joining Manchester in 2023, he was a Research Fellow in Rehabilitation Robotics at the University of Leeds (2019–2023). His research focuses on control technologies for human-robot systems, with applications in healthcare and industry. Key areas include physical human-robot interaction for rehabilitation, brain-computer interfaces, and neuromusculoskeletal modeling. He leads the Neurorobotics Lab (NRL) at Manchester and collaborates with healthcare professionals, industries, and designers via EPSRC/STFC/Wellcome Trust funding. Notable achievements include the 2019 Best Paper Award for Unmanned Systems and the 2020 EPS International Academic Pump-priming Award. In 2025, he was elected as a Senior Member of the IEEE. He actively organizes conferences and special issues, including the 2025 IEEE UK Robotics Conference and a Frontiers special issue on intelligent rehabilitation technology. Dr. Li supervises PhD candidates in robotics and control, emphasizing interdisciplinary approaches to human-robot interaction. His lab develops cutting-edge technologies like assistive exoskeletons and adaptive control systems for healthcare and industrial applications.
Dr. Tan Viet Tuyen Nguyen is a New Frontiers Fellow (Lecturer) in AI at the University of Southampton, specializing in Human-Centered Artificial Intelligence and Social Human-Robot Interaction. His research focuses on multimodal learning for robots to adapt their behavior to human social needs, with applications in healthcare, education, and service environments. Prior to this role, he was a Research Associate at King’s College London and a Research Assistant on the EU-funded CARESSES project, developing culturally-aware assistive robots for elderly support. Education: PhD in Information Science (Robotics) from Japan Advanced Institute of Science and Technology. He has organized conferences such as the IEEE RO-MAN 2022 special session on nonverbal communication and served as a reviewer for top-tier robotics and AI conferences. Research Interests include: Human-Robot Collaboration, Multimodal Perception, Generative AI for Social Interaction, and Context-Aware Robot Behavior Generation. His work has been recognized with awards including the Best Paper Award at ROMAN 2022 and the Prospective Research Award at ICServ 2023. Teaching Responsibilities include courses on Biologically Inspired Robotics, High-Level Programming, and MSc/Undergraduate project supervision. He currently oversees two PhD students and collaborates on projects like 'Exploring the impact of AI-driven writing of engagement in climate change' and 'Bridging Generations and Cultures through Generative AI.' Labs/Teams: Member of the Agents, Interaction and Complexity Centre and the Centre for Robotics Research at Southampton.
Fethiye Irmak Dogan is a Postdoctoral Research Associate at the University of Cambridge's Department of Computer Science and Technology, working in the Affective Intelligence and Robotics Laboratory. She holds a Ph.D. in Computer Science from KTH Royal Institute of Technology (2023), an M.Sc. and B.Sc. in Computer Engineering from Middle East Technical University (METU). Her research focuses on human-robot interaction, continual learning, and socially appropriate robot behaviors leveraging explainability. She has conducted robotics research at KTH's Division of Robotics, Perception and Learning and collaborated internationally, including a visiting scholar stint at Georgia Institute of Technology. Education highlights include: B.Sc., Computer Engineering, METU (2015) M.Sc., Computer Engineering, METU (2018), with research at Kovan Robotics Lab Ph.D., Computer Science, KTH (2023), with visiting research at Georgia Tech Research interests emphasize deploying autonomous robots in human environments, resolving ambiguous user instructions through explainability, and enabling socially intelligent robot behaviors. Recent work explores continual learning for context adaptation, multimodal frameworks for human-robot collaboration, and vision-language models for wellbeing assessment in children. Key projects include BT-ACTION (modular instruction understanding), GRACE (LLM-driven socially appropriate actions), and STREAK (continual learning for household tasks). Her contributions span robotics, AI ethics, and human-centered design, with a focus on real-world applications in healthcare and education.
Dr. Hyung Jin Chang is an Associate Professor at the School of Computer Science, University of Birmingham, and a Turing Fellow at the Alan Turing Institute. He holds a Ph.D. and B.S. from Seoul National University. His research focuses on human-centered visual learning, particularly in human-robot interaction, with expertise in computer vision, machine learning, and deep learning. He has been involved in organizing conferences like ECCV and ICCV workshops (e.g., VOTS Challenge, HANDS Workshop) and serves on program committees including AAAI and CVPR. His work spans areas like gaze estimation, domain adaptation, 3D pose estimation, and robotic perception for assistive technologies. Key achievements include receiving the Royal Society Research Grant (2019–2020) and Wellcome Trust funding. Notable contributions include frameworks for unsupervised domain adaptation, gaze estimation models (e.g., RT-Gene), and collaborative learning methods for hand-object reconstruction. He has led projects in medical robotics, personalized dressing assistance, and safety-critical systems like driver attention prediction. His 15 most recent articles (2024–2025) emphasize advancements in diffusion models, domain adaptation, 3D reconstruction, gaze-controllable systems, and generative AI for motion and interaction modeling. These reflect a trend toward integrating multimodal data (vision + language) and bridging theoretical foundations with applied robotics. Awards: Royal Society Grant, Wellcome Trust, Turing Fellowship Grants: Active in securing funding for robotics, vision, and healthcare applications He leads the Personal Robotics Lab and collaborates on projects like the VOTS Challenge for visual object tracking. His research bridges academia and real-world applications in healthcare robotics and human-technology interaction.
Professor Bing Chu is an academic at the University of Southampton, actively contributing to research in control systems, robotics, and machine learning. They are a member of the Vision, Learning and Control Centre for Internet of Things and Pervasive Systems and the Centre for Robotics, focusing on interdisciplinary approaches that combine control theory with data-driven methodologies. Current research interests include: Iterative learning control Human-robot interaction Wind farm power optimization Robot behavior modeling Control system architectures Collaborative learning systems Recent publications highlight trends in data-driven control systems, human-robot interaction datasets, and optimization techniques for both continuous-time systems and wind energy applications. Professor Chu supervises multiple PhD students across robotics and electronic engineering, including Balint Gucsi, Haonan Shen, and Aleksander Wolski, while leading projects funded by Zhengzhou University and the Royal Society.
Dr. Lin Wang is a Lecturer in Applied Data Science and Signal Processing at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science. He leads the Machine Listening Lab and is a member of the Centre for Multimodal AI, Centre for Intelligent Sensing (CIS), and Institute of Coding (IoC). His research focuses on audio-visual signal processing, robotic perception, and machine learning, with applications in healthcare, drone-based sensing, and human activity recognition. Dr. Wang holds a PhD from Dalian University of Technology and has held postdoctoral positions at QMUL, the University of Sussex, and the Alexander von Humboldt Foundation in Germany. Education and Roles: PhD in Signal Processing, Dalian University of Technology (2010) Postdoc at Queen Mary University of London (2014–2017) Postdoc at University of Sussex (2017–2018) Alexander von Humboldt Fellow at University of Oldenburg (2011–2013) Fellow of the Higher Education Academy (UK) Research Interests: Audio-visual signal processing for drones and wearable devices Machine listening and robotic perception Machine learning for healthcare and environmental monitoring Human activity recognition using multimodal sensors Awards and Grants: Early Career Champion on AI&Data, UK Acoustics Network Outstanding Article Award, Frontiers in Computer Science (2022) EPSRC grant: Bioacoustic Monitoring Using Drones (£46,821, 2022–2023) Innovate UK grant: Music Source Separation (£48,144, 2024–2025) Teaching and Students: Dr. Wang teaches Applied Statistics , Website Design and Authoring , and Machine Learning for Visual Data Analysis . He supervises PhD students including Ashish Alex (speech separation), Michael Clayton (drone audition), and Dmitrii Mukhutdinov (audio-visual processing). Labs and Teams: He co-leads the Machine Listening Lab and is part of the Centre for Multimodal AI, focusing on interdisciplinary projects in robotics, acoustics, and AI.
Dr. Guangliang Cheng is an Associate Professor in the Department of Computer Science at the University of Liverpool. His research focuses on deep learning, computer vision, and perception algorithms with applications in remote sensing, medical imaging, and autonomous systems. Prior to his current role, he served as a vice research director in the Autonomous Driving Group at SenseTime and completed postdoctoral research at the Aerospace Information Research Institute, Chinese Academy of Sciences. Ph.D. in Pattern Recognition from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) Postdoctoral Researcher at Aerospace Information Research Institute, Chinese Academy of Sciences (2017–2019) Dr. Cheng’s research integrates computer vision and deep learning to address challenges in semantic segmentation, domain adaptation, and robust detection. Recent work explores wavelet-based multimodal fusion for remote sensing and attention-guided architectures for medical imaging. His 2025 publications span journals like GIScience & Remote Sensing and Knowledge-Based Systems , emphasizing scalable solutions for geospatial and biomedical applications. In 2025, Dr. Cheng’s article trends highlight remote sensing semantic segmentation, cross-domain medical imaging, and drone-based fire detection. His collaborations span institutions such as SenseTime, Chinese Academy of Sciences, and University of Liverpool teams, focusing on frequency-domain fusion, attention mechanisms, and GPU optimization. As a supervisor, Dr. Cheng seeks highly motivated PhD students to join projects supported by scholarships including the Centres for Doctoral Training (CDT) and Duncan Norman Scholarship. He serves as Module Co-ordinator for COMP338: Computer Vision (2024–2025) and actively reviews for top-tier journals and conferences.
Dr. Francesca Carbone is a Lecturer in Social and Developmental Psychology at the School of Psychology, University of Kent. She completed her PhD at the University of Naples "L'Orientale" studying emotional processing through prosody and semantics, followed by postdoctoral fellowships at CNRS (Aix-Marseille University) and University of Kent. Education: PhD in Psychology (University of Naples "L'Orientale") Her research focuses on cognitive, emotional, and psychophysiological mechanisms underlying art reception and emotional communication. Current projects examine physiological speech production mechanisms and how facial expressions mediate emotional prosody perception. She also investigates film's cognitive effects on open-mindedness. Recent publications in Language Resources & Evaluation , Motivation and Emotion , and Quarterly Journal of Experimental Psychology demonstrate expertise in emotional semantics, prosody analysis, and media effects. Publications span experimental studies on speech interaction, film cognition, and multimodal emotion perception. Teaching responsibilities include module convenorship and instruction in advanced developmental psychopathology, child development, cognitive development, and psychology fundamentals.
Professor Tony McEnery is a Distinguished Professor of English Language and Linguistics at Lancaster University, where he has held various senior roles, including Dean of the Faculty of Arts and Social Sciences (2008–2014) and Head of the Department of Linguistics and English Language (2000–2005). His research focuses on corpus linguistics, applied linguistics, and sociolinguistics, with notable contributions to corpus-based analyses of historical medical discourse, media representation of Islam, and swearing in English. He has led major research projects, including the ESRC Centre for Corpus Approaches to Social Science (CASS) and the Trinity Lancaster Corpus. McEnery has secured over £10 million in research funding and holds visiting professorships at institutions worldwide, including Zhejiang University of Media and Communications and Shanghai International Studies University. He is a Fellow of the Academy of Social Sciences, Trinity College London, and the Royal Society for the Arts. Education: No explicit details provided, but his career trajectory implies advanced degrees in linguistics. Research Interests: Corpus linguistics applications in theoretical and applied linguistics, historical language analysis, and language as a socially situated phenomenon. McEnery’s work extends to interdisciplinary collaborations, including projects with the Environment Agency and the Home Office. His teaching includes summer schools in corpus linguistics and a Futurelearn MOOC, reaching tens of thousands globally. He advises on research funding for international bodies like the Irish Research Council and the Qatar National Research Foundation. Grants and Awards: Queen’s Anniversary Prize (2015), Changjiang Chair (2021), and multiple fellowships. Current grants include the Lancaster-Northern Arizona corpus and the Trinity Lancaster Corpus projects. Advising and Labs: Supervises postgraduate students in linguistics and collaborates with teams like the ESRC CASS Centre and UCREL (University Centre for Computer Corpus Research on Language).
Dr. João Henriques is a Research Fellow of the Royal Academy of Engineering (RAEng) at the Visual Geometry Group (VGG), University of Oxford. His research focuses on advancing computer vision, deep learning, and robotics, particularly in areas like 3D scene understanding, reinforcement learning, and multi-agent systems. He is renowned for developing the KCF and SiameseFC visual trackers, which won the VOT Challenge and are deployed in consumer hardware. His work spans 3D geometry, self-supervised learning, causal inference, and neuro-symbolic systems. Key contributions include methods for egocentric video analysis, unsupervised reconstruction, and robot navigation. He leads the VGG's research on neural feature fields, hierarchical scene understanding, and real-time 3D perception. Recent publications emphasize 3D-aware segmentation, universal place recognition, and neuro-symbolic world modeling for robotics. His research often bridges theoretical guarantees with practical applications, such as medical imaging and autonomous systems. Dr. Henriques collaborates with industry and academia on AI ethics, friendly AI, and interpretable learning. His lab hosts DPhil students advancing creative AI applications, such as generative models for gameplay design and LLM evaluations in real-world editorial workflows.
