جست‌وجوی استاد و پژوهشگر

Roger Clarkeمشاهده پروفایل

مدرس ارشد

• Sculpture
• Contemporary Art
• Scientific Visualisation
• +۴ مورد دیگر

Roger Clarke is a Senior Lecturer in Fine Art at Bath Spa University's Bath School of Art, Film and Media, with over twenty years of professional experience as both a practicing artist and educator. His career spans significant exhibitions across the UK and internationally, including venues such as the Henry Moore Institute in Leeds and the Venice Biennale. His academic credentials include: BA(Hons) in Fine Art from the Slade School of Fine Art, University College London MA from London Metropolitan University Clarke's research centers on scientific visualisation and computerised fabrication processes , exploring intersections between art and science through sculptures based on molecular structures transformed into anthropomorphic forms inspired by Japanese anime. His fascination with scientific models for representing invisible phenomena culminated in works like A View of a Non-visible World (2005), which utilized Mount St Helens eruption data. His practice frequently incorporates kinetic elements, as demonstrated by the Record Player Orchestra series, merging sound, movement, and visual aesthetics through innovative use of industrial materials and analog technologies. Analysis of his 15 most recent exhibitions (2007-2021) reveals a consistent trajectory toward kinetic and sound-based sculptures, with increasing emphasis on public engagement and interdisciplinary collaboration. His work demonstrates technical evolution from geometric steel constructions to complex data-driven installations, maintaining a core focus on material transformation and viewer interaction while bridging traditional sculptural techniques with digital fabrication. His principal recognition includes: Henry Moore Sculpture Fellowship (1991) Clarke has held substantial external roles including External Examiner for Fine Art at De Montfort University (2007-2011) and advisory positions for degree validations at Thames Valley University, University of Chichester, and Central Saint Martins. His professional memberships encompass the Royal British Society of Sculptors and the National Association for Fine Art Education. His practice involves significant collaborations, as evidenced by co-curated exhibitions like DRIVE THRU and group shows engaging community contexts and public spaces.

Joe Liuمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Data Visualisation
• Immersive Analytics
• +۳ مورد دیگر

Joe Liu is an Assistant Lecturer in the Department of Human Centred Computing at Monash University, within the Faculty of IT. He holds a PhD in Computer Science from Monash University (2023), a Master of Information Technology (2018), and a Bachelor of Science in Computer Science from the University of New South Wales (2013). His research focuses on Human-Computer Interaction, Data Visualisation, and Immersive Analytics, with contributions to embodied interaction and construction safety management. He has taught courses such as FIT1050, FIT2081, and FIT2095, and served as a teaching associate for units like FIT3179 (Data Visualisation) and FIT5152 (User Interface Design). Education: PhD in Computer Science, Monash University (2023) MSc in Information Technology, Monash University (2018) BSc in Computer Science, UNSW (2013) Research Interests: Joe’s work explores immersive analytics, embodied interaction in virtual environments, and the application of augmented reality (AR) for safety in construction. He investigates spatial memory in AR, gesture-based interfaces, and multi-view visualisation techniques. His recent studies include physical landmarks’ impact on spatial recall and AR-driven fall hazard detection on construction sites. Publications: Over 10 peer-reviewed articles, emphasizing trends in AR/VR applications, gesture interaction, and immersive analytics. Key themes include spatial cognition in immersive environments and industrial safety solutions through digital technologies. Awards: None explicitly listed beyond academic degrees. Advising & Grants: No formal advisees listed. Collaborates with researchers in embodied visualisation and safety engineering through Monash’s Embodied Visualisation group. Labs/Teams: Active member of the Embodied Visualisation research group at Monash University, focusing on interdisciplinary projects blending HCI and data science.

• Medical Imaging
• Machine Learning
• Real-Time Computing
• +۷ مورد دیگر

Professor Bernhard Kainz is a leading academic in the Department of Computing at Imperial College London and Professor at Friedrich-Alexander-University Erlangen-Nuremberg. He heads the Image Data Exploration and Analysis Lab (IDEA Lab) and co-leads the Biomedical Image Analysis (BioMedIA) group , focusing on human-in-the-loop computing for healthcare applications. His research develops intelligent algorithms for multi-modal healthcare , emphasizing generative and discriminative machine learning methods to enhance diagnostic decision-making and provide real-time guidance during medical procedures. Current projects address democratizing rare healthcare expertise through AI, normative learning for human-like data analysis, and interpretable medical machine learning. Co-founder of Fraiya Ltd. for medical AI solutions Scientific advisor to ThinkSono Ltd. Leader in EPSRC Centre for Doctoral Training in Smart Medical Imaging and UKRI AI for Healthcare CDT He has received numerous accolades including MICCAI best paper awards, IEEE TMI Distinguished Reviewer status, and Imperial President’s Research Team Award. His team's work spans fetal imaging , cardiac ultrasound , digital pathology , and AI explainability in healthcare.

• Visualization
• Image Processing
• Fractal Geometry
• +۳ مورد دیگر

Martin Turner is a Business Relationship Manager for Research IT at the University of Manchester, specializing in e-Science and computational infrastructure. He also serves as an Honorary Lecturer in Computer Science. His roles include project management for UKRI grants and leadership in initiatives like the Harwell Imaging Partnership and the Scientific Computing Division at STFC. Research interests span visualization, image processing, and fractal geometry, with notable contributions including a book on Fractal Geometry in Digital Imaging and over 120 peer-reviewed publications. He has supervised 21 PhD/MPhil students and contributed to edited volumes such as Geographic Visualization: Concepts, Tools and Applications . Key awards include a short-term Fellowship with British Telecom. His work integrates real-world applications with cutting-edge imaging technologies, emphasizing collaboration and longitudinal research support. Projects involve visualization frameworks, tomography, and UAV-based computer vision applications. Advising focuses on MSc and PhD levels, while grants and collaborations address imaging science, geology, and computational methods. He leads teams in developing tools like the Core Imaging Library and SIRF framework, advancing tomographic and multi-channel imaging techniques.

• Machine Learning
• Deep Learning
• Computer Vision
• +۸ مورد دیگر

Dr. Amir Atapour-Abarghouei is an Assistant Professor in the Department of Computer Science at Durham University, UK, and a Fellow of the Wolfson Research Institute for Health and Wellbeing. He leads the VIViD (Vision, Imaging and Visualisation in Durham) research group. Previously, he held roles at Newcastle University and Shahid Bahonar University of Kerman (Iran). His research focuses on machine learning, deep learning, computer vision, 3D scene understanding, and natural language processing. Notable contributions include the GANomaly anomaly detection framework, now part of Intel's AI products. Education : Ph.D., Computer Science, Durham University (UK) M.Sc., Computer Science, Universiti Teknologi Malaysia (Malaysia) B.Sc., Computer Engineering, Shahid Bahonar University of Kerman (Iran) Research Interests : His work spans machine learning, deep learning, image processing, 3D scene analysis, and robotics. Key areas include depth estimation, domain adaptation, semantic segmentation, and causal-based models for action quality assessment. Recent projects involve datasets like DurTOMD and Dur360BEV for autonomous systems and image inpainting techniques (e.g., HINT, SEM-Net). Advising & Grants : He supervises over 15 postgraduate students and has contributed to grants focused on AI-driven systems in healthcare, robotics, and computer vision. His team's work on GANomaly and neural architecture search (NAS) has been widely cited and applied in industry. Labs/Teams : Leads the VIViD Research Group and collaborates on interdisciplinary projects involving healthcare imaging, autonomous vehicles, and ethical AI. Active in organizing workshops at CVPR, IEEE BigData, and the BMVA Summer School.

• Computer Graphics
• Motion Capture
• Artificial Intelligence
• +۹ مورد دیگر

Dr. Zhidong Xiao serves as Principal Academic (Associate Professor) at Bournemouth University's National Centre for Computer Animation within the Faculty of Media and Communication. With over ten years of leadership experience including roles as Programme Leader, Head of Education, and Deputy Head of Department, he drives academic strategy and research innovation in computer animation and digital media. His work bridges technical excellence with creative industry applications through extensive collaborations across the UK and China. Dr. Xiao's educational foundation includes a PhD in Computer Graphics (2010) and postgraduate certificates in Education Practice (2010) and Research Degree Supervision (2011) from Bournemouth University, complemented by a BEng (Hons) in Thermodynamics from Taiyuan University of Technology, China (1994). PhD in Computer Graphics, Bournemouth University (2010) PGCE in Education Practice, Bournemouth University (2010) PGCE in Research Degree Supervision, Bournemouth University (2011) BEng (Hons) in Thermodynamics, Taiyuan University of Technology (1994) His research spans Computer Graphics, Motion Capture, Artificial Intelligence, and Virtual Reality with focus on physics-based simulation, sign language recognition, and motion synthesis. Recent work integrates partial differential equations with machine learning to solve animation challenges in facial realism, deformation simulation, and 3D reconstruction. His interdisciplinary approach connects computer science with creative industries, healthcare applications, and educational technology while advancing core techniques in neural rendering and motion analysis. Analysis of his 15 most recent publications reveals consistent innovation in physics-based animation techniques (40%), motion capture processing (25%), and neural approaches to 3D reconstruction (35%). Key trends include the fusion of analytical physics models with deep learning architectures, development of efficient real-time simulation methods, and expansion into accessibility applications through sign language recognition systems. Scientific recognitions include: Fellow of British Computer Society (2023) Fellow of Higher Education Academy (2011) Best Poster Award at Pacific Graphics 2014 He maintains active peer review roles for EPSRC, ESRC, IEEE Transactions on Multimedia, and ACM SIGGRAPH conferences. Dr. Xiao has supervised seven PhD students to completion while currently guiding Alexandra Sergeeva Alexdottir's research on Phantom Touch phenomena. His grant portfolio demonstrates strong industry-academia collaboration: Principal Investigator Capturing and representing sign language (British Council, 2025) VE Communication Programme (Erasmus+, 2020) Co-Investigator Rehabilitation Enhancement via Motion Capture (BU Fusion Fund, 2013) Cross-Channel Film Lab (Interreg, 2012) Digital Beijing Opera Project (2010) As a core member of Bournemouth's Computer Graphics and Visualisation Research Group and Centre for Digital Entertainment, he leads initiatives in motion capture technology through AccessMocap Studio. His international outreach includes invited lectures across China on computer animation education and visual effects techniques, strengthening global partnerships in creative technology development.

Michael Tippingمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Science
• +۹ مورد دیگر

Michael Tipping is Professor of Machine Learning within the Department of Computer Science at the University of Bath, where he joined in early 2016. He is a member of the Artificial Intelligence & Machine Learning research group and the Bath Institute for the Augmented Human. His work spans both theoretical and applied aspects of machine learning, with particular expertise in probabilistic modeling and Bayesian statistical approaches. Professor Tipping's research interests center on Machine Learning and extend to Data Science and Artificial Intelligence, viewed from a probabilistic modeling and Bayesian statistical perspective. His specific research areas include Sparse Bayesian models (the "relevance vector machine") and related novel learning techniques, probabilistic approaches to tree-based pattern recognition, adaptive analysis of multivariate time series, methods for intelligent statistical automation, new perspectives on deep neural networks, and model-driven data mapping and visualisation techniques. He has maintained an interest in specific application areas including medical diagnostics, automotive modeling, payment fraud detection, sports performance analysis, and interactive entertainment. His publication record demonstrates consistent contributions to the field since the 1990s, with particular impact in probabilistic methods. His most influential works include the Relevance Vector Machine (over 6,300 citations) and Probabilistic Principal Component Analysis (over 2,800 citations), which have become foundational techniques in Bayesian machine learning. His recent work continues to bridge theoretical advances with practical applications across diverse domains, as evidenced by his 2024 publication on underwater environment classification. Professor Tipping has been recognized for his significant contributions to the field, with his publications being cited thousands of times across various platforms. His work on the Relevance Vector Machine has been particularly influential, offering a probabilistic alternative to Support Vector Machines with comparable performance but dramatically fewer basis functions. With extensive industry experience including eight years at Microsoft Research (Cambridge), six years running an independent statistical consultancy, and two years as Director of Science at Cambridge analytics start-up Featurespace, Professor Tipping brings a unique perspective that bridges academic research and real-world applications. His work on the "Drivatar AI" system for Microsoft's Forza Motorsport franchise demonstrates his ability to translate advanced machine learning concepts into consumer-facing technologies.

Frank Tietzeمشاهده پروفایل

استاد

• Intellectual Property Management
• Innovation Engineering
• Sustainability Transitions
• +۳ مورد دیگر

Professor Frank Tietze serves as Professor of Innovation Engineering at the Institute for Manufacturing (IfM), Department of Engineering, University of Cambridge. He leads the Innovation and Intellectual Property Management (IIPM) Lab within the Centre for Technology Management, focusing on engineering management perspectives for intellectual property in distributed innovation systems. His expertise spans collaborations with governmental agencies, patent offices, and multinational corporations across diverse sectors. He holds affiliations as an Affiliate to the Cambridge Centre for Intellectual Property and Information Law (CIPIL) and member of Chalmers University's Innovation and IP research group, while serving on CIPA's IP Commercialisation Committee. Research interests emphasize intellectual property strategy development for sustainability transitions, open innovation ecosystems, and blockchain-enabled IP management. Key investigations include IP models for circular economy implementation, climate adaptation technologies, and green innovation commercialization. His work addresses how firms navigate IP challenges in collaborative R&D while balancing protection and sharing imperatives. Recent publications reveal concentrated focus on sustainability-driven IP frameworks, with significant contributions to electric vehicle battery technology governance, climate adaptation patent landscapes from developing economies, and blockchain applications for automated licensing. His research increasingly integrates advanced analytics like LLMs for litigation prediction and visual mapping methods for ecosystem dynamics. Professor Tietze directs the IIPM Lab which develops practical tools including the SBM-IP Canvas for sustainable business model integration and SVEL (Standardised Visualising Ecosystem Language) for capturing competitive dynamics. The lab's outputs directly inform IfM Engage's industry-facing IP strategy development services, bridging academic research with real-world technology management challenges.