جست‌وجوی استاد و پژوهشگر

• Visual Analytics
• Machine Learning
• Digital Geometry Processing
• +۶ مورد دیگر

Dr. Gary KL Tam is a Senior Lecturer in the Department of Computer Science at Swansea University's School of Mathematics and Computer Science. He has been with Swansea University since 2012, initially as a Lecturer (2012-2018) and promoted to Senior Lecturer in 2018. He is based at The Computational Foundry on Bay Campus and is actively involved in research through the Visual Computing Group and RIVIC. Dr. Tam's research spans several key areas in computer science, with a strong focus on visual analytics, machine learning, and digital geometry processing. His work demonstrates expertise in analyzing multidimensional data, information retrieval, and pattern recognition. He has developed substantial research in 3D reconstruction techniques, point cloud processing, and their applications in medical imaging and urban modeling. His publication record shows a clear evolution from foundational work in 3D shape matching and non-rigid registration to current applications in medical diagnostics (particularly Chronic Kidney Disease classification), Welsh language processing (CymruFluency project), and industrial applications. Recent publications demonstrate strong interdisciplinary work bridging computer vision, machine learning, and healthcare applications. Best VAST Paper Award at VIS 2016 Conference for 'Analysis of Machine- and Human-Analytics in Classification' Excellence in Learning and Teaching Award (ELTA) from Swansea University (2018) Dr. Tam actively supervises numerous PhD students across diverse research topics including explainable AI for economic growth, 3D city reconstruction, medical diagnostics, and human-in-the-loop systems. He has secured multiple research grants totaling over £50,000 from sources including CHERISH-DE, EPSRC, and Swansea University funds, supporting projects in medical AI, Welsh language processing, and computational infrastructure. His research group maintains strong collaborations with Cardiff University, National University of Singapore, and various medical research institutions, focusing on applying advanced computational techniques to real-world problems in healthcare, language processing, and industrial applications.

Volker Schwiegerمشاهده پروفایل

استاد

• Geodesy
• Engineering Geodesy
• Integrative Computational Design and Construction
• +۴ مورد دیگر

Volker Schwieger is a Professor at the Institute of Engineering Geodesy , affiliated with the Faculty of Aerospace Engineering and Geodesy at the University of Stuttgart . As a Principal Investigator in the Cluster of Excellence IntCDC, he focuses on integrating geodetic measurement technologies into computational design and construction workflows, with emphasis on robotic control, 3D scanning, and quality modeling. Key research areas: Geodesy, Computational Design, Robotics in Construction, Quality Assurance Notable projects: RP 16-1 (Robotic Assembly), RP 18-1 (Holistic Quality Model), AP 7 (Space-Time Point Cloud Modeling) Recent publications demonstrate his work in terrestrial laser scanning (TLS) for deformation analysis, point cloud processing algorithms, and holistic quality models for sustainable construction. He collaborates with institutions like Technical University of Munich and contributes to applications in bio-composites, timber structures, and climate-resilient urban planning. His methodological innovations include patch-based M3C2 analysis and supervoxel registration techniques. Despite extensive research output, no specific awards or educational background details are mentioned in the provided texts.

Hidekata Hontaniمشاهده پروفایل

استاد

• Image Processing
• Medical Image Analysis
• Computational Anatomy
• +۳ مورد دیگر

Hidekata Hontani is a Professor at the Graduate School of Engineering, Nagoya Institute of Technology, specializing in image processing and medical image analysis. His research focuses on computational anatomy, deep learning applications in medical imaging, and pathological image analysis, particularly for cancer diagnosis and treatment. Professor Hontani's research interests center around medical image processing , with specific focus on computational anatomy , deep learning for medical applications , pancreatic cancer analysis , and malignant lymphoma grading . His work bridges computer science and medical diagnostics, developing innovative algorithms for analyzing pathological images and creating 3D models of tumors. His recent publications (2023-2024) show a strong trend toward applying diffusion models and weakly supervised learning techniques to medical image analysis, particularly for cancer diagnosis. Key areas include pathological image analysis , tumor microenvironment modeling , anomaly detection , and multi-modal image registration , with increasing focus on lymphoma grading and pancreatic cancer analysis. Scientific Awards: Multiple Japanese Society of Medical Imaging Technology Conference Encouragement Awards (2012-2024) JAMIT Young Medical Imaging Engineering Symposium Encouragement Awards (2023) Cum Laude Poster Award, SPIE Medical Imaging (2018) Student Award, VISAPP 2017 Professor Hontani has secured multiple significant research grants, including an ongoing Grant-in-Aid for Scientific Research (B) from the Japan Society for the Promotion of Science titled Comprehensive Characterization of All Two Million Nuclei in Pathology Images for Quantitative Assessment of Atypia in Malignant Lymphoma (2025-2028). He serves on key academic committees including as Secretary of the Medical Imaging Research Group at IEICE since 2010 and as a committee member of the Pattern Recognition and Media Understanding Research Group since 2013.

Bruno Valletمشاهده پروفایل

پژوهشگر

• Geographic Information Science
• Computer Vision
• Remote Sensing
• +۷ مورد دیگر

Bruno Vallet is a Senior Researcher at IGN (French National Institute of Geographic and Forest Information) within the LASTIG lab and leads the ACTE research team since 2019. His work focuses on geospatial data processing, including LiDAR and image registration, 3D urban modeling, and computer vision applications. He contributes to projects like AI4GEO and Time Machine , specializing in large-scale point cloud analysis and structured city reconstruction. Education : Habilitation (HDR) in Geographic Information Science (Univ Paris-Est, 2016), PhD in Computer Science (Institut National Polytechnique de Lorraine, 2008), and Master's in Computer Vision (Telecom ParisTech, 2005). His research integrates surface reconstruction , semantic labelings , and uncertainty propagation , with methodologies applied to autonomous navigation and urban change detection. Recent publications emphasize data fusion, visibility computation, and deep learning for 3D scene analysis. He co-supervises PhD students and leads teaching activities at ENSG (National School of Geographic Sciences), covering image processing and 3D data structures. Bruno Vallet also chairs the ISPRS Working Group II/4 on 3D Scene Reconstruction, demonstrating leadership in photogrammetry and remote sensing communities.

• Positron Emission Tomography (PET)
• Magnetic Resonance Imaging (MRI)
• Biomedical Informatics
• +۴ مورد دیگر

Thibault Marin, PhD, is an Assistant Professor at the Yale School of Medicine in both the Department of Radiology & Biomedical Imaging and the Biomedical Informatics & Data Science program. He is affiliated with the Bioimaging Sciences Division , Center for Brain & Mind Health , and Center for Molecular Imaging Technology and Translation (CMITT) . Education: PhD in Electrical Engineering from Illinois Institute of Technology (2010) Key Collaborations: Georges El Fakhri, Chao Ma, Yue Zhuo, Jinsong Ouyang, Paul Han His research focuses on PET/MRI instrumentation , dynamic imaging reconstruction , and motion correction algorithms . He develops diffusion models for tumor segmentation and Bayesian methods for kinetic parameter estimation, contributing to advancements in radiomics-driven radiotherapy and ultra-high resolution PET systems . Recent publications highlight innovations in TOF-DOI panel detectors , linear tangent space alignment (LTSA) for cardiac imaging, and adversarial domain generalization for PET denoising. His work bridges biomedical engineering , machine learning , and clinical imaging applications .

Nicolas ROUGONمشاهده پروفایل

دانشیار

• Medical Imaging
• Image Registration
• Cardiovascular Imaging
• +۳ مورد دیگر

Nicolas ROUGON is an Associate Professor at Telecom SudParis, affiliated with the SAMOVAR laboratory and the ARMEDIA team. His research focuses on medical imaging and computational methods, particularly in MRI, image registration, and cardiovascular analysis. He has contributed to non-rigid registration techniques for myocardial contraction quantification and developed information-theoretic approaches for segmentation and alignment. His work spans cardiovascular imaging (myocardial perfusion, deformation analysis), multimodal image fusion (PET/MRI), and mathematical foundations of medical image processing. Notable applications include lesion segmentation in neurological disorders and dynamic statistical modeling of cardiac motion. Publications emphasize clinical translation of algorithms, with applications in cardiology and oncology. No awards are explicitly mentioned but his work has been presented globally at conferences like MICCAI, SPIE, and ISBI. Active since 1998, his research bridges signal processing theory and practical medical diagnostics.

• Virtual Reality
• Augmented Reality
• Artificial Intelligence
• +۳ مورد دیگر

Professor Wen Tang is a leading academic in Digital Games Technology at Bournemouth University, appointed as Professor in 2016. She holds a PhD in Computer Science from the University of Leeds, UK, with prior roles as a Research Fellow at Leeds and Bradford Universities. Her expertise spans Virtual Reality (VR), Augmented Reality (AR), AI, and medical applications, focusing on surgical robotics and endoscopy. She leads major research projects, including the Horizon-MSCA-2024-IP-01-01:3D-Intel Surgery initiative starting in 2025. Her research integrates VR/AR algorithms, AI-driven medical imaging, and robotics. Notable grants include EPSRC, Innovate UK, EU H2020, and Royal Academy of Engineering funding, totaling over £2 million. She serves on the EPSRC Peer Review College and has published extensively in journals like IEEE Transactions on Medical Imaging and Neurocomputing. Key projects include developing interactive surgical simulation tools, point cloud registration methods, and gamified health interventions. Her work aligns with UN SDGs for Quality Education, Decent Work, and Sustainable Innovation. Future work emphasizes 3D medical visualization and healthcare technology integration. Publications highlight advancements in medical image fusion, AI for surgical robotics, and VR safety training. She collaborates widely, mentoring teams in digital health and extended reality applications.

• Robotics and Automation
• Control Systems
• Biomedical Engineering
• +۴ مورد دیگر

Dr. Tajdari is a distinguished researcher at Delft University of Technology, specializing in interdisciplinary fields at the intersection of robotics, control systems, and biomedical engineering. His work focuses on advanced control methodologies, non-rigid registration techniques for medical imaging, intelligent transportation systems, and personalized product design through digital fabrication. He has contributed significantly to the development of adaptive controllers for robotics systems, including Stewart platforms and surgical robots, as well as innovative approaches to 3D/4D human mesh registration for applications in healthcare and biomechanics. Key Research Areas: Autonomous Vehicles, Medical Robotics, Intelligent Transportation, 3D Reconstruction, and Human-Motion Analysis His recent research emphasizes integrating machine learning with traditional control theory to address challenges in traffic management, spinal deformity prognosis, and human-centered design. Collaborations include projects on smart infrastructure systems and personalized medical devices. Over 25 peer-reviewed articles demonstrate his expertise in both theoretical and applied engineering domains.

Hongkai Zhaoمشاهده پروفایل

استاد

• Computational and Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +۴ مورد دیگر

Hongkai Zhao is the Ruth F. DeVarney Distinguished Professor of Mathematics and Chair of the Department of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. in Mathematics from UCLA (1996) and has held prior positions including Chancellor's Professor at UC Irvine and Gábor Szegő Assistant Professor at Stanford University. His research focuses on computational and applied mathematics with applications in inverse problems, imaging, PDEs, and scientific computing. Key research areas include numerical methods for wave propagation, radiative transfer, and shape analysis. Zhao has led NSF-funded projects on PDE learning, radiative transfer, and analysis training. He has received prestigious awards including the Feng Kang Prize (2007) and SIAM Fellowship (2022). Teaching includes advanced courses on stochastic processes, numerical linear algebra, and mathematical analysis. His work bridges theory and practice, contributing to medical imaging algorithms, computational physics, and geometric modeling.

Gary Tamمشاهده پروفایل

مدرس ارشد

• Visual Analytics
• 3D Computer Vision
• Point Cloud Processing
• +۲ مورد دیگر

Dr. Gary Tam is Senior Lecturer in Computer Science at Swansea University and member of the Visual Computing Group. Education: B.Eng. from Hong Kong University of Science and Technology M.Phil. from City University of Hong Kong Ph.D. from Durham University PGCtHE from Swansea University Research: Focuses on 3D computer vision, visual analytics, and geometric processing. Recent work includes point cloud compression, saliency detection, and 3D reconstruction techniques. Publications: Research demonstrates strong focus on 3D data processing (point clouds, reconstruction) and visual attention modeling, with increasing application of deep learning across domains including healthcare.

Llado Bardera, Xavierمشاهده پروفایل

استاد

• Medical Image Analysis
• Computer Vision
• Image Processing
• +۱۱ مورد دیگر

Xavier Lladó Bardera is a Full Professor and ICREA Academia at the University of Girona, affiliated with the Department of Computer Architecture and Technology and the Computer Vision and Robotics Institute (VICOROB). He holds a PhD in Computer Engineering from the University of Girona and completed postdoctoral research at Queen Mary, University of London. B.S. in Computer Science, University of Girona (1999) Ph.D. in Computer Engineering, University of Girona (2004) Post-doctoral Research Assistant, Queen Mary, University of London (2004–2006) His research centers on medical image analysis, particularly in brain MRI, multiple sclerosis, prostate imaging, and deep learning. He has made significant contributions to lesion segmentation, tissue classification, and image registration, with a focus on clinical translation. His work integrates advanced computer vision techniques with biomedical applications, leveraging deep learning, non-rigid modeling, and multimodal imaging. The recent publications reflect a strong trend in deep learning for medical image analysis, especially in stroke, multiple sclerosis, and neonatal imaging. Key themes include lesion segmentation, hematoma expansion prediction, brain atrophy quantification, and end-to-end deep learning frameworks. There is a clear emphasis on clinical applicability, model interpretability, and handling real-world imaging challenges. Scientific Awards and Recognition: Best Project Prize – MAIA Master (Mladen Rakic, 2019) Best Project Prize – VIBOT Master (Jose Bernal, 2017) Best Project Prize – EINF Degree Project (E. Roura, 2011) Best Project Prize – EINF Degree Project (M. Cabezas, 2009) Best Project Prize – ETIG Degree Project (C. Fontanella, 2009) Best Paper Prize – Pattern Recognition (J. Salvi et al., 2010) Dr. Lladó has supervised over 20 PhD and master’s students, many of whom have continued in academia or industry (e.g., Icometrix, Johns Hopkins). He has been involved in numerous research grants and technology transfer initiatives, particularly in CAD systems for breast imaging and MS monitoring tools. His group actively collaborates with clinical partners, ensuring translational impact. He leads the NIC group at the VICOROB Institute, focusing on neuroimage computing and deep learning applications in neurology. The team develops open-source tools and participates in international challenges (e.g., BraTS, WMH), contributing to standardized evaluation in medical image analysis.

Jan Lellmannمشاهده پروفایل

استاد

• Variational Image Processing
• Optimization on Manifolds
• Image Registration
• +۷ مورد دیگر

Jan Lellmann is a Professor at the Institute of Mathematics and Image Computing of the University of Lübeck, with affiliations to Fraunhofer MEVIS . His research focuses on variational image processing , emphasizing the systematic formulation of prior knowledge into energy functions for improved accuracy and data efficiency. Applications span medical imaging , earth sciences , and biological data analysis . He develops non-smooth optimization methods for problems with combinatorial aspects like image segmentation . His recent work includes manifold-constrained optimization and quantum algorithms for imaging tasks. He has contributed software libraries like MFOPT (for manifold optimization) and COAL (for convex energy minimization). Scientific Awards: Best Student Paper Award at SSVM 2021 Honorable Mention at CVPR 2016 Recent Research Trends: Integration of quantum computing with classical image registration. Advancements in Riemannian geometry for protein dynamics and cryo-EM. Development of meta-learning frameworks for adaptive image alignment. Focus on non-smooth and higher-order regularization for sparse data reconstruction.

Diego Borro Yagüezمشاهده پروفایل

استاد

• Computer Graphics
• Virtual Reality
• Augmented Reality
• +۶ مورد دیگر

Diego Borro is a Full Professor (Catedrático) in Computer Science and Artificial Intelligence at TECNUN, Technological Campus of the University of Navarra , where he has been part of the faculty since 2004. He is a leading researcher at CEIT since 2003, focusing on Robotics, Virtual/Augmented Reality, Computer Vision, and Artificial Intelligence. His academic credentials include a PhD in Computer Science (2003) and an MS in Computer Science (2000) from the University of Navarra and University of Basque Country respectively. His research spans from 3D tracking and haptics to industry 4.0 applications and medical robotics, with over 34 journal papers and 65 conference articles. He has supervised 14 doctoral theses and participated in 55+ research projects. His leadership roles include heading CEIT's Simulation Unit (2012-2016) and Vision and Robotics (V&R) research line (2016-2022), currently serving as main researcher at Intelligent Systems for Industry 4.0 group (SS4I4). Accredited as Full Professor (Catedrático) by ANECA (3 sexenios) Member of IEEE, ACM, and Eurographics societies Key projects: STEPbySTEP exoskeleton benchmark, WARM AR maintenance systems, and inner ear drug delivery research

Chihray Liuمشاهده پروفایل

استاد

• Intensity-modulated radiation therapy (IMRT) delivery and verification
• Dose and machine output calculation
• Treatment planning optimization
• +۷ مورد دیگر

Chihray Liu is a Professor in the Department of Radiation Oncology at the University of Florida. His research focuses on advancing radiation therapy technologies, including IMRT delivery optimization, dose calculation, and quality assurance automation. He is also affiliated with the Biomedical Engineering (BME) Graduate Faculty. Key interests include: Dynamic wedge and multileaf collimator modeling Image-guided radiation therapy innovations Optical tracking systems for patient safety Benchmark datasets and treatment planning improvements No specific awards or grants are listed here, but his work emphasizes technical advancements in radiation oncology workflows and precision therapy.

• Topological Deep Learning
• Geometric Machine Learning
• 3D Computer Vision
• +۴ مورد دیگر

Tolga Birdal is an Assistant Professor (Lecturer) and UKRI Future Leaders Fellow at Imperial College London's Department of Computing, leading the CIRCLE research group. He holds affiliations with the Artificial Intelligence Network, Visual Computing, and the Department of Computing. His research focuses on topological deep learning, geometric machine learning, and 3D computer vision, with applications in drug discovery and non-Euclidean spaces. Education: PhD in Computer Science, Technical University of Munich (2014-2018) MSc in Computer Science, Technical University of Munich (2008-2011) BSc in Computer Engineering, Sabancı University (2004-2008) Research Interests: Birdal investigates limits in geometric computing and non-Euclidean inference, developing topological methods for deep learning. His work bridges algebraic topology, differential geometry, and deep neural networks. Current projects include UNTOLD (EPSRC), a foundational study in topological deep learning, and drug discovery initiatives funded by the Royal Society. Awards & Grants: UKRI Future Leaders Fellowship (2024) EMVA Young Professional Award (2018) PI/Co-PI on projects totaling £multi-million across EPSRC, Royal Society, and industry partnerships Advisees & Labs: He leads the CIRCLE group at Imperial College, fostering interdisciplinary research in geometric and topological AI. Prior roles include Postdoc at Stanford University (Guibas Group) and Research Scientist at Hover (USA).