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Jan Lemeire is an active researcher at Vrije Universiteit Brussel (VUB), affiliated with the Department of Electronics and Informatics within the Faculty of Engineering. Based in Brussels, Belgium at Pleinlaan 2, he maintains an active research profile with an h-index of 10 and 581 citations according to Scopus data. His research interests span multiple domains including GPU computing, machine learning, embedded systems, and their applications in diverse fields from biomedical engineering to forensic science. His work demonstrates strong interdisciplinary connections, bridging computer science with practical applications in health technology, crime analysis, and industrial systems. Analysis of his recent publications reveals a consistent focus on computational efficiency, with particular emphasis on GPU acceleration, embedded AI deployment, and machine learning applications. His work shows evolution from hardware-focused research toward more applied domains including healthcare technology and crime pattern analysis. Dr. Lemeire actively participates in multiple research projects including NSIS2: PRISMA network (2024-2029), IOF3016: GEAR (2021-2025), and Tech4Health (2024-2025), demonstrating sustained research funding and collaborative work across disciplines. His academic activities include supervision of graduate students, as evidenced by his role as advisor for the 2017 Master's thesis on GPU-accelerated holography, and regular participation in major conferences including the Conference on Uncertainty in Artificial Intelligence and the International Symposium on Embedded Multicore Systems. Dr. Lemeire maintains an active research laboratory focused on computational methods, with particular strengths in parallel processing techniques and their application to real-world problems across multiple domains including healthcare, industrial systems, and forensic science.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +7 more

David Blinder is a Professor at the Vrije Universiteit Brussel (VUB) in the Department of Electronics and Informatics (ETRO), where he maintains a dual affiliation with the imec research institute. He concurrently holds a JSPS International Research Fellow position at Chiba University, leveraging over a decade of cross-institutional expertise in optics, signal processing, and computer science to advance computational holography and photonics. His research program centers on signal processing in photonics and holography, with emphasis on numerical diffraction, time-frequency analysis, data compression, and computational imaging. As lead editor of the JPEG Pleno Holography standard, he drives innovation in holographic data representation and transmission. His work integrates optical setups, holographic displays, and high-performance computing to solve complex problems in 3D visualization and inverse methods. Recent publications demonstrate a cohesive trajectory toward efficient computational frameworks for holographic systems, with recurring themes in numerical diffraction algorithms, polygon-based hologram generation, and asymmetric point-spread function optimization. These advances directly enable next-generation holographic displays and computational imaging applications across medical visualization and augmented reality. Blinder's contributions have been recognized through significant honors: Editor's Pick award (October 2024) Editor's Pick award (September 2024) Suzuki-Okada Memorial prize (June 2024) OSA Outstanding Reviewer recognition (2018) He has mentored master's students including A. Ouahabi (2015, Motion Estimation for Dynamic Holography) and F. Suarez Groen (2017, GPU-optimized Computer-Generated Holography). His research is sustained by major grants: FWOAL1100: Advanced dynamic computer-generated holography (2024-2026) FWOAL1101: Computational Incoherent holographic plenoptic camera (2024-2027) FWOTM1099: Chirplet-based diffraction framework (2022-2027) Tech4Health: Future health technologies (2024-2025) As a core member of VUB's ETRO department and imec, Blinder leads interdisciplinary teams in projects like GEAR and Tech4Health, developing holographic visualization systems for medical applications while advancing fundamental optical computing methodologies.

• Optics
• Signal Processing
• Computer Science
• +7 more

Dr Simon Kent serves as an Honorary Reader in the Department of Computer Science within Brunel University London's College of Engineering, Design and Physical Sciences, while currently working at the University of Nottingham. Previously, he held the position of Director of Learning and Teaching in Brunel's Computer Science Department, leveraging his dual background in academia and financial services software architecture to bridge industry-academia gaps. His educational qualifications include a PhD in Artificial Intelligence focused on robot path planning and a Postgraduate Certificate in Learning and Teaching Higher Education. Kent is recognized as a Fellow of the Higher Education Academy and maintains membership in the British Computer Society. Research interests span digital education analytics, machine learning, distributed/cloud computing, and software engineering pedagogy. Kent pioneers industry-aligned educational innovations, notably implementing the UK's first large-scale BYOD digital examination system at Brunel and analyzing assessment data to enhance student outcomes. His work consistently applies computational techniques like genetic programming to cross-disciplinary challenges in healthcare, defense, and finance. Publication trends reveal an evolution from foundational AI work in the 1990s (oral cancer diagnosis, missile systems) through distributed computing applications in the 2000s (video rendering, diabetes monitoring) toward contemporary digital education research. Key thematic threads include real-world problem solving, industry collaboration, and translating technical innovation into educational impact. Scientific recognition includes: Fellow of the Higher Education Academy Member of the British Computer Society As an educator, Kent directs the Level 2 Group Project (CS2001) simulating commercial software development environments and mentors across undergraduate/master's programs. He secured significant research funding including a Sucden UK collaboration for distributed order matching systems and Diabetes UK proposals for diabetic teenager monitoring applications, emphasizing practical industry partnerships. Affiliated with Brunel's CIKM and CSSB research groups, Kent continues advancing digital assessment methodologies while exploring IoT applications for home energy optimization through appliance collaboration networks.

• Digital Education
• Machine Learning
• Distributed Computing
• +4 more

Roberto Pierdicca is a Researcher in Geomatics (scientific sector CEAR-04/A) at the Department of Civil, Building and Architectural Engineering, College of Engineering, Università Politecnica delle Marche (UNIVPM) in Ancona, Italy. His academic career focuses on the intersection of geospatial technologies, artificial intelligence, and cultural heritage documentation, with a strong emphasis on practical applications in environmental monitoring and preservation. Dr. Pierdicca's research interests center around advanced geomatics techniques including LiDAR technology, UAV-based remote sensing, and 3D modeling for cultural heritage preservation and environmental applications. His work demonstrates a strong interdisciplinary approach, bridging geospatial sciences with computer vision, machine learning, and environmental engineering to develop innovative solutions for complex documentation and monitoring challenges. Analysis of his publication record from 2023-2025 reveals a clear research trajectory focused on integrating artificial intelligence with traditional geomatics techniques. His work spans multiple domains including cultural heritage documentation (particularly for sites in Syria, Uzbekistan, and Italy), environmental monitoring (floodplains, vegetation recovery, olive groves), and forestry management. A notable trend is the increasing sophistication of AI applications in his work, moving from basic classification to complex generative models and context-aware systems. Dr. Pierdicca is actively involved in multiple research projects that combine geospatial data acquisition with advanced computational methods. His work frequently appears in interdisciplinary contexts, suggesting strong collaborations across engineering, computer science, and cultural heritage disciplines. The practical applications of his research are evident in numerous case studies addressing real-world challenges in heritage preservation, environmental monitoring, and precision agriculture.

• Geomatics
• Remote Sensing
• Cultural Heritage Documentation
• +5 more

Loïc Denis is a Professor at Université Jean Monnet Saint-Etienne, teaching at TELECOM Saint-Etienne (Graduate School in electrical engineering and computer science) and conducting research at the Laboratoire Hubert Curien, a joint lab between the University and the CNRS. His research focuses on image restoration and reconstruction with applications spanning from microscopy and optical metrology to SAR imaging and astronomy. Denis's research interests center on image processing and artificial intelligence, with particular expertise in Synthetic Aperture Radar (SAR) imaging, exoplanet detection and characterization, digital holography, and Earth observation. His work bridges theoretical developments in image restoration with practical applications in remote sensing, astronomy, and biomedical imaging. He has developed innovative algorithms like PACO for exoplanet detection and MERLIN for SAR despeckling that have significantly advanced these fields. His recent publications demonstrate a strong focus on deep learning approaches for SAR image processing, particularly in multitemporal speckle reduction, polarimetric SAR despeckling, and SAR tomography. His astronomical work centers on high-contrast imaging techniques for exoplanet detection and characterization, with significant contributions to angular and spectral differential imaging. The interdisciplinary nature of his research connects computer vision, signal processing, and domain-specific applications across multiple scientific fields. IEEE GRSS Symposium Prize Paper Award 2021 for 'Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning' IEEE Geoscience and Remote Sensing Society 2016 Transactions Prize Paper Award for 'NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising' Denis has advised numerous PhD students including Emanuele Dalsasso, Olivier Flasseur, and Sylvain Lobry, whose work has received multiple awards. He has led significant research projects including DIAGHOLO (2018-2023), RESSOURCES (2017-2018), and DETECTION (2015-2018). His research is supported by various grants from the Auvergne-Rhône-Alpes Region, ANR, and CNRS. At the Laboratoire Hubert Curien, he collaborates with colleagues across multiple disciplines to advance image processing techniques for diverse applications.

• Image Processing
• Artificial Intelligence
• Synthetic Aperture Radar Imaging
• +5 more

Prof. Dr.-Ing. Uwe Freiherr von Lukas is Professor of Maritime Graphics at the University of Rostock , Faculty of Computer Science and Electrical Engineering, and heads the Rostock site of the Fraunhofer Institute for Computer Graphics Research IGD . Since 2010 he has also been Honorary Professor for Virtual Product Development and is a member of the Interdisciplinary Faculty’s Department of Maritime Systems. Education: Diplom in Computer Science, TU Darmstadt (1994) Doctorate (Promotion), University of Rostock Research Focus: His research concentrates on three pillars: Machine Learning for automated image and video analysis with a strong emphasis on underwater imagery , interactive visualization and immersive analytics of large-scale maritime data , and technology-oriented networks for innovation transfer and regional development . These topics converge in applications such as digital twins for the Baltic Sea, VR-based maritime training, and AI-driven ecosystem monitoring. Scientific Output & Trends: Recent publications (2022-2025) reveal an intensified integration of deep learning and computer vision techniques to solve domain-specific challenges in marine and environmental sciences. Studies range from transformer-based seagrass segmentation and unsupervised clustering of ROV imagery to digital-twin frameworks for ocean research, reflecting a clear trend toward AI-supported, data-centric maritime analytics . Professional Service & Networks: Board member, Subsea Monitoring Network e.V. Speaker, BMBF Future Cluster “Ocean Technology Campus Rostock” Managing Director, competence network “OceanTechnologies@Fraunhofer” Member of advisory boards of Society for Maritime Technology, Maritime Future Advisory Board Mecklenburg-Vorpommern, and others Labs & Teams: At Fraunhofer IGD Rostock he leads the Maritime Graphics group, coordinating more than thirty collaborative R&D projects that bridge computer graphics, VR/AR, and maritime engineering. The group operates state-of-the-art VR laboratories, underwater vision testbeds, and high-performance visualization facilities serving both academic and industrial partners.

• Maritime Graphics
• Underwater Image Processing
• Machine Learning for Computer Vision
• +4 more

Paul G. Kry is an Associate Professor in the School of Computer Science at McGill University, where he leads the Computer Animation and Interaction Capture Laboratory. His research focuses on physically based animation, computer graphics, and human motor control, with applications in video games, movies, training simulations, ergonomics, and robotics. He teaches graduate and undergraduate courses including COMP 557 (Fundamentals of Computer Graphics) and COMP 559 (Fundamentals of Computer Animation). Dr. Kry's research interests center on computer graphics, physically based animation, skin deformations of articulated characters, motion capture, interaction, and physically based modeling of humans and animals. He investigates human and animal motor control (locomotion, grasping, manipulation) in combination with the physics of rigid objects, deformation, and contact. His work emphasizes the combination of real-world measurements, approximate models, and physically based simulation, with additional interests in machine learning, numerical methods, and audio. His recent publications demonstrate strong trends in physically based animation, with focus areas including adaptive simulation techniques, real-to-sim calibration, constraint-based physics, neural network applications in graphics, and motion control policies. His work spans both theoretical foundations and practical applications, often bridging the gap between computer graphics and robotics. BEST PAPER at Graphics Interface, 2023 BEST STUDENT PRESENTATION at ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 2019 BEST PAPER at Computational Aesthetics / Expressive, 2016 Dr. Kry actively supervises numerous PhD and MSc students, with many graduates going on to positions at leading companies including Ubisoft, Google, Facebook, and Autodesk. He has served on program committees for major conferences including ACM SIGGRAPH, Symposium on Computer Animation, and Graphics Interface. His lab maintains specialized equipment including a 24-camera motion capture system, force plates, consumer DSLR cameras, high-speed cameras, data gloves, quadrotor robots, and various pressure sensors.

• Computer Graphics
• Physically Based Animation
• Motion Capture
• +4 more

Dr. Geb W. Thomas is a Professor in the Department of Industrial and Systems Engineering at the University of Iowa , with a joint appointment in Orthopedics and Rehabilitation . He also serves as a Researcher at the Iowa Initiative for Artificial Intelligence . PhD, Industrial Engineering, Penn State University (1996) MS, Industrial Engineering, Penn State University (1995) BS, Physics, State University of New York (1991) Dr. Thomas specializes in Human-Computer Interface , Robotics , Virtual Reality , and Human Factors , focusing on applications in Orthopedic Surgery and Environmental Monitoring . His work bridges AI with surgical skill assessment and sensor network design for workplace safety. The 15 most recent publications highlight his leadership in Medical Simulation , including Fluoroscopy-based skill assessment , VR surgical training platforms , and Low-cost sensor networks for noise/ozone monitoring. Key trends include AI integration in orthopedic education and physics-informed environmental exposure modeling. Scientific Awards : Phi Kappa Phi - Engineering Honor Society Human Factors and Ergonomics Society Institute of Industrial Engineering His research involves Multidisciplinary Collaboration between engineering, medicine, and public health, with a focus on Simulation-Based Training and Autonomous Systems . He leads projects at the intersection of Medical Robotics and Occupational Safety .

• Human-Computer Interaction
• Robotics
• Virtual Reality
• +4 more

Julius Bier Kirkegaard is a Tenure Track Assistant Professor at the University of Copenhagen, holding dual appointments at the Niels Bohr Institute (Biocomplexity section) and the Department of Computer Science (Image Analysis, Computational Modelling and Geometry group). His work bridges theoretical physics, computational biology, and computer science, focusing on complex biological systems through quantitative approaches. Dr. Kirkegaard's research spans multiple interconnected areas in biocomplexity and biophysics. His primary interests include: Biological transport networks and their optimization Chemotaxis and cellular gradient sensing mechanisms Computational modeling of biological structures (particularly plant venation networks) Image analysis techniques for biological systems Network topology in chemical reaction systems Deep learning applications in medical imaging Analysis of Dr. Kirkegaard's recent publications reveals a strong interdisciplinary approach combining physics, biology, and computational methods. His work often focuses on how biological systems optimize information processing and transport under physical constraints. A significant theme is the study of network structures in biological contexts, from leaf venation to cellular receptor organization. His more recent work increasingly incorporates machine learning techniques, particularly deep learning, for analyzing complex biological systems and medical imaging data. Dr. Kirkegaard maintains active collaborations across multiple institutions, as evidenced by his diverse publication record spanning physics, biology, and computer science journals. His research appears to be supported by various grants, though specific funding sources aren't detailed in the provided information. Based at the University of Copenhagen, Dr. Kirkegaard works within the Biocomplexity and Biophysics research environment at the Niels Bohr Institute, which provides a collaborative setting for interdisciplinary research at the interface of physics and biology.

• Biocomplexity
• Biophysics
• Computational Biology
• +6 more

Javier Civera Sancho is an Associate Professor at the University of Zaragoza, where he serves as Deputy Director of the Institute of Research in Engineering of Aragon (I3A). He is affiliated with the School of Engineering and Architecture in the Department of Computer Science and Systems Engineering, where he leads research in the RoPeRT group (Robotics, Computer Vision and Artificial Intelligence). With a background in Industrial Engineering and a PhD in Systems and Computer Engineering, both from the University of Zaragoza, Civera has established himself as a prominent researcher in computer vision and robotics. Industrial Engineering degree (2004, University of Zaragoza) PhD in Systems and Computer Engineering (2009, University of Zaragoza) Civera's research primarily focuses on computer vision, with special emphasis on SLAM (Simultaneous Localization and Mapping), 3D reconstruction, and spatial artificial intelligence. His work aims to provide computers with human-like visual capabilities, including object recognition, 3D structure estimation, spatial context understanding, and tracking of moving objects. He has maintained a long-standing research line in scene localization and mapping, making significant contributions to visual SLAM methodologies and applications. His publication trends reveal a strong focus on advancing visual SLAM techniques, with recent work exploring neural rendering integration, semantic mapping, robust camera calibration, and applications in challenging environments like agriculture. His research bridges theoretical computer vision with practical robotics applications, showing increasing integration with deep learning approaches while maintaining strong geometric foundations. 2 research sexenios (with last granted for 2012-2017) 2 teaching quinquenios h-index: 21 (Google Scholar) 397 citations in last 5 years (Google Scholar) Civera has directed 2 doctoral theses and is currently supervising 4 PhD students. His research has been supported through various institutional frameworks at the University of Zaragoza, including his role in the I3A which provides critical research infrastructure. He is actively involved in guiding the next generation of researchers in computer vision and robotics, emphasizing both theoretical understanding and practical implementation. As a member of the RoPeRT research group, Civera collaborates with a multidisciplinary team working at the intersection of robotics, computer vision, and artificial intelligence. His work often involves developing systems that integrate multiple sensor modalities and create robust spatial understanding for autonomous agents.

• Computer Vision
• Robotics
• Artificial Intelligence
• +5 more

Romain Pacanowski is a Research Scientist at INRIA Bordeaux South-West, a French national research institute for computer science and applied mathematics. His work bridges computer graphics, optics, and physics with a focus on material appearance representation and rendering techniques. His primary research interests center on Material Appearance Representation, with specific focus on BRDF modeling and acquisition. He works closely with opticians and physicists to improve the quality and speed of (SV)-BRDF and roughness acquisition. His research also involves developing new mathematical representations and parametrizations of the BRDF space. Additional interests include Global Illumination for both offline and real-time rendering systems, sketched-based UI for highlight design, and emerging technologies like OptiX for hybrid rendering systems. His publication record shows a consistent focus on BRDF modeling, material appearance, and rendering techniques. His recent work (2016-2018) particularly emphasizes diffraction effects in material measurements, novel BRDF representations, and efficient sampling techniques for rendering. He frequently collaborates with researchers from academic institutions while working at the INRIA research institute. Pacanowski maintains an active research program with numerous publications in top computer graphics venues including SIGGRAPH, Eurographics, and Computer Graphics Forum. His work combines theoretical modeling with practical implementation, often providing open-source tools like the ALTA BRDF Analysis Library.

• Material Appearance Representation
• BRDF Modeling
• Computer Graphics
• +3 more

Prof. Bastian Leibe serves as a University Professor at RWTH Aachen University, leading the Computer Vision Group within the Chair of Computer Sciences 8 (Computer Graphics, Computer Vision, and Multimedia). His research focuses on developing computer vision applications for mobile devices, robotic systems, and autonomous vehicles, with strong institutional ties to the Cluster of Excellence "UMIC - Ultra High-Speed Mobile Information and Communication". His core research spans visual object recognition, tracking, self-localization, and 3D reconstruction, with increasing emphasis on integrated solutions for real-world deployment. Recent work demonstrates deep specialization in autonomous driving perception systems, human-robot interaction interfaces, and foundational computer vision methodologies that bridge theoretical advances with practical engineering constraints. Analysis of recent publications reveals dominant trends in LiDAR-based anomaly detection for autonomous systems, efficient 3D scene understanding frameworks, and novel applications of foundation models in robotics. The group consistently contributes to top-tier conferences with innovations in diffusion models, vision transformers, and interactive segmentation techniques that push the boundaries of real-time mobile vision. The Computer Vision Group maintains active educational engagement through specialized lectures and seminars in computer vision and machine learning, while operating from the UMIC Research Centre facility in Aachen with direct industry and academic collaborations in mobile information systems.

• Computer Vision
• Machine Learning
• Object Recognition
• +6 more

David H. Laidlaw is a Professor of Computer Science at Brown University's School of Engineering, where he leads the Visualization Research Lab (VRL) as part of the Brown University Graphics, Visualization, & Interaction (GVI) Group. His work bridges computer science with applications across scientific disciplines through visualization and computational modeling. His research interests focus on applications of visualization, computational modeling, computer graphics, and computer science to other scientific disciplines. Key areas include visualization of multivalued multidimensional imaging data, comparisons of virtual and non-virtual environments for scientific tasks, and applications of art and perception to visualization. His work spans medical imaging, neuroscience, and biological visualization, with particular emphasis on diffusion MRI data analysis and virtual reality interfaces. Analysis of his recent publications reveals a consistent focus on visualization techniques applied to complex scientific data, particularly in medical imaging and neuroscience. His work increasingly incorporates virtual reality technologies for data exploration, with recent publications examining text readability in VR environments, placental vasculature visualization, and white matter integrity analysis. His research demonstrates interdisciplinary collaboration across computer science, medicine, and biology. Professor Laidlaw earned his Computer Science PhD from Caltech while working in the graphics group there, followed by post-doctoral work at Caltech's Fraser lab in the Beckman Institute (Biology division). He maintains an active research program with recent funding support from NIH and collaborations with institutions like the University of Edinburgh. His Visualization Research Lab brings together an interdisciplinary team from Computer Science and other disciplines to develop robust interactive visualization tools. The lab's work is guided by collaborative projects with colleagues in arts and sciences, ensuring practical applicability of their visualization techniques. The lab also maintains the VR Wiki, developed by his CS1951T class, as a resource for virtual reality knowledge sharing.

• Visualization
• Computational modeling
• Computer graphics
• +7 more

Regine Hartwig is a current researcher at the Technical University of Munich , affiliated with the TUM School of Computation, Information and Technology and the Department of Informatics 9 . She is supervised by Professor Daniel Cremers within the Computer Vision Group. Education : M.Sc. in Electrical Engineering Her research focuses on geometric awareness of semantic image encodings and the mathematical formulations and interpretability of machine learning . She has contributed to advancing techniques in 3D reconstruction, real-time navigation, and medical imaging applications. Key trends in her publications include: Geometric consistency in neural embeddings (ICCV 2025) Constrained visual-inertial localization for laparoscopic surgery (ICRA 2022) These works bridge computer vision with medical robotics , emphasizing mathematical rigor and real-world deployment.

• Computer Vision
• Machine Learning
• Geometric Awareness
• +4 more

Dávid Komorowicz is a researcher at the Computer Vision Group within the School of Computation, Information and Technology at the Technical University of Munich (TUM). He is affiliated with Computer Science Department IX, working under the supervision of Prof. Dr. Daniel Cremers in the field of computer vision and 3D reconstruction. His research interests focus on computer vision, 3D reconstruction, neural rendering, historical building reconstruction, deep learning, and image-based modeling. His work bridges traditional computer vision techniques with modern neural approaches to solve complex reconstruction problems. His recent publication at GCPR 2024 demonstrates expertise in applying neural methods to historical building reconstruction from archival photography, showing how computer vision can be applied to cultural heritage preservation. This work combines photogrammetry, neural rendering, and geometry processing techniques to create accurate 3D models of historical structures from limited archival photographic sources. As part of the Computer Vision Group at TUM, he contributes to research areas including Visual SLAM, Photometry-Based Reconstruction, Geometry Processing, and Deep Learning applications in computer vision.

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +3 more