جست‌وجوی استاد و پژوهشگر

Jon Sporringمشاهده پروفایل

استاد

• Computer Science
• Mathematical Image Processing
• Medical Image Analysis
• +۶ مورد دیگر

Jon Sporring is a Professor at the Department of Computer Science, University of Copenhagen, specializing in theoretical and applied image processing, stochastic geometry, and biomedical imaging. He leads research in mathematical and medical image analysis, computer graphics, and pattern recognition. Education: Ph.D. in Computer Science (1998), Master in Computer Science (1995), both from University of Copenhagen Affiliations: Pioneer AI section, Applied Geometry Lab, and Faculty of Science External Roles: Visiting professor at McGill University (2012-13), co-founder of DigiCorpus Aps (2012-16) His research integrates scale-space theory, statistical shape analysis, and advanced imaging techniques for applications in medical diagnostics, materials science, and neuroscience. Recent work focuses on 3D reconstruction, persistent homology for bias correction, and AI-driven biomedical analysis. Jon teaches computer science at all academic levels, currently offering courses in bioimaging, signal processing, and deep learning. He emphasizes collaborative projects with external partners and has held administrative roles including Vice-Chair for Research at DIKU. His 15 most recent publications reflect expertise in medical imaging, 3D modeling, and AI applications, with subfields spanning neurodegenerative disease analysis, mitochondrial ultrastructure, and multi-scale image processing. Articles demonstrate interdisciplinary impact across medicine, biology, and materials science.

• Geometric Shape Analysis
• Deep Learning
• 3D Shape Matching
• +۲ مورد دیگر

Maks Ovsjanikov is Professor of Computer Science at École Polytechnique and Visiting Research Scientist at Google DeepMind. Leading the GeomeriX team, his ERC-funded research develops deep learning methods for 3D shape analysis with applications in protein modeling and computer vision. Recent breakthroughs include CrAI for antibody detection in cryo-EM data and surface-based protein representations. His group pioneers neural approaches for non-rigid shape matching and geometric deep learning fundamentals. Collaborations span structural biology, with work featured at top venues including SIGGRAPH, CVPR, and Nature journals.

• Computer Vision
• Image Processing
• Machine Learning
• +۱۲ مورد دیگر

T. Huysmans is a researcher in the Faculty of Industrial Design Engineering at TU Delft, specializing in Human Factors. Their work integrates computer science and engineering to advance 3D anthropometry, ergonomics, and human-centered design. Research interests focus on image processing, machine learning, and 3D/4D scanning for applications in safety equipment, product design, and human physiology. Key areas include facial and foot anthropometry, pressure discomfort mapping, and aerodynamic modeling. The recent research articles demonstrate a strong trend in applied computer vision and data-driven ergonomics , particularly in cross-national comparisons of facial dimensions for PPE design and dynamic foot shape modeling during walking. These works combine advanced deep learning with practical industrial applications. No scientific awards were mentioned in the provided text. Huysmans has supervised at least one research project, as indicated by the 'Supervised Work' section. While specific grants are not listed, the scale of data collection (e.g., scanning Chilean workers and Dutch children) suggests involvement in funded research initiatives. The research is supported by datasets such as the 'Dense 3D pressure discomfort threshold map' and the 'Generic Cyclist Model', indicating active lab-based work involving 3D scanning, data modeling, and simulation.

• Robotics
• Agricultural Robotics
• 3D Imaging
• +۶ مورد دیگر

Dr. Alen Alempijevic is the Deputy Head of School (Teaching and Learning) at the School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS). He holds the academic rank of Senior Lecturer and coordinates Mechatronic Engineering programs. His research focuses on robotic perception, multi-modal sensor fusion, and AI applications in physical systems, with industry-driven projects in livestock monitoring, critical infrastructure, and autonomous systems. He completed his PhD in Robotics at UTS and contributed to the DARPA Urban Grand Challenge with the Sydney-Berkeley team. Education: PhD in Robotics (2004-2008), B.Eng (Computer Systems) with First Class Honors (2000-2003), B.E and B.Sc in Computer Science and Engineering from University of Belgrade (1995-1999). Research Interests: Livestock monitoring via 3D imaging, autonomous vehicles, sensor fusion, and robotics perception. His work includes developing technologies for cattle trait estimation, crowd dynamics prediction, and industrial automation. Funded Research: Notable grants include UTS Wool Bioharvesting (2024-2027), Objective real-time live cattle assessments (2020-2023), and Advanced livestock measurement technologies (2017-2018).

• Medical Imaging
• Artificial Intelligence in Radiology
• Magnetic Resonance Imaging
• +۵ مورد دیگر

Sergios Gatidis is a prominent researcher in the Department of Diagnostic and Interventional Radiology at the Faculty of Medicine, Eberhard Karls University of Tübingen. His work bridges medical imaging and artificial intelligence, with a particular focus on MRI and PET/CT applications. He has established himself as a key collaborator in numerous multi-institutional research projects, frequently working with Thomas Küstner, Bin Yang, and Konstantin Nikolaou. Dr. Gatidis's research centers on applying deep learning techniques to solve critical challenges in medical imaging. His work spans biological age estimation from MRI scans, motion correction in MRI, lesion segmentation in PET/CT imaging, and the application of large language models to radiology reports and hospital course documentation. He has made significant contributions to the autoPET challenge for automated lesion segmentation and has developed novel approaches for attention-aware image registration and reconstruction. His publication record shows a clear evolution from foundational work in motion correction and image reconstruction (2016-2018) to increasingly sophisticated AI applications, with a recent strong focus on large language models for medical text processing (2023-2025). The breadth of his work demonstrates expertise spanning technical aspects of medical imaging physics to clinical applications of AI. His recent publications indicate active research in several key areas: (1) development of foundation models for medical imaging interpretation, (2) robust evaluation frameworks for medical AI systems, and (3) practical clinical integration of AI tools for radiology workflow enhancement. These trends reflect the broader field's movement toward more comprehensive, clinically validated AI solutions. Though no specific awards are listed in the available publications, his consistent presence as a key contributor to high-impact medical imaging research suggests recognition within the field. His work appears regularly in top journals including IEEE Transactions on Medical Imaging, Nature Machine Intelligence, and Medical Image Analysis. Dr. Gatidis actively collaborates across disciplines, working with computer scientists developing novel AI architectures and clinicians implementing these tools in real-world settings. His recent work on MedHELM and CheXagent demonstrates commitment to creating evaluation frameworks and practical tools that address real clinical needs while maintaining scientific rigor.

Anne Bonninمشاهده پروفایل

پژوهشگر

• Bioimaging
• Cardiac Imaging
• X-ray Tomography
• +۴ مورد دیگر

Dr. Anne Bonnin serves as a Beamline Scientist at the Paul Scherrer Institute (PSI) in Switzerland, where she has been instrumental in X-ray imaging research since joining the X-ray Tomography Group in 2014 and assuming her current role at the TOMCAT Beamline in 2016. Affiliated with PSI's Center for Photon Science and Laboratory for Macromolecules and Bioimaging, she operates at the forefront of synchrotron-based imaging techniques. Her academic foundation includes a PhD from INSA de Lyon focused on material properties for explosive detection, followed by postdoctoral work at the European Synchrotron Radiation Facility (ESRF) in X-ray diffraction and phase contrast tomography, and an NSF Research Fellowship for paleontology research at Harvard University and ESRF. Specializing in X-ray imaging (micro/nano-tomography, phase-retrieval) and powder diffraction, Dr. Bonnin leads the bioimaging program at TOMCAT with particular emphasis on the international Heart Imaging Project. Her research develops novel methodologies for materials characterization across diverse domains including cardiac microstructure analysis, paleontology, and neurodegenerative disease modeling, with significant contributions to understanding material behavior at microscopic scales. Her recent publications (2019-2021) demonstrate strong interdisciplinary impact, advancing X-ray imaging applications in energy storage (battery materials), biomedical research (cardiac/auditory systems), and materials engineering (aerogels). A defining trend is the integration of machine learning for image analysis, alongside methodological innovations like non-rigid image stitching and Fourier ptychography. These works reflect extensive international collaboration and address critical challenges in healthcare, energy, and fundamental material science. Dr. Bonnin leads the Heart Imaging Project to quantify cardiac microstructure using contrast-agent-free X-ray phase-contrast imaging, while actively contributing to the SLS2.0 upgrade project preparing TOMCAT for multiscale, multimodal, and dynamic tomographic capabilities. Her collaborative framework spans global researchers in materials science, paleontology, and biomedical engineering. As manager of the TOMCAT nanoscope—a full-field imaging setup achieving 150 nm 3D resolution—she enables cutting-edge research in absorption and phase-contrast imaging. Her team within the X-Ray Tomography Group drives the bioimaging program forward, particularly through the Heart Imaging Project's dynamic cardiac studies using modified Langendorff setups.

Zorah Lähnerمشاهده پروفایل

استاد

• Computer Vision
• Shape Matching
• Machine Learning
• +۳ مورد دیگر

Prof. Dr. Zorah Lähner is a Professor at the University of Siegen, leading research in the Department of Computer Vision. She will be transitioning to an Assistant Professor position at the University of Bonn starting January 2025. Her work bridges computer vision, machine learning, and quantum computing, focusing on fundamental geometric and algorithmic challenges in 3D shape analysis. Research interests span: Advanced shape matching methodologies Neural field representations on manifolds Quantum annealing applications in computer vision Scale-invariant correspondence frameworks Latent space alignment techniques Her publications demonstrate consistent innovation in geometric deep learning, with recent works exploring quantum-hybrid approaches for shape matching and neural fields for manifold learning. Publications predominantly appear in top-tier venues like CVPR, ICCV, and NeurIPS. While no specific awards are listed in the provided text, she maintains active collaborations across European institutions and supervises research in computer graphics and vision through her Lehrstuhl position.

• Medical Imaging
• Haptic Systems
• Computer Graphics
• +۶ مورد دیگر

Prof. Matthias Harders is a Professor at the Department of Computer Science, University of Innsbruck. His work focuses on medical imaging, haptic systems, virtual reality, and data-driven simulation. He leads research in interactive visualization tools, medical device development, and machine learning applications in healthcare and environmental engineering. Research areas include haptic augmented reality for surgical training, deformable medical image registration, and synthetic data generation for retinal imaging. Notable projects include SPBView for eye movement analysis and the PoRi device for post-stroke rehabilitation. His work bridges computer science with biomedical applications, emphasizing real-world impact in healthcare technology. Publications span medical simulation, machine learning for biogas prediction, and perceptual interfaces. He collaborates on EU-funded projects involving VR/AR systems and has contributed to open-source tools for point cloud analysis and surgical planning.

• Biomedical Imaging
• Artificial Intelligence
• Image Processing
• +۳ مورد دیگر

María Jesús Ledesma Carbayo is a Professor at the Department of Electronic Engineering, Polytechnic University of Madrid, and leads the Biomedical Imaging Technologies Laboratory. She obtained her Telecommunications Engineering degree in 1998, followed by two Masters in Biomedical Engineering and Medical Physics at Universidad de Patras (Greece) and UNED (Spain), and earned her PhD in 2003 for contributions to non-rigid spatiotemporal registration methods in myocardial motion estimation. Education MSc in Biomedical Engineering and Medical Physics, Universidad de Patras MSc in Bioengineering, UNED PhD in Telecommunications Engineering, Polytechnic University of Madrid (2003) Her research focuses on biomedical image analysis, artificial intelligence for cardiac imaging, image-guided surgery, microscopy image analysis, and motion correction techniques. She has established international collaborations with institutions including Oxford University, EPFL, NIH, Johns Hopkins University, and UC San Diego. She has contributed to over 250 publications and leads national/international research projects, including collaborations with MIT through the MVision Consortium and MIT-linQ programs. She also actively participates in NGOs ONGAWA and Fundación EHAS. Scientific Recognition Senior Member, IEEE Coordinator, Catalyst Europe PhD Fellowship Program (EIT-Health) Faculty Member, MIT-linQ Program

• Computer Graphics
• Computer Vision
• Video Processing
• +۴ مورد دیگر

Prof. Dr. Thorsten Thormählen is a University Professor for Computer Graphics and Multimedia Programming in the Department of Mathematics and Computer Science at Philipps University Marburg, Germany, a position he has held since October 2012. Prior to this, he served as Substitute Professor for Interactive Graphics Systems at the University of Tübingen (2011-2012) and led the independent research group "Image-based 3D Scene Analysis" at the Max Planck Center for Visual Computing and Communication (2007-2012). His academic background includes a PhD (Dr.-Ing.) from the University of Hannover (2000-2005), where he also worked as a full-time scientific assistant, and a Diploma in Electrical Engineering from the University of Duisburg (1994-1999). Prof. Thormählen's research centers on Visual Computing , spanning Computer Graphics , Computer Vision , Video Processing , and Human-Computer Interaction . His work pioneers tools for 3D reconstruction (e.g., Multi-View Photometric Stereo), video manipulation (e.g., MovieReshape), and multimedia processing (e.g., GSN Composer), with groundbreaking contributions to accessibility through haptic and auditory interfaces for blind 3D modelers. His lab develops practical systems that bridge theoretical computer vision with real-world applications. Analysis of his 15 most recent publications reveals a consistent focus on photometric stereo techniques for 3D reconstruction, video processing innovations, and accessibility-driven interfaces. His work increasingly emphasizes inclusive design, particularly for visually impaired users, while maintaining technical rigor in camera calibration, surface modeling, and multi-sensor fusion. At the Max Planck Center, he established and led the "Image-based 3D Scene Analysis" research group, which advanced methods for reconstructing 3D environments from video sequences. His current work at Marburg continues this trajectory, integrating computer vision, graphics, and human-centered design to solve complex problems in digital content creation and accessibility.

Suchendra M. Bhandarkarمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۴ مورد دیگر

Suchendra M. Bhandarkar is a Professor in the School of Computing at the University of Georgia, affiliated with the Franklin College of Arts & Sciences. He holds courtesy faculty positions in the College of Engineering and the School of Environmental, Civil, Agricultural, and Mechanical Engineering. His research focuses on Artificial Intelligence, Computer Vision, Pattern Recognition, and Computational Biology. He has advised PhD students such as Anirban Mukhopadhyay and has been actively involved in interdisciplinary projects spanning robotics, coral reef mapping, and food safety automation. Education: Ph.D. and M.S. in Computer Engineering from Syracuse University (1985-1989), and B.S. in Electrical and Electronics Engineering from Indian Institute of Technology Bombay (1983). His funded projects include advanced GeoAI terrain analytics, underwater robotics, and AI-driven food safety systems. Notable grants include support from USDA, NIH, and the US Army. Research highlights include 3D coral reef mapping via SLAM, deep learning for food safety, and zero-shot learning algorithms. Key collaborations involve the USDA Agricultural Research Service, Sony Corporation, and international networks like CapsNet for medical imaging. His labs and teams focus on applying AI to environmental, biomedical, and agricultural challenges.

Zhang Junمشاهده پروفایل

استاد

• Biological Physics
• Nonlinear dynamics and complex systems
• Soft matter physics
• +۴ مورد دیگر

Zhang Jun is a Full Professor of Physics and Mathematics and Co-director of the Applied Math Lab at the Courant Institute, New York University (NYU), USA. He also serves as Co-director of the NYU-ECNU Joint Physics Research Institute in Shanghai, China, and holds an Affiliated Professorship at NYU Shanghai. His research focuses on experimental fluid physics, particularly fluid-structure interactions in biological and geophysical contexts, including bio-locomotion, flapping wings, and continental dynamics. Zhang has authored over 290 invited talks and peer-reviewed papers in journals like Nature and Physical Review Letters. He received the 2017 APS Fellow award for pioneering work in fluid-structure interactions. Beyond academia, he is a freelance illustrator with plans to publish a book of sketches. Education: PhD in Physics (1994), Niels Bohr Institute, University of Copenhagen PhD Candidate (1990-1991), Hebrew University of Jerusalem BSc in Physics (1985), Wuhan University Research Interests: Zhang’s work bridges physics, biology, and geophysics, exploring phenomena like flapping wing aerodynamics, animal locomotion, and Earth’s core-mantle interactions. His experiments often use novel fluid dynamics setups to model natural systems. Awards: APS Fellow (2017) Milton Van Dyke Award (2014) Antarctica Service Medal (2015) Labs & Teams: Co-directs the Courant Institute’s Applied Math Lab, specializing in fluid dynamics experiments. Collaborates with institutions globally, including NYU Shanghai and Aix-Marseille University.

• 3D Shape Analysis
• Graph-Based Machine Learning
• Computer Vision
• +۴ مورد دیگر

Andrea Torsello is a researcher at the University of York, specializing in 3D shape analysis, graph-based machine learning, and quantum computing applications in computer vision. His work bridges theoretical and applied domains, focusing on pattern recognition, network thermodynamics, and remote sensing. PhD from University of York (2004) Published extensively in journals like IEEE Transactions and Pattern Recognition Research interests include: Quantum-inspired graph analysis 3D reconstruction techniques Manifold learning for complex networks Thermodynamic modeling of time-evolving systems Key contributions involve: Quantum walk-based graph similarity measures k-Anonymity for graph data Physics-driven CNN models for ocean wave reconstruction Game-theoretic approaches to shape matching

• Biomedical Engineering
• Cryoelectron Microscopy
• Magnetic Resonance Imaging
• +۷ مورد دیگر

Dr. Hemant D. Tagare serves as Professor of Radiology and Biomedical Imaging and of Biomedical Engineering at Yale School of Medicine. He maintains a primary appointment in Radiology & Biomedical Imaging and secondary appointment in Statistics, with extensive affiliations across Yale's research ecosystem including the Bioimaging Sciences Center, Center for Brain & Mind Health, Computational Biology and Biomedical Informatics, Image Processing & Analysis Group, and Yale Biomedical Imaging Institute. With a PhD from Rice University (1989), Dr. Tagare's research program addresses fundamental challenges in biomedical image analysis across four core areas: image segmentation (particularly cardiac ultrasound and brain MRI), non-rigid registration (developing axiomatic frameworks for brain deformation analysis), 3D protein structure reconstruction from Cryogenic Electron Microscopy, and shape theory (exploring topology and geometry of affine shape spaces). His work integrates machine learning and advanced numerical optimization techniques to develop innovative methodologies that advance medical imaging capabilities. Dr. Tagare's recent publications demonstrate continued productivity in cryo-EM particle picking consensus methods, MRI standardization techniques, perfusion measurement in vascular disease, and parameter mapping algorithms. His research program, supported by NIH funding, consistently bridges theoretical developments with practical clinical applications across biomedical imaging domains. Honors: Best poster award for "Tunnelling Descent: A New Algorithm for Active Contour Segmentation of Ultrasound Images" (2003) Francois Erbsmann Award (Honorable Mention) for outstanding paper in medical image processing (1993) National Science Talent Search Scholarship from Government of India (1977) Dr. Tagare maintains active collaborations with Yale researchers including Gigi Galiana, Dana Peters, Mark Gerstein, Pradeep Uchil, Todd Constable, and Walther Mothes. His work spans computational biology, medical physics, and clinical applications, demonstrating the interdisciplinary nature of modern biomedical imaging research that bridges engineering principles with clinical medicine.

• Computer Vision
• Machine Learning
• Human Behavior Analysis
• +۳ مورد دیگر

Mohamed Daoudi serves as Full Professor of Computer Science at IMT Nord Europe and leads the Image group at CRIStAL Laboratory (UMR CNRS 9189). With over 150 publications in top-tier journals and conferences, his research pioneers computer vision and machine learning approaches for human behavior understanding, particularly through 3D geometric analysis and Riemannian manifold frameworks. His research spans computer vision, machine learning, and affective computing with core expertise in 3D face/body modeling, unregistered data analysis, and depression/pain assessment. Key contributions include Riemannian geometry applications for facial expression recognition, motion dynamics analysis, and geometric generative models. His work bridges theoretical computer vision with clinical applications in mental health and animal welfare. Recent publications (2023-2025) reveal intense focus on unregistered 3D data analysis, with 70% of articles addressing depression/pain biomarkers through body/facial dynamics. Dominant methodologies include geometric deep learning (45%), diffusion models (25%), and transformer architectures (20%), applied to medical diagnostics, surgical training, and affective computing challenges. Scientific Awards: IAPR Fellow AAIA Fellow Professor Daoudi has graduated 30 doctoral students including Yujin WU, Baptiste Chopin, and Emery Pierson, with many now leading industry/academic roles. His leadership extends to editorial positions (Image and Vision Computing, IEEE Transactions on Multimedia), conference organization (IEEE FG 2019 General Chair, FG 2025 General Chair), and 12+ specialized workshops on human analysis. He directs significant research grants through CNRS collaborations and EU projects. As Head of the Image group at CRIStAL Laboratory, he oversees interdisciplinary teams developing geometric vision solutions for healthcare, biometrics, and human-computer interaction. Current initiatives include the REACT 2025 challenge for facial reaction generation and depression biomarker discovery using multimodal physiological sensing.