جست‌وجوی استاد و پژوهشگر

• Biomedical Image Processing
• Mathematical Analysis
• Computational Geometry
• +۳ مورد دیگر

Giacomo Nardi is an Associate Professor (Maître de Conférences) at INSA Rouen starting September 2025, holding a PhD in Mathematics from Sorbonne University. His research bridges mathematical analysis and biomedical image processing through extensive collaborations with the Institut Pasteur in Paris, focusing on developing advanced mathematical frameworks for biological imaging applications. His educational background includes: PhD in Mathematics, Sorbonne University (Pierre and Marie Curie), thesis: "On a characterization of the relaxation of a generalized Willmore functional" under supervisors S. Masnou and E. Mascolo. Nardi's work spans Biomedical Image Processing and Mathematical Analysis , evolving from foundational contributions in calculus of variations (Willmore functionals, BV functions) to cutting-edge biomedical applications. His current research employs topological data analysis and geometric methods to solve complex biological imaging problems including 3D segmentation of hematopoietic stem cells, receptor dynamics analysis, and retinal degeneration quantification. This interdisciplinary approach integrates deep mathematical theory with practical biological challenges through close partnerships with biologists at the Institut Pasteur. Analysis of his publication trajectory reveals a decisive shift toward biomedical applications since 2016, with recent work (2022-2025) dominating in high-impact imaging journals and conferences. His research consistently applies geometric measure theory and functional analysis to innovate in cellular dynamics modeling and 3D image segmentation, demonstrating exceptional versatility across pure mathematics and translational biomedical research. Nardi actively mentors the next generation of researchers: Postdoctoral supervision: Somoballi Ghoshal (2024-present, Institut Pasteur) on persistent homology-based 3D cell segmentation in cardiac tissue. PhD supervision: Benjamin Dorra (defense March 5, 2025, Medetia/Institut Pasteur) on AI-driven histopathology for ciliopathy models, resulting in ISBI 2024 award-winning publications. He co-develops the open-source Fshapestk (Functional shapes toolkit) and collaborates with J.-C. Olivo-Marin's Quantitative Image Analysis Unit at the Institut Pasteur, maintaining strong ties between theoretical mathematics and experimental biology through ongoing joint projects in cellular dynamics and tissue imaging.

Coen Hurkmansمشاهده پروفایل

استاد

• Radiation Therapy
• Medical Imaging
• Artificial Intelligence
• +۳ مورد دیگر

Coen Hurkmans is a part-time professor in Radiation Therapy Clinical Physics at Eindhoven University of Technology (TU/e), holding a joint chair between the departments of Electrical Engineering and Experimental Physics. He also serves as the Head Clinical Physicist for Radiotherapy at Catharina Hospital in Eindhoven, a position he has held since 2011. MSc in Experimental Physics from Utrecht University (1995) PhD from University of Amsterdam (2001) Hurkmans’ research focuses on advancing radiation therapy through MR imaging and artificial intelligence , aiming to enable real-time adaptive replanning and improve clinical physics applications. His work bridges technical expertise from TU/e with clinical practice at Catharina Hospital. His recent publications highlight AI-driven synthetic CT image generation , automated breast cancer treatment planning , and dosimetric evaluations for cervical cancer radiotherapy , reflecting a strong trend toward AI integration in radiation oncology. He is a member of several professional societies, a reviewer for scientific journals, and an editor of two journals. Although specific awards are not mentioned in the provided text, his extensive publication record (>100 peer-reviewed papers) and invitations to conferences underscore his academic influence. Hurkmans collaborates with industry leaders like Viewray , Raysearch , and Elekta , focusing on translational research that connects technical advancements to clinical practice. His work aligns with UN Sustainable Development Goals related to health and well-being .

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.

Markus Grossمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Graphics
• +۸ مورد دیگر

Markus Gross is a Professor of Computer Science at ETH Zurich, where he founded the Computer Graphics Laboratory in 1994. He also serves as the Chief Scientist of the Walt Disney Studios and Director of DisneyResearch|Studios, a position he has held since 2008. His work bridges academia and industry, with research that has been applied in Hollywood films, sports broadcasting, and medical applications. Professor Gross received his Master of Science in electrical and computer engineering and his Ph.D. in computer graphics and image analysis from Saarland University in Germany in 1986 and 1989. His research spans multiple domains of computer graphics and visual computing. Early in his career, he pioneered point-based graphics techniques that offered alternatives to traditional triangle-based rendering pipelines. More recently, his work has focused on digital humans, AI characters, and machine learning applications for visual computing. His research has led to significant practical applications, including the Medusa capture system used in Hollywood films, the blue-c immersive telepresence system, and the Liberovision technology now used by major sports broadcasters. Analysis of his recent publications reveals a strong focus on neural rendering techniques, particularly around Gaussian splatting and diffusion models. His work increasingly integrates AI with traditional computer graphics methods, with applications in digital humans, medical visualization, and video processing. Many papers demonstrate practical applications in film production, medical treatment planning, and interactive systems. Professor Gross has received numerous prestigious awards throughout his career: 2024 Eurographics Gold Medal 2021 Steven Anson Coons Award for outstanding creative contributions to computer graphics 2019 Technical Achievement Award from the Academy of Motion Picture Arts and Sciences 2013 Karl Heinz Beckurts-Preis 2013 Konrad-Zuse-Medaille für Informatik 2013 Technical Achievement Award from the Academy of Motion Picture Arts and Sciences 2012 Academy Sci-Tech Oscar award for Wavelet Turbulence Professor Gross has mentored numerous Ph.D. students throughout his career, with 20 Ph.D. students contributing to his blue-c project alone. His research has been supported by significant funding from both academic and industry sources, enabling the creation of multiple startups including Cyfex, Novodex, LiberoVision, Dybuster, and Animatico (acquired by Nvidia in 2022). He leads the Computer Graphics Laboratory at ETH Zurich and DisneyResearch|Studios, fostering collaboration between academic research and practical industry applications. His teams have developed groundbreaking technologies that have impacted film production, sports broadcasting, medical visualization, and educational technology.

Yi Panمشاهده پروفایل

استاد

• Parallel and Cloud Computing
• Wireless Networks
• Bioinformatics
• +۳ مورد دیگر

Dr. Yi Pan is a Distinguished University Professor and Chair of the Department of Computer Science at Georgia State University. He holds a B.Eng. and M.Eng. from Tsinghua University (China) and a Ph.D. from the University of Pittsburgh (USA). His research focuses on parallel computing, wireless networks, bioinformatics, and machine learning, with over 330 publications and 39 authored/edited books. Dr. Pan has organized international conferences and delivered over 40 keynote speeches globally. Education: B.Eng., Computer Engineering, Tsinghua University, 1982 M.Eng., Computer Engineering, Tsinghua University, 1984 Ph.D., Computer Science, University of Pittsburgh, 1991 Research Interests: Dr. Pan’s work spans parallel and cloud computing, wireless networks, and bioinformatics. His contributions include scalable algorithms for functional module discovery in protein networks, self-defense mechanisms for cyber threats, and efficient routing protocols for sensor networks. Recent efforts integrate AI into healthcare, such as lesion localization in OCT imaging and autism spectrum disorder diagnosis via EEG analysis. Awards & Recognition: IEEE Distinguished Speaker (2000–2002) Yamacraw Distinguished Speaker (2002) Listed in Who’s Who in America and Who’s Who in Computational Science Awards from NSF, AFOSR, IBM, and Mellon Foundation Advising & Leadership: As department chair, Dr. Pan oversees interdisciplinary collaborations between Computer Science and Computer Information Systems. He has advised numerous students and holds editorial roles in 15 journals, including several IEEE Transactions. His grants focus on advancing AI-driven healthcare, cybersecurity, and computational biology. Labs & Teams: Leads research initiatives in bioinformatics, medical AI, and parallel computing, collaborating with industry partners like IBM and Shell Oil. Active in organizing conferences such as the IEEE International Conference on Bioinformatics and Biomedicine.

Jan Sijbersمشاهده پروفایل

استاد

• Image Reconstruction
• Medical Imaging
• Quantitative MRI
• +۳ مورد دیگر

Jan Sijbers is a Full Professor at the University of Antwerp , affiliated with the Department of Physics . He serves as Spokesperson of the imec-Vision Lab and is a co-founder of spin-offs IcoMetrix and Deltaray . His academic role includes a 10% position as Research Director at imec. Research Interests: Quantitative Magnetic Resonance Imaging (MRI), Tomographic Image Reconstruction, Medical Imaging Algorithms Editorial Roles: Senior Area Editor (IEEE Transactions on Image Processing), Associated Editor (IEEE Transactions on Computational Imaging) Teaching: Courses in Physics, Mathematics, Signal/Image Processing, and X-ray Microtomography Research Trends: His 2025 publications focus on X-ray phase contrast imaging, zebrafish models for bone disease, mesh-based CT reconstruction, and AI-driven biomedical imaging. Earlier works emphasize statistical signal processing in MRI and discrete tomography algorithms. PhD Advisees: He has supervised 68 PhD theses since 2004, with recent topics spanning edge illumination X-ray imaging, 3D knee morphology, and advanced MRI relaxometry. Laboratory Leadership: As head of imec-Vision Lab, he leads research in computational imaging, with applications in medical diagnostics and industrial X-ray systems.

• Medical Imaging
• Deep Learning
• Cardiac Ultrasound
• +۷ مورد دیگر

Dr. Qingjie Meng is a Researcher in the Department of Computing at Imperial College London, affiliated with the Faculty of Engineering. Their work focuses on advancing AI-driven medical imaging technologies, particularly in cardiac ultrasound, fetal anomaly detection, and MRI analysis. Meng's research emphasizes privacy-preserving data solutions and generative models for healthcare applications. Research interests include deep learning for medical image synthesis, motion tracking in MRI, and real-time AI applications in ultrasound screening. Recent work explores foundational models like EchoFlow for cardiac imaging and SACB-Net for medical registration tasks. Publications highlight trends in generative AI for medical data, robust segmentation techniques, and multi-view cardiac tracking. Meng's contributions address challenges in fetal anomaly detection and respiratory motion correction in MRI scans. No awards or advising roles are explicitly listed in the provided text. Their work contributes to labs focused on biomedical AI and healthcare informatics, though specific lab affiliations are not detailed.

• Machine Learning
• Computer Vision
• Signal Processing
• +۶ مورد دیگر

Giacomo Boracchi is a researcher at the Polytechnic University of Milan , focusing on machine learning, computer vision, and signal processing. His work spans anomaly detection, change detection in data streams, 3D imaging, and biomedical applications. Key Collaborations : Diego Carrera, Luca Magri, Cesare Alippi Industries : Embedded systems, medical imaging, environmental monitoring Recent research explores adaptive Kalman filtering for battery estimation, zero-shot anomaly detection, and explainable AI for vision-language models. Publications highlight applications in waste sorting, histological data generation, and cardiac monitoring. His technical focus includes convolutional networks, ensemble learning, and domain adaptation. Boracchi's work bridges theoretical advancements with real-world systems in fraud detection, semiconductor manufacturing, and wearable health devices.

Mohammad Amir Owrangiمشاهده پروفایل

دانشیار

• Radiation Oncology
• Medical Physics
• Brachytherapy
• +۳ مورد دیگر

Mohammad Amir Owrangi serves as Associate Professor in Radiation Oncology at Baylor College of Medicine (Houston) and University of Texas Southwestern (Dallas), with prior appointments as Assistant Professor at both institutions and the University of Toronto. His expertise bridges medical physics and clinical radiation oncology, focusing on advancing precision radiotherapy through technological innovation. His academic foundation includes a BSc and MSc in Physics and Radiation-Medicine Engineering from Shiraz University (Iran), followed by a PhD in Biomedical Engineering from The University of Western Ontario (Canada), and a CAMPEP-accredited residency in Therapeutic Radiologic Medical Physics at University of Michigan Health System. Dr. Owrangi's research centers on solving critical challenges in image-guided radiotherapy, particularly MRI-based treatment planning for gynecological cancers. His work pioneers synthetic CT generation using deep learning to eliminate CT simulation needs, optimizes brachytherapy applicators for cervical cancer, and develops metal artifact reduction techniques. Recent publications demonstrate increasing integration of AI methods to improve dosimetric accuracy and workflow efficiency in radiation therapy. Analysis of his 15 most recent publications (2017-2024) reveals three dominant research thrusts: (1) MRI-only radiotherapy workflows with synthetic CT generation using deep learning/GANs, (2) brachytherapy optimization for cervical cancer through applicator design and dosimetry, and (3) artifact reduction in multi-modal imaging. His work consistently targets clinical translation, with 70% of recent publications focusing on gynecological applications. Scientific Awards: No awards explicitly mentioned in source materials Academic advising and research grant details were not provided in available documentation. His collaborative network spans Baylor College of Medicine, UT Southwestern, University of Toronto, and international institutions including Shiraz University and University of Western Ontario, with frequent co-authorship among medical physicists and radiation oncologists. While specific laboratory infrastructure isn't detailed, his research requires advanced capabilities in MRI/CT image processing, Monte Carlo simulation, and deep learning development, suggesting access to high-performance computing resources and clinical imaging datasets at his affiliated institutions.

Parvaneh Saeediمشاهده پروفایل

استاد

• Computer Vision
• Biomedical Engineering
• Machine Learning
• +۵ مورد دیگر

Dr. Parvaneh Saeedi is a Professor and Associate Dean, Research & Graduate Studies in the School of Engineering Science at Simon Fraser University (SFU). She holds a Ph.D. in Electrical Engineering from the University of British Columbia (2004) and P.Eng. credentials. Her research focuses on computer vision, machine learning, and biomedical applications, with contributions to robotics, medical imaging, and remote sensing. Affiliations: Faculty of Applied Sciences, Simon Fraser University Education: B.Sc. (Iran University of Science and Technology), M.Sc. and Ph.D. (University of British Columbia) Her work spans automated image analysis systems, 3D reconstruction, and medical applications such as embryo grading and organ segmentation. She leads the Laboratory for Robotic Vision and has held roles at MacDonald Dettwiler and Associates and the Canada’s Michael Smith Genome Sciences Centre. Recognized with the NSERC University Faculty Award (2007), her teaching includes courses like ENSC 460/895 (Digital Image Processing).

• Image Processing
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Weiwei Cui is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. She specializes in image processing, machine learning, and medical imaging applications. Her research focuses on advancing computational methods for medical diagnostics, particularly in dental imaging and ophthalmology. She teaches courses on Image Processing and Machine Learning, integrating theoretical foundations with practical applications in computer science and engineering. Her research contributions include developing novel datasets like Ctooth+ and CARDIAN, advancing semi-supervised learning techniques for medical image segmentation, and applying transformer networks to conversational AI. Key areas of expertise include deep learning frameworks, neural networks, and biomedical signal processing. She has published extensively on topics ranging from dental CT segmentation to real-time cardiovascular imaging analysis. Dr. Cui’s work bridges computer science methodologies with healthcare challenges, emphasizing practical solutions for diagnostic accuracy and clinical decision-making. Her datasets and benchmarking efforts contribute to reproducible research in medical imaging communities. She actively engages in interdisciplinary collaborations to address real-world healthcare technology challenges.

Remco Duitsمشاهده پروفایل

دانشیار

• Differential Geometry
• Mathematical Image Analysis
• Geometric Deep Learning
• +۲ مورد دیگر

Remco Duits is Associate Professor at Eindhoven University of Technology, leading research in mathematical image analysis and geometric deep learning. His work develops PDE-based methods on Lie groups for medical imaging, crack detection in infrastructure, and optimal transport. Key innovations include equivariant neural networks for homogeneous spaces and geodesic tracking algorithms for vascular analysis. Dr. Duits holds an ERC Starting Grant (2013) for Lie Group Analysis in Medical Imaging and an NWO VICI Grant (2021) for Geometric Learning. His team pioneers data-driven Cartan connections for vascular tree tracking and PDE-CNN architectures with axiomatic foundations.

Mitko Vetaمشاهده پروفایل

دانشیار

• Medical Image Analysis
• Deep Learning
• Histopathology
• +۳ مورد دیگر

Mitko Veta is an Associate Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), specializing in medical image analysis. His research focuses on developing deep learning methods for histopathology image analysis to enhance diagnostic accuracy and reduce pathologist workload. He leads the Medical Image Analysis group, contributing to automated quantitative tools for pathology reporting and treatment planning. Academic Background: Veta earned his M.Sc. in Electrical Engineering from Ss. Cyril and Methodius University (Macedonia, 2009), followed by a Ph.D. in histopathology image analysis at University Medical Center Utrecht (2014). He joined TU/e as a postdoc in 2014 and became an assistant professor in 2016, advancing to his current role in 2021. Research Interests: His work spans histopathology image analysis, deep learning, domain adaptation, and applications in oncology (e.g., breast cancer, melanoma). He develops algorithms for mitosis detection, tumor-infiltrating lymphocyte assessment, and AI-driven clinical decision support. Key contributions include the MIDOG++ dataset and the LYSTO benchmark. Projects: He co-leads the Spectralligence AI project (2021–2024), focusing on AI-driven medical imaging solutions. His research addresses clinical challenges like myocardial scar quantification and checkpoint inhibitor treatment outcomes. Teaching: Veta teaches courses such as Machine Learning in Medical Imaging and Biology and AI for Medical Image Analysis , emphasizing practical applications of AI in healthcare.

Fons van der Sommenمشاهده پروفایل

دانشیار

• Medical Image Analysis
• Computer-Aided Diagnosis
• Artificial Intelligence
• +۲ مورد دیگر

Fons van der Sommen is an Associate Professor in Electrical Engineering at Eindhoven University of Technology, specializing in Video Coding & Architectures. He leads research on computer-aided detection systems for early cancer diagnosis, particularly focusing on esophageal and colorectal neoplasia through advanced AI and computer vision techniques. His research interests span medical image analysis, AI-assisted diagnostics, and developing robust systems for clinical deployment. Recent publications focus on overcoming real-world implementation challenges of AI in endoscopy and enhancing the trustworthiness of diagnostic systems. Recent research trends show strong emphasis on surgical AI applications (robot-assisted procedures), generative models for medical data augmentation, and quality assurance frameworks for clinical AI deployment. His work integrates deep learning with clinical validation across gastrointestinal and pulmonary oncology. TU/e Best PhD Thesis Award (2018) Best Poster Presentation (2017, 2013) He coordinates multiple research projects including TASTI-XECS221002 (Advanced AR for AI-based Servitization) and XL-ARGOS (extended reality solutions). Manages collaborations with medical centers on AI implementation for cancer screening.

Zhibin Chenمشاهده پروفایل

پژوهشگر ارشد

• Neurology
• Epidemiology
• Health Economics
• +۳ مورد دیگر

Dr Zhibin (Ben) Chen is a Senior Research Fellow in the Department of Neuroscience at Monash University's School of Translational Medicine. He holds dual expertise as a neuroscientist and Chartered Statistician (Royal Statistical Society, UK). His research focuses on statistical modeling, epidemiology, and health economics applied to neurological diseases, particularly epilepsy, traumatic brain injury, and dementia. He secured $6.3 million in grants as Chief Investigator, including NHMRC Early Career Fellowship (2018–2022). Dr Chen leads over 50 cohort studies and clinical trials, authoring 75 articles in journals like JAMA Neurology and BMJ . Education: PhD in Clinical Pharmacology, University of Melbourne (2016) Master of Biostatistics, University of Sydney (2013) Bachelor of Science (Honours) in Neuroscience, University of Melbourne (2012) Bachelor of Commerce (Accounting), Macquarie University (2007) Research Interests: Chen’s work integrates machine learning, clinical epidemiology, and health economics to improve epilepsy management. Key areas include: Modeling treatment responses to antiseizure medications Predicting seizure outcomes using deep learning Evaluating the economic burden of neurological disorders Optimizing personalized therapy strategies Grants & Awards: NHMRC Early Career Fellowship (GNT1156444) IDEAS Grant (GNT2010382-CIB) Clinical Trials Grant (GNT2014800-CIB) Collaborations: Active partnerships with institutions like the University of Melbourne, Epilepsy Foundation Australia, and international epilepsy networks. Leads projects such as the PERSONAL trial for personalized epilepsy medication selection. Labs/Teams: Oversees interdisciplinary teams combining neurology, biostatistics, and health economics to advance translational research in epilepsy management.