جست‌وجوی استاد و پژوهشگر

LAMBERT Zoéمشاهده پروفایل

دانشیار

• Signal and image processing
• Medical imaging
• Deep learning
• +۷ مورد دیگر

Zoé Lambert is an Associate Professor at the University of Rouen Normandy and a member of the LITIS laboratory. She completed her PhD in Applied Mathematics (2019–2022) at INSA Rouen Normandie, funded by the Normandy Region, with a thesis on hybrid variational and deep learning approaches for medical image segmentation. Prior to her PhD, she worked as a Research Engineer at INSA Rouen (2018–2019) on weakly supervised learning for medical imaging and as a Data Scientist Intern at AID in Paris (2018). Her current research focuses on integrating geometric and topological constraints into deep learning models for medical imaging and coastal cliff crack detection. Research Interests Zoé develops advanced image segmentation methods combining variational models and deep learning, with emphasis on: Topological prescriptions in CNNs Geometric constraints for anatomical accuracy Registration techniques via nonlinear elasticity Public dataset creation for crack segmentation Directional total variation decomposition models AI applications in energy transition (Prioreno tool) Scientific Contributions She co-organized the SegTHOR challenge at IEEE ISBI’19, which automated segmentation of thoracic organs-at-risk for radiotherapy. Her work on weighted total variation regularization and Mumford-Shah terms has improved segmentation reliability in scenarios with limited data. She also contributed to the DEPHY3GEO project at CEREMA (2023–2024), focusing on cliff crack detection using thermal infrared imagery. Honors and Collaborations Zoé won first place in the Normandy Datathon (2023) for her energy vulnerability decision support tool. She served on INSA Rouen’s Scientific Council (2020–2022) and laboratory council (LMI). Her collaborations span LITIS, LMI, CEREMA, INRIA, and international institutions like INSA Lyon and the University of Bordeaux.

• Medical Imaging
• Quantitative MRI
• Magnetic Resonance Imaging (MRI) Techniques
• +۵ مورد دیگر

Rudolf Stollberger is a Professor at TU Graz's Institute of Biomedical Imaging. His research focuses on advanced MRI techniques, including CEST-MRI, arterial spin labeling, quantitative MRI parameter quantification, and optimal control-based RF pulse design. He holds academic appointments in medical technology and has expertise in high-field MRI (7T), image reconstruction algorithms, and deep learning applications in medical imaging. Education and affiliations are not explicitly detailed, but his work spans interdisciplinary areas such as neuroimaging, perfusion quantification, and biomedical signal processing. His research portal is accessible via http://www.imt.tugraz.at/ . Key research interests include developing robust RF pulses for inhomogeneous fields, improving CEST MRI contrast via optimal control, and enhancing real-time fMRI neurofeedback systems. He has contributed to model-based reconstructions for IVIM and DTI, as well as automated segmentation methods using deep learning. Notable articles from 2025–2023 highlight innovations in MRI sequence optimization, artifact reduction, and explainable AI for medical imaging datasets. His work addresses challenges in high-resolution ASL perfusion imaging, T1 quantification, and lung vessel morphology analysis.

Thomas Pockمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Mathematical Optimization
• +۷ مورد دیگر

Thomas Pock is a Professor of Computer Science at Graz University of Technology, holding the AIT Stiftungsprofessur for Mobile Computer Vision. He is affiliated with the Institute for Computer Graphics and Vision (ICG) within the Faculty of Computer Science and serves as a principal scientist at the Austrian Institute of Technology (AIT), Center for Vision, Automation & Control. He leads the Vision, Learning and Optimization (VLO) research group, which focuses on mathematical modeling and optimization in computer vision. His research interests lie at the intersection of computer vision, image processing, and mathematical optimization. Specifically, he develops mathematical models for computer vision and efficient convex and non-smooth optimization algorithms , particularly for mobile scenarios. His recent work increasingly integrates variational methods with deep learning, especially in solving inverse problems in imaging such as medical reconstruction and deblurring. The trends in his recent publications show a strong emphasis on deep learning for inverse problems , variational networks , and learned optimization . His group explores how to combine classical mathematical models with data-driven deep learning approaches to achieve stable, interpretable, and high-performance solutions in image reconstruction and processing. His scientific achievements have been recognized with several prestigious awards: START Prize, Austrian Science Fund (FWF), 2013 German Pattern Recognition Award, DAGM, 2013 ERC Starting Grant, European Research Council, 2014 Thomas Pock actively mentors students and leads a research group of 10 PhD students and 2 postdocs. He has secured significant research grants, including the ERC Starting Grant, which supports his foundational work. He is also engaged in scientific communication, giving invited talks at international venues such as SIAM and co-organizing the IMAGINE One World seminar series to foster global collaboration in imaging and inverse problems. He leads the Vision, Learning and Optimization (VLO) group at the Institute for Computer Graphics and Vision. The group develops mathematical models and efficient algorithms for computer vision and image processing, with a focus on mobile applications. The team includes multiple PhD students and postdoctoral researchers and has produced notable software and publications in top venues.

• Nonlinear Functional Analysis
• Calculus of Variations
• Partial Differential Equations
• +۵ مورد دیگر

Dr. Joan Duran Grimalt is an Associate Professor at the University of the Balearic Islands (UIB) , affiliated with the Department of Mathematical Sciences and Computer Science . He is a member of the Mathematical Image Analysis (TAMI) research group and Institute for Community Code Computing Applications (IAC3) . His work bridges nonlinear functional analysis , calculus of variations , and deep learning for applications in image processing and computer vision . PhD in Mathematics (2016) from UIB MSc in Advanced Mathematics and Mathematical Engineering (2011) from UPC BSc in Mathematics (2010) from UIB His research integrates partial differential equations , convex optimization , and deep unfolding architectures to solve problems in satellite imaging , hypersharpening , and low-light image enhancement . Recent work focuses on nonlocal regularization, multi-head attention mechanisms, and model-guided neural networks. He has led two research projects and collaborated with institutions such as the French National Centre for Space Studies (CNES) and the Balearic Islands Oceanographic Centre . Key contributions include: State-of-the-art methods for pansharpening and hypersharpening using nonlocal modules Unfolding frameworks combining variational models with deep learning Retinex-based enhancement techniques for low-light imaging He has supervised PhD students Daniel Torres and Francesc Alcover and taught courses like Partial Differential Equations , Fundamentals of Mathematics , and Mathematical Models for Image Restoration across multiple degrees including Mathematics, Computer Engineering, and Chemistry. His personal website provides further details.

Nicholas Protonotariosمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Image Reconstruction
• Nuclear Medicine
• +۳ مورد دیگر

Dr. Nicholas Protonotarios is a Visiting Research Associate at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, affiliated with the Cambridge Mathematics of Information in Healthcare Hub (CMIH). His work focuses on mathematical foundations of medical imaging reconstruction techniques for PET, SPECT, and MRI systems, bridging theoretical mathematics with clinical applications. His research centers on inverse problems in medical imaging, with expertise in Radon transform theory, spline-based reconstruction methods, and Chebyshev polynomial applications for tomography. Recent work expands into privacy-preserving federated learning architectures and graph neural networks for healthcare data analysis, emphasizing secure medical device networks and efficient resource allocation in distributed systems. Key contributions include novel regularization techniques for emission tomography and motion-compensated reconstruction algorithms. Analysis of his 15 most recent publications reveals a clear evolution from foundational mathematical imaging techniques toward AI-driven healthcare solutions. His work consistently addresses computational challenges in medical image reconstruction while increasingly incorporating privacy-preserving machine learning frameworks. Dominant themes include tomographic reconstruction algorithms (40% of publications), federated learning for medical data (30%), and cybersecurity for medical devices (20%), demonstrating a strategic shift toward interdisciplinary healthcare technology solutions. No scientific awards were documented in available sources. There is no publicly available information regarding graduate student mentorship or research grant acquisitions for Dr. Protonotarios. He operates within the Cambridge Mathematics of Information in Healthcare Hub (CMIH), a multidisciplinary research center at the Centre for Mathematical Sciences focusing on mathematical approaches to healthcare data challenges. The hub facilitates collaborations between mathematicians, computer scientists, and clinical researchers to develop innovative solutions for medical imaging, diagnostics, and secure health data infrastructure.

Hong Jae-heeمشاهده پروفایل

دانشیار

• Food Sensory Science
• Consumer Sensory Evaluation
• Ethnic Food Acceptance
• +۳ مورد دیگر

Hong Jae-hee is Associate Professor in the Department of Food and Nutrition at the College of Human Ecology, Seoul National University . Since September 2017 she has led the Sensory Science Laboratory, focusing on measurement and interpretation of sensory characteristics and consumer responses to foods. Education: Bachelor of Science in Food and Nutrition, Ewha Womans University (1990–1994) Master of Science in Food and Nutrition, Ewha Womans University (1994–1996) Doctor of Philosophy, Virginia Polytechnic Institute and State University (2003–2006) Research Interests Her research centers on Food Sensory Science , integrating psychophysics, consumer psychology, and cross-cultural studies to understand how intrinsic and extrinsic factors shape perception and preference. She investigates color, taste, aroma, flavor, and texture attributes alongside hedonic and emotional responses. A major theme is the acceptance of ethnic foods —particularly Korean traditional foods—among international consumers, examining how information, culture, and repeated exposure influence liking. Another focus is sweetness perception , evaluating natural alternative sweeteners such as xylobiose and phyllodulcin for their sensory quality and application potential. Publication Trends Across the last 15 publications (2016–2020), her work reveals three clear trajectories: (1) cross-cultural consumer studies comparing Korean, Chinese, Japanese, Malaysian, and American acceptance of kimchi, bulgogi, and other ethnic foods; (2) sensory characterization of novel or functional ingredients such as xylobiose, phyllodulcin, and low-sodium formulations; and (3) methodological advancement via flash profiling, ideal profiling, and CATA techniques to capture consumer language and ideal sensory representations. Leadership & Service Academic Director, Korean Society of Food Culture Director, Korean Society for Dietary Life Education Chair, Sensory Evaluation Committee, Korean Society of Food Science and Technology Editorial Board Member, Korean Society of East Asian Dietary Life Editorial Board Member, English Journal of the Korean Society of Food Science and Technology Research Funding & Industry Collaboration Since 2013 she has secured continuous funding as Principal Investigator or Co-Investigator from the Ministry of Agriculture, Food and Rural Affairs , National Research Foundation of Korea , and industry partners including LG Electronics and Dongseong Food. Projects total more than ₩3.5 billion and cover optimization of natural sweeteners, halal Korean food development, premium ramen sensory profiling, and gluten-free flour evaluation. Laboratory & Team She directs the Sensory Science Laboratory in Building 222, Room 511, College of Human Ecology. The lab is equipped for descriptive sensory analysis, consumer testing booths, and data analysis suites. Graduate students and research staff collaborate on national and international projects, with regular participation in Pangborn and SenseAsia symposia.

• Computer Vision
• Optimization Methods
• Medical Imaging
• +۵ مورد دیگر

Thomas Pock is a Professor at the Institute of Visual Computing at TU Graz. His research focuses on computer vision, optimization methods, and mathematical models in computer vision, with significant contributions to medical imaging and inverse problems. He leads projects integrating deep learning with traditional variational methods for applications in MRI reconstruction, microscopy, and cardiac signal analysis. His work emphasizes efficient sampling techniques, uncertainty quantification, and algorithmic optimization in computational imaging. Education and academic background are not explicitly detailed in the provided texts, but his extensive publication record indicates expertise in interdisciplinary areas such as biomedical engineering, signal processing, and machine learning. Key research themes include variational networks, total variation methods, and generative adversarial networks (GANs) for image reconstruction and segmentation. His research spans collaborations in both academic and clinical settings, addressing challenges in 3D reconstruction, particle flow estimation, and cardiac electrophysiology modeling. Notable projects include Total Deep Variation and inverse Eikonal methods for medical data analysis. He actively contributes to open-source tools and frameworks for variational optimization and deep learning integration.

Xiangxiong Zhangمشاهده پروفایل

دانشیار

• Numerical Analysis
• Scientific Computing
• Applied Mathematics
• +۳ مورد دیگر

Xiangxiong Zhang is an Associate Professor in the Department of Mathematics at Purdue University, specializing in numerical analysis, scientific computing, and applied mathematics. His research focuses on numerical methods for partial differential equations, optimization algorithms, and computational fluid dynamics. He holds a PhD and has been actively involved in teaching graduate and undergraduate courses, including numerical PDEs, optimization, and linear algebra. His work emphasizes high-order numerical methods such as discontinuous Galerkin schemes, finite element methods, and spectral element methods, with applications in fluid dynamics, plasma physics, and compressible flow simulations. Notable contributions include positivity-preserving limiters, bound-preserving schemes, and Riemannian optimization techniques for matrix constraints. Zhang collaborates with researchers in computational mathematics and has published extensively in top journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . His research also addresses challenges in GPU-accelerated computing for large-scale scientific simulations.

Tsuhan Chenمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۳ مورد دیگر

Tsuhan Chen is a Distinguished Professor and Deputy President (Research and Technology) at the National University of Singapore (NUS), serving as Chief Scientist of AI Singapore and Co-Chair of the SIA-NUS Digital Aviation Corporate Laboratory. Previously, he served as Dean of the College of Engineering at Nanyang Technological University and held professorships at Cornell University and Carnegie Mellon University. Education: Ph.D. in Electrical Engineering, California Institute of Technology M.S. in Electrical Engineering, California Institute of Technology B.Sc. in Electrical Engineering, National Taiwan University A globally recognized expert in pattern recognition, computer vision, and machine learning, Prof. Chen's research centers on media artificial intelligence including computer vision, pattern recognition, multimedia coding/retrieval, and biometric authentication. His work has produced over 300 technical publications and nearly 30 US patents. His recent publications reveal dominant themes in computer vision and multimedia processing, with significant contributions to image captioning, semantic segmentation, emotion recognition, and medical imaging through advanced techniques like deep learning, tensor regularization, and multimodal fusion. Scientific Awards: Charles Wilts Prize (1993) NSF CAREER Award (2000-2003) Benjamin Richard Teare Teaching Award (2006) Eta Kappa Nu Award (2007) Michael Tien Teaching Award (2014) Robert M Janowiak Award (2017) Fellow of IEEE Fellow of Institute of Engineers, Singapore Prof. Chen has secured substantial research funding including the NSF CAREER Award and directed the Advanced Multimedia Processing Laboratory at Cornell. He currently leads national AI initiatives through AI Singapore and has championed interdisciplinary research programs aligned with Singapore's RIE2020 strategy.

Jakob Sauer Jørgensenمشاهده پروفایل

پژوهشگر

• Image Reconstruction
• Sparsity
• Regularization
• +۴ مورد دیگر

Jakob Sauer Jørgensen serves as a Senior Researcher in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), affiliated with the UltraSound and Biomechanics Scientific Computing group within the Center for Fast Ultrasound Imaging. His work spans computational mathematics, medical imaging, and uncertainty quantification. His research focuses on Image Reconstruction , Tomographic Reconstruction , and Uncertainty Quantification with emphasis on regularization techniques, sparsity, and Bayesian approaches. Key methodologies include total variation, inverse problems, and computational frameworks like CUQIpy for Python-based uncertainty quantification in inverse problems. His publication trends show strong emphasis on computational methods for medical imaging (2025), uncertainty quantification frameworks (2024-2025), and applications in nuclear fusion diagnostics (2024). Recent work integrates neural networks with traditional reconstruction methods and develops open-source tools for uncertainty modeling. He actively supervises PhD students across multiple projects including neural network-based X-ray tissue imaging, Mars meteorite analysis, and additive manufacturing modeling. Current projects include: Neural networks-based X-ray tissue imaging for robotic automation (2025-2028) Multi-modality reconstruction for Mars meteorite analysis (2024-2027) Modeling of volumetric additive manufacturing (2023-2026) Uncertainty quantification for tomographic reconstruction (2020-2024)

Gregory Ongieمشاهده پروفایل

استادیار

• Machine Learning
• Medical Imaging
• Computational Imaging
• +۵ مورد دیگر

Dr. Gregory Ongie is an Assistant Professor in the Department of Mathematical and Statistical Sciences at Marquette University. He holds a Ph.D. in Applied Mathematical and Computational Sciences from the University of Iowa (2016), an M.S. in Mathematics (2011), and a B.S. in Mathematics and Physics from Coe College (2008). His research focuses on machine learning, optimization, and compressed sensing applied to medical imaging, particularly in MRI and CT reconstruction. He has developed algorithms for inverse problems in imaging, including deep equilibrium models and Neumann networks. Research Interests: Mathematics of deep learning, applied algebraic geometry in data science, machine learning for medical image reconstruction, and optimization in computational imaging. His work bridges theoretical foundations of neural networks with practical applications in medical imaging, including model observer analysis and compressed sensing techniques. Grants and Awards: NSF CISE Research Initiation Initiative Grant (2022), Way Klinger Early Career Award (2023), D. C. Spriestersbach Dissertation Prize (2018), and Best Student Paper Award at ISBI 2015 for MRI super-resolution work. Teaching: Courses include Applied Linear Algebra, Differential Equations, and Mathematics of Medical Imaging. He has mentored multiple graduate and undergraduate students in imaging-related projects. Active in service roles, including Marquette's Faculty Hiring Committee and seminar coordination. Labs & Collaborations: Engaged in collaborative research with institutions like the University of Chicago and University of Michigan, focusing on computational imaging and machine learning. GitHub repositories include tools for matrix completion and MRI reconstruction.

Kostas Papafitsorosمشاهده پروفایل

مدرس

• Mathematical Imaging
• Inverse Problems
• Variational Regularisation Methods
• +۳ مورد دیگر

Kostas Papafitsoros is a Lecturer in Mathematical Data Science at the School of Mathematical Sciences, Queen Mary University of London, and Lead for Communication and Public Engagement. He previously held a postdoctoral position at the Weierstrass Institute in Berlin and was an Alexander von Humboldt Fellow at Humboldt University (2015–2017). His PhD (2014) and early postdoctoral work were at the University of Cambridge, where he contributed to the Cambridge Image Analysis group. His research focuses on mathematical imaging, inverse problems, and variational methods, with a strong emphasis on combining classical techniques with deep learning. He also leads projects in environmental conservation, particularly sea turtle photo-identification and citizen science initiatives like the Zakynthos Turtles platform, which engages tourists in conservation efforts. His work bridges academia and public engagement, leveraging AI to enhance wildlife monitoring. Recent research highlights include developing adaptive regularization methods for imaging and applying machine learning to quantitative MRI. He has published extensively in top journals and conferences, including SIAM and IEEE venues, and received grants such as the QMUL Impact Acceleration Fund and British Chelonia Group Conservation Grant. He supervises PhD students in mathematical data science and collaborates with NGOs like ARCHELON and MEDASSET. His interdisciplinary approach addresses global challenges in imaging science and biodiversity conservation.

Yuxin Chenمشاهده پروفایل

استاد

• Machine Learning
• High-Dimensional Estimation
• Nonconvex Optimization
• +۹ مورد دیگر

Yuxin Chen is a Professor at the Wharton School, University of Pennsylvania , appointed in both the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering . He previously served as an Assistant Professor at Princeton University (2017–2021) and a Postdoctoral Scholar at Stanford University (2015–2017). Education: Ph.D., Electrical Engineering, Stanford University, 2015 M.S., Statistics, Stanford University, 2013 M.S., Electrical and Computer Engineering, UT Austin, 2010 B.S., Microelectronics, Tsinghua University, 2008 His research spans high-dimensional estimation , machine learning theory , nonconvex optimization , and statistical signal processing , with applications in medical imaging and computational biology . Recent work focuses on diffusion models , large language models (LLMs) , and reinforcement learning (RL) . His 15 most recent publications emphasize diffusion models (generalization, sampling efficiency, scientific applications), reinforcement learning (sample complexity, convergence theory), and nonconvex optimization (implicit regularization, statistical guarantees). Scientific Awards: Alfred P. Sloan Research Fellowship, 2022 Princeton SEAS Junior Faculty Award, 2021 Princeton Graduate Mentoring Award, 2020 ICCM Best Paper Award (Gold Medal), 2020 ARO Young Investigator Program (YIP) Award, 2020 AFOSR YIP Award, 2019 SIAM Activity Group on Imaging Science Best Paper Prize IEEE Transactions on Power Electronics Prize Paper Award (first place) He mentors PhD students and leads interdisciplinary research projects funded by agencies like the Army Research Office and Air Force Office of Scientific Research . His team develops frameworks such as LOCO Edit for controllable diffusion model editing and MagNet for power magnetics modeling .

• Numerical Analysis
• Optimization
• Machine Learning
• +۵ مورد دیگر

Federica Porta is an Associate Professor at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia. Her academic career focuses on numerical analysis, optimization, and stochastic gradient methods, particularly in machine learning and image restoration. Her research interests include: Numerical Analysis and Statistics for Computer Engineering Numerical Optimization for Artificial Intelligence Stochastic Gradient Descent with Variance Control Deep Image Prior Frameworks Regularization Techniques for Biomedical Imaging Federica's recent publications (2021–2025) demonstrate expertise in hybrid gradient projection methods, adaptive learning rate selection, and deep learning applications for image segmentation and classification. She collaborates extensively with researchers like Giorgia Franchini, Valeria Ruggiero, and Luca Zanni, applying these methods to both convex and non-convex optimization problems. She teaches courses such as Numerical Analysis and Statistics for Computer Engineering, Numerical Analysis for Mathematics, and Numerical Optimization for Artificial Intelligence. Her teaching emphasizes MATLAB/Python implementation of numerical methods, convergence properties, and computational complexity analysis. Contact: federica.porta@unimore.it | Office: Mathematics Building, Via Campi 213/b

• Nonsmooth Optimization
• Inverse Problems
• Image Reconstruction
• +۵ مورد دیگر

Tuomo Valkonen is a Professor and Principal Investigator at the University of Helsinki , with significant contributions to mathematical optimization and inverse problems. He serves as the Managing Editor of the Journal of Nonsmooth Analysis and Optimization , advancing methodologies for nonsmooth and nonconvex optimization in imaging and PDE-constrained problems. His research spans Nonsmooth Optimization : Developing linearized block coordinate descent methods and forward-backward splitting in non-Hilbertian spaces. Inverse Problems : Applying these techniques to Electrical Impedance Tomography , Diffusion Tensor Imaging , and Magnetic Resonance Imaging . Optimal Transport : Innovating in unbalanced transport for point source localization and dynamic regularization via infimal convolution. Recent publications highlight his work on Online Optimization for real-time imaging. Multigrid Methods for total variation problems. Differential Estimates in bilevel PDE-constrained optimization. He has received the Magna Cum Laude Award at ISMRM 2013 for dynamic imaging work and authored a textbook on nonsmooth analysis (2024). His methods integrate stochastic and deterministic solvers, emphasizing efficiency in high-dimensional medical and geophysical imaging.