جست‌وجوی استاد و پژوهشگر

Al Barrمشاهده پروفایل

استاد

• Computer Graphics
• Predictive Modeling
• Scientific Visualization
• +۵ مورد دیگر

Alan H. Barr is a Professor of Computer Science at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science and the Computation & Neural Systems (CNS) department. He is a founding member of the Caltech Computer Graphics Group and a leader in developing mathematically rigorous methods for computer graphics and predictive modeling. His research focuses on enhancing computational modeling accuracy through approaches like interval analysis and constraint-based systems. Notable contributions include deformable models, quaternion interpolation, and cellular simulation frameworks. He has advised over 20 graduate students, many of whom became industry leaders at Pixar, Microsoft Research, and academic institutions like NYU and Brown University. Awards include the ACM SIGGRAPH Achievement Award (1988) and ACM Fellow (1995). Research Interests: Predictive modeling with error bounds Scientific visualization and MRI data analysis Biophysical systems simulation (e.g., cellular organelles) Self-assembling robotic structures for space colonization Mathematically robust computer graphics techniques Key Collaborations: Caltech Biological Imaging Center (Beckman Institute) JPL (Jet Propulsion Laboratory) New computational substrates research (quantum/DNA computing) Recent Work: Expanding into computational biology, medical imaging optimization, and high-confidence systems for managing complex computational interactions. Active in interdisciplinary projects across Caltech divisions.

Florian Knollمشاهده پروفایل

استاد

• Medical Imaging
• Machine Learning
• Magnetic Resonance Imaging (MRI)
• +۳ مورد دیگر

Prof. Dr. Florian Knoll is a full professor in Computational Imaging at the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He leads the Computational Imaging Lab, focusing on machine learning applications in medical imaging, particularly accelerating MRI through innovative reconstruction algorithms and translating them into clinical practice. His research emphasizes improving MRI speed, artifact robustness, and accessibility, alongside developing quantitative biomarkers for disease processes. Knoll's work is funded by NIH grants, including projects on machine learning for musculoskeletal imaging, MR fingerprinting, and deep learning frameworks for MRI reconstruction. He is a key figure in open science initiatives, co-creating the fastMRI dataset with Facebook AI, providing public access to over 1300 knee and 7000 brain MRI scans. He currently serves as deputy editor of Magnetic Resonance in Medicine and chairs the ISMRM Reproducible Research Study Group. His contributions extend to reproducible research, maintaining GitHub repositories with code for image reconstruction techniques (e.g., AGILE, gpuNUFFT) and educational materials. He teaches medical imaging fundamentals at FAU, integrating theoretical and practical insights for students and researchers. Grants: NIH R01EB024532, R21EB027241, P41EB017183, R01EB029957 Labs/Teams: Computational Imaging Lab, fastMRI initiative Software: GitHub repositories for MRI reconstruction (e.g., github.com/FlorianKnoll )

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Pulmonary Diseases
• +۵ مورد دیگر

Wilson Miller serves as Associate Professor of Radiology and Medical Imaging within the Department of Radiology and Medical Imaging at the University of Virginia School of Medicine. His research bridges advanced medical imaging physics with clinical pulmonary and neurological applications, maintaining active collaborations across radiology, pulmonology, and neurosurgery departments. Dr. Miller's research program centers on two transformative domains: hyperpolarized gas MRI for pulmonary disease characterization and focused ultrasound for neurological interventions. In pulmonary imaging, he pioneers hyperpolarized xenon-129 and helium-3 MRI techniques to map regional lung function in COPD, asthma, and lung transplantation, identifying novel imaging biomarkers for early disease detection and treatment monitoring. His neurological work develops focused ultrasound protocols for blood-brain barrier opening to enhance therapeutic delivery for cerebral cavernous malformations and brain tumors, with recent publications demonstrating lesion regression and improved drug penetration. Analysis of his 2023-2025 publications reveals accelerating integration of molecular techniques with imaging, particularly transcriptomic analysis of rejection in lung transplants and immune response mapping in glioblastoma. His work increasingly emphasizes multimodal assessment combining hyperpolarized gas MRI with histological and molecular validation, while maintaining a secondary research thread in spin-polarized fusion physics for energy applications. Scientific Awards: No specific awards documented in source materials Dr. Miller actively mentors graduate students and postdoctoral researchers within the Medical Imaging PhD program, though individual advisee names were not provided in source texts. His research program likely operates through NIH-funded R01 grants from the National Heart, Lung, and Blood Institute (NHLBI) and National Institute of Neurological Disorders and Stroke (NINDS), supported by collaborative infrastructure from the University of Virginia's Radiology Research Division. His laboratory operates advanced 3T MRI systems with hyperpolarized gas delivery capabilities and preclinical focused ultrasound platforms, collaborating with the UVA Brain Immunology and Glia Center and Lung Repair and Regeneration Consortium. Current projects include developing AI-enhanced analysis of hyperpolarized gas MRI for COPD endotyping and optimizing microbubble parameters for focused ultrasound-mediated drug delivery to brain lesions.

• MR Imaging
• MR Elastography
• Low-field MRI
• +۵ مورد دیگر

Dr. Najat Salameh is a Senior Lecturer at the University of Aberdeen's School of Medicine, Medical Sciences and Nutrition, where she leads the Medical Imaging Technologies theme at the Institute of Medical Sciences. She co-founded the Center for Adaptable MRI Technology (AMT Center) in 2017 and relocated it to Aberdeen in April 2023, establishing what will become the largest low-field MR technology platform with approximately 900 m 2 of lab and office space at the Foresterhill campus. Her educational background includes: PhD in Biomedical Sciences from Université Catholique de Louvain (2009) DEA (Master) in Health Sciences from Université Catholique de Louvain (2008) Diplôme ès Sciences Physiques from University of Geneva (2005) Dr. Salameh has developed over 20+ years of expertise in MR imaging, positioning herself at the interface between Physics and Medicine. Her research spans from fundamental physics to microsurgery in rodents, with specialization in MR elastography for liver and brain applications, thermometry, metabolic imaging, and low magnetic field MRI systems. She designs, implements, and validates imaging protocols across magnetic fields ranging from 0.0065 T to 9.4 T, with a strong focus on making MRI technology more accessible and adaptable for clinical use. Her work bridges the gap between advanced imaging technology development and practical clinical applications. Her recent publication portfolio reveals a clear trajectory toward practical implementation of low-field MRI systems, with emphasis on hardware innovation, deep learning applications for image reconstruction, and clinical translation of MR elastography techniques. The research demonstrates significant progress in making quantitative tissue characterization more accessible through cost-effective imaging solutions. Her scientific recognition includes: V-LF-Spiro3D grant from Research Councils UK (2023-2027) for low-field 3D magnetic resonance spirometry Interventional and susceptibility artifact-free MR Imaging award from Swiss National Science Foundation (2017-2021) In vivo detection and imaging of free radicals using the Overhauser effect grant from Swiss National Science Foundation (2013-2014) MR elastography for chronic liver diseases award from Fonds pour la formation à la recherche dans l'industrie et l'agriculture (2005-2009) As co-founder and leader of the AMT Center, Dr. Salameh collaborates with Dr. Sarracanie, Dr. Broche, and Dr. Ross on developing the next generation of low-field MRI technology. Her center is currently establishing a major research infrastructure with three fixed low-field scanners and two Field-Cycling whole-body systems in a newly refurbished facility, positioning Aberdeen as a global hub for adaptable MRI technology development. The Center for Adaptable MRI Technology represents a significant investment in accessible imaging technology, with the relocated platform forming what will be the largest low-field MR technology facility globally. This initiative supports research across multiple clinical applications while addressing the need for more affordable and widely deployable imaging solutions.

Markus Zimmermannمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Quantitative MRI
• Neuroscience
• +۳ مورد دیگر

Dr. Markus Zimmermann is a researcher at the Institute of Neuroscience and Medicine (INM-4: Physics of Medical Imaging) at the Research Center Jülich. His work focuses on advancing quantitative MRI techniques, particularly in water content mapping, multiparametric imaging, and ultrahigh-field MRI applications. He contributes to developing methods for eddy current characterization, multi-exponential relaxometry, and rapid whole-brain protocols. His research addresses neurological and medical imaging challenges, including cerebral pathologies and neurobiological implications. Key areas of expertise include MRI parameter estimation, medical imaging algorithms, and the integration of advanced imaging techniques for clinical and neuroscience applications. His projects often involve collaborations to validate methodologies using in vivo/ex vivo experiments and super-resolution reconstruction. Dr. Zimmermann’s work aims to enhance diagnostic precision and understanding of brain physiology through innovative MRI technologies.

Uwe Meyer-Baeseمشاهده پروفایل

دانشیار

• Digital Signal Processing
• FPGA Design
• VLSI Systems
• +۴ مورد دیگر

Uwe Meyer-Baese is an Associate Professor in the Electrical and Computer Engineering Department at the FAMU-FSU College of Engineering. He holds a Ph.D. (Dr.-Ing. habil) from Darmstadt University of Technology, Germany. His research focuses on Digital Signal Processing with FPGAs, VLSI design, and medical imaging applications. He has authored over 100 publications, 5 books, and holds 3 patents. He has been recognized with awards such as the Humboldt Fellowship (2009) and the FAMU-FSU Teaching Award (2007). Education History: Dr.-Ing. habil (Venia Legendi), Darmstadt University of Technology, Germany, 2003 Ph.D. (Dr. Ing.), Darmstadt University of Technology, Germany, 1995 M.S., Darmstadt University of Technology, Germany, 1989 Research Interests: FPGA-based embedded systems and real-time DSP Low-power VLSI architectures Medical image processing (e.g., breast MRI, brain tumor analysis) Hardware security and intellectual property protection Graph theory applications in biological networks Recent work includes advancements in FPGA implementations for microprocessor systems, brain network controllability studies, and AI-driven medical diagnostics. His lab focuses on bridging hardware design with biomedical applications, emphasizing practical implementations through FPGA platforms. Awards: Max-Kade Award in Neuroengineering (1997) ECE Department Research Award (2005) Humboldt Fellowship (2009) FAMU-FSU Teaching Award (2007) He has advised over 60 master’s theses and contributed to major grants in FPGA-based medical systems. His book Digital Signal Processing with Field Programmable Gate Arrays is a widely used textbook in the field.

Mingyan Liمشاهده پروفایل

پژوهشگر ارشد

• Imaging
• Sensing
• Biomedical Engineering
• +۳ مورد دیگر

Dr. Mingyan Li is an Adjunct Research Fellow at The University of Queensland's School of Electrical Engineering and Computer Science. Their research focuses on advanced imaging and sensing technologies with applications in biomedical engineering, particularly in MRI system development, RF coil design, and medical signal processing. They hold a PhD from The University of Queensland (2015). Research interests include high-field MRI systems, rotating RF coil technologies, MRI-Linac integration, and electrical properties tomography (EPT). Key contributions include innovations in MRI-Linac distortion correction, RF shielding for SAR reduction, and deep learning approaches for cardiac arrhythmia classification. Publications span MRI hardware optimization, image reconstruction algorithms, and biomedical signal analysis. Collaborations include work on metamaterial-inspired RF shielding and multi-modal antenna systems for body MRI.

• Neuroscience
• Computational Modeling
• Meditation
• +۵ مورد دیگر

Giuseppe PAGNONI is an Associate Professor at the Department of Biomedical, Metabolic and Neurosciences of Università di Modena e Reggio Emilia . His research spans neuroscience , computational modeling , meditation , and brain network analysis . He teaches courses such as Neural Systems Modeling for Bioengineering students and Human Physiology and Pathophysiology in Medicine programs, with a focus on predictive coding , default mode network , and emotional valence processing . Pagnoni has contributed to ENIGMA-Meditation , a global initiative exploring meditation's neuroscientific basis, and developed sparse Bayesian models for analyzing brain network individual differences. His work on Alzheimer's disease investigates anosognosia mechanisms through resting-state fMRI, while other studies examine ghrelin's role in obesity-related reward processing and oxytocin's effects on social anxiety . He employs dynamic causal modeling and predictive processing frameworks to study cognitive effort, mind wandering, and neuromorphic learning. His methodological expertise includes functional MRI , independent component analysis , and computational simulations .

Rebecca Feldmanمشاهده پروفایل

استادیار

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Pulse Sequence Design
• +۴ مورد دیگر

Rebecca Feldman is an Assistant Professor in Medical Physics and Physics at the Irving K. Barber Faculty of Science, University of British Columbia Okanagan. Her research integrates MR physics, engineering, and medical research to advance MRI pulse sequences and hardware for clinical translation, particularly in neurological disorders. University: University of British Columbia Okanagan Academic Rank: Assistant Professor PhD: University of Western Ontario Research Interests: Dr. Feldman specializes in technical innovation in MRI (accelerated imaging, spectroscopic imaging, non-proton imaging) and translational research applying MRI to neurological disease detection, characterization, and treatment. Her work leverages ultra-high-field (7T) MRI for enhanced resolution of brain structures like hippocampal subfields and perivascular spaces. Publications: Recent work includes advancements in self-supervised medical imaging backbones (MedMAE), segmentation of venous structures in epilepsy, automated MRI pulse design via neural networks, and clinical applications of 7T MRI in neurosurgical planning and psychiatric disorders like major depressive disorder. Teaching: Currently teaches courses in physics, including physics of waves.

Katie Boumanمشاهده پروفایل

دانشیار

• Computational Imaging
• Astrophysics
• Machine Learning
• +۳ مورد دیگر

Katie Bouman is an Associate Professor of Computing and Mathematical Sciences (CMS), with courtesy appointments in Electrical Engineering and Astronomy at the California Institute of Technology (Caltech). She is also a Rosenberg Scholar and an Investigator at the Heritage Medical Research Institute. Her research focuses on computational imaging, integrating algorithm and sensor design to observe phenomena traditionally inaccessible through conventional methods. She leads the Computational Cameras Group, which develops systems for scientific discovery and technological innovation in fields such as astrophysics, medical imaging, and computer vision. Bouman holds a B.S.E. in Electrical Engineering from the University of Michigan (2011), an S.M. and Ph.D. in Electrical Engineering and Computer Science from MIT (2013 and 2017). Before joining Caltech, she was a postdoctoral fellow with the Event Horizon Telescope (EHT), contributing to the groundbreaking first image of a black hole in 2019. Her research interests span signal processing, machine learning, and physics-driven imaging solutions. Notable projects include 3D black hole gas reconstruction, MRI acceleration, and neural field-based tomography. She emphasizes interdisciplinary collaboration, merging computational methods with domain-specific challenges in astronomy and biomedical engineering. Awards: PECASE Award, Sloan Fellowship, NSF CAREER Award, Royal Photographic Society Progress Medal, Breakthrough Prize (co-recipient). Grants: Okawa Research Grant, HMRI Investigator funding. Bouman advises students in CMS and collaborates with industry through the Caltech Center for Sensing to Intelligence (S2I). Her lab fosters diversity and inclusion, prioritizing equitable participation in STEM fields.

Douglas Nollمشاهده پروفایل

استاد

• Biomedical Engineering
• Radiology
• Magnetic Resonance Imaging
• +۹ مورد دیگر

Douglas Noll is the Ann and Robert H. Lurie Professor in the Department of Biomedical Engineering at the University of Michigan , with additional appointments as Professor of Radiology and affiliate of multiple research institutes including the Michigan Alzheimer’s Disease Research Center, Michigan Neuroscience Institute, and Michigan Institute for Imaging Technology and Translation (MIITT). He co-directs the Functional MRI Laboratory and leads the NeuroImaging Core at the Michigan Alzheimer’s Disease Research Center. Prof. Noll’s research focuses on MRI technology development , particularly for functional MRI (fMRI) . His group specializes in high-speed MRI acquisition , signal processing , and image reconstruction to map brain function. Key areas include artifact elimination , physiological modeling , and quantitative imaging for neurological and psychiatric disorders. Recent collaborations apply MRI to histotripsy therapy monitoring. The 15 most recent articles reflect his work in dynamic MRI reconstruction , artifact correction , and cross-vendor protocol standardization . Topics span B0 shimming , histotripsy targeting , and non-Cartesian sampling , emphasizing spatiotemporal modeling and machine learning integration for accelerated imaging. Scientific Awards : Ann and Robert H. Lurie Professor. Research Collaborations : Functional MRI Laboratory, Michigan Alzheimer’s Disease Research Center, Michigan Institute for Imaging Technology and Translation (MIITT).

• Robotics
• Bio-Inspired Robotics
• Swarm Intelligence
• +۷ مورد دیگر

Jordan Boyle is an Assistant Professor in the Department of Sustainable Design Engineering at the Faculty of Industrial Design Engineering, Delft University of Technology (TU Delft). He is affiliated with the Materializing Futures Section, where he conducts research in robotics, bio-inspired systems, swarm intelligence, and human-robot interaction. Academic Background: PhD in Computer Science, University of Leeds – focused on neuro-mechanical control of locomotion in C. elegans . MSc and BSc (Hons) in Electrical Engineering, University of Cape Town. Prior academic experience at the University of Leeds as Research Fellow, Lecturer, and Associate Professor over 12 years. Research Interests: Dr. Boyle’s research centers on robotics with a strong emphasis on bio-inspired design. His work spans swarm intelligence , multi-robot systems , human-robot interaction , and robotic fabrication . He also specializes in designing experimental apparatus for pre-clinical and engineering applications. His interdisciplinary approach integrates mechanical design, control systems, and AI for real-world deployment in construction, medicine, and infrastructure. Publication Trends: His recent publications (2022–2024) demonstrate a clear trajectory toward bio-inspired autonomous systems applied in construction and medical imaging. Key themes include swarm robotics for construction, MRI trajectory correction with robotic components, and locomotion mechanisms inspired by biological systems. These works reflect strong interdisciplinary collaboration, particularly with biomedical and mechanical engineering teams. Teaching: Product Engineering (2024, 2025) Advanced Product Engineering (2024, 2025) Scientific Affiliations and Activities: Visiting Researcher, School of Mechanical Engineering, University of Leeds (2022–2026) Advising and Grants: While no formal students or specific grants are listed in the provided text, Dr. Boyle has supervised research projects and collaborated across disciplines, particularly in medical and civil engineering applications. His role in designing experimental apparatus indicates active involvement in grant-funded interdisciplinary research. Labs and Research Groups: He is part of the Materializing Futures Section within Sustainable Design Engineering, which likely operates in conjunction with TU Delft’s broader design and robotics labs, though specific lab names are not mentioned.

• Medical Engineering
• Biomedical Engineering
• Control Systems
• +۷ مورد دیگر

Dr. Mihailo Ristic is a Senior Lecturer in the Department of Mechanical Engineering at Imperial College London, part of the Faculty of Engineering. He holds a First Class Honours Degree in Mechanical Engineering from University College London (1981), an M.Sc. in Control Systems (Imperial College London, 1982), and a Ph.D. in Robotics (Imperial College London, 1986). His research spans control systems, CAD/CAM, robotics, and medical engineering, with recent focus on Magnetic Resonance Imaging (MRI) systems and mechatronic devices for clinical applications. Dr. Ristic is a Chartered Engineer and Fellow of the Institution of Mechanical Engineers. He co-founded Turbo Power Systems, specializing in high-speed electric machines and power electronics. His work integrates robotics, medical imaging, and advanced manufacturing, addressing challenges in distributed power generation and biomedical device design. Key projects include novel MRI magnet configurations, intraoperative MRI tools, and robotic systems for MRI-guided interventions. Education: B.Sc. (First Class Honours) Mechanical Engineering, University College London (1981) M.Sc. Control Systems, Imperial College London (1982) Ph.D. Robotics, Imperial College London (1986) Affiliations: Robotics Forum CAD/CAM Research Group Mecheatronics in Medicine His research interests include medical engineering innovations, such as MRI system design, collagen fiber analysis, and robotic-assisted surgery. He has contributed to over 50 publications in journals and conferences, focusing on imaging technologies, robotics, and mechatronics. His work bridges academic research and industrial applications, particularly in energy systems and biomedical devices. Dr. Ristic’s awards include the Fellowship of the Institution of Mechanical Engineers. His grants and projects involve collaborations with industry partners to advance MRI hardware, teleoperated surgical systems, and energy-efficient power solutions.

• Magnetic Resonance Imaging (MRI)
• Neuroimaging Biophysics
• Brain Metabolism Analysis
• +۶ مورد دیگر

Harald E. Möller is a Professor and Head of the Nuclear Magnetic Resonance Research and Development Unit at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig. With a career spanning over four decades, he has held academic positions including Honorary Professor at the University of Leipzig and leadership roles in institutions like Duke University Medical Center and the University of Münster. His research focuses on advancing MRI methodologies, biophysical imaging principles, and their applications in neurology and neuroscience. Education: 1979-1985: Chemistry & Physics studies at Universities of Dortmund and Münster 1985: M.Sc. (Diploma) in Chemistry 1988: PhD in Physical Chemistry (summa cum laude) 2000: Habilitation in Physical Chemistry 2002: Habilitation in Biophysical Chemistry Research Interests: Development of novel MRI methods Quantitative tissue characterization Myelin sheath imaging Cerebral blood flow dynamics High-field MRI hardware

Daniel Sodicksonمشاهده پروفایل

استاد

• Biomedical Imaging
• Medical Imaging Technology
• Artificial Intelligence in Healthcare
• +۴ مورد دیگر

Dr. Daniel Sodickson is a Professor of Radiology, Neuroscience and Physiology, and Biomedical Engineering at New York University. He serves as Vice Chair for Research in Radiology and directs the Bernard and Irene Schwartz Center for Biomedical Imaging and the Center for Advanced Imaging Innovation and Research. His work focuses on advancing biomedical imaging techniques to improve healthcare, particularly in MRI, PET, and CT technologies. He leads multidisciplinary teams developing methods for rapid, continuous imaging leveraging parallel imaging, compressed sensing, and AI. Dr. Sodickson’s research interests include AI-driven medical imaging, low-field MRI applications, and novel imaging sensors. He co-directs Tech4Health, fostering translational healthcare technologies. Notably, he pioneered the fastMRI initiative, an open dataset and benchmarks for accelerated MRI using machine learning. Key achievements include developing the fastMRI public dataset for knee and prostate imaging, and contributions to global AI events like the World’s Largest AI for Business Summit. His work addresses challenges in imaging speed, cost, and accessibility, with a focus on clinical translation. Dr. Sodickson has received prestigious recognition, including being named a Fellow of the National Academy of Inventors. His lab collaborations and grants emphasize interdisciplinary innovation, aiming to bridge imaging technology with clinical practice.