جستجوی استاد راهنمای دانشگاهی

James C. Geeمشاهده پروفایل

استاد

• Biomedical Image Analysis
• Medical Imaging Computing
• Image Segmentation
• +۵ مورد دیگر

James C. Gee is a Professor of Radiologic Science in Radiology at the University of Pennsylvania's Perelman School of Medicine. He serves as Director of the Penn Image Computing and Science Laboratory and Co-Director of the Translational Biomedical Imaging Center , with affiliations in Bioengineering and Applied Mathematics graduate groups. His research focuses on biomedical image analysis, specialization in segmentation, registration, and morphometry applied to neurodegenerative diseases and multi-organ systems. Education : B.S. in Computer Science/Electrical Engineering (University of Washington, 1987), Ph.D. in Computer and Information Science (University of Pennsylvania, 1996) Research : Quantitative medical imaging methods, brain connectomics, neurodegeneration mapping, and translational imaging technologies Publications : 15+ recent works on AI-driven image analysis for Alzheimer's disease, cardiac amyloidosis, and radiomics applications Leadership : Directs MSE-DS Online Degree Program, co-chairs Radiology DCOAP Committee, and founded RISE (Radiology Initiative to Support Inclusive Excellence) His laboratory develops advanced computational tools like ITK-SNAP for biomedical imaging, with applications in both in vivo clinical imaging and ex vivo histology . The work spans cross-disciplinary collaborations in computer science, neuroscience, and clinical medicine.

Justin P. Haldarمشاهده پروفایل

استاد

• Computational Imaging
• Inverse Problems
• Magnetic Resonance Imaging (MRI)
• +۳ مورد دیگر

Justin P. Haldar is a Professor in the Ming Hsieh Department of Electrical and Computer Engineering at the University of Southern California (USC), with a joint appointment in the Department of Biomedical Engineering. He co-directs the Biomedical Imaging Group and serves as Director of the Signal and Image Processing Institute. His affiliations include the Dornsife Cognitive Neuroscience Imaging Center, the Brain and Creativity Institute, and the Dynamic Imaging Science Center. Education : B.S. and M.S. in Electrical Engineering (2004, 2005), Ph.D. in Electrical and Computer Engineering (2011) from the University of Illinois at Urbana-Champaign. His research focuses on computational imaging, inverse problems, and magnetic resonance imaging (MRI), with an emphasis on constrained image reconstruction, parameter estimation, and novel data acquisition strategies. His work combines physical modeling, high-dimensional signal structures, and fast computational algorithms to address MRI's limitations in speed, noise, and cost. Recent publications analyze challenges like the 'hidden noise' problem in MR reconstruction (2025) and innovations in dynamic imaging. His research has enabled faster MRI exams and next-generation imaging techniques by exploiting dimensionality's 'blessings' while mitigating its 'curses.' Scientific awards : NSF CAREER Award (2014) IEEE ISBI Best Paper Award (2010) IEEE EMBC First-Place Student Paper Award Haldar's leadership roles include Chair of the IEEE Signal Processing Society's Technical Committee on Computational Imaging and editorial positions at IEEE Transactions on Computational Imaging and Magnetic Resonance in Medicine . He actively mentors students and develops novel MRI approaches at USC's Michelson Center for Convergent Bioscience.

• Medical Imaging
• Nuclear Engineering
• Biomedical Engineering
• +۱۲ مورد دیگر

Spencer L. Bowen, Ph.D., is an Assistant Professor in the Department of Radiology at UT Southwestern Medical Center, where he is a member of the Radiology Research section and serves as a PET research scientist. His work is centered on advancing nuclear imaging technologies for clinical and research applications in oncology, neurology, and cardiology. Education: Bachelor's in Biomedical Engineering – University of Washington, Seattle Ph.D. in Biomedical Engineering – University of California, Davis Research Fellow – Massachusetts General Hospital, Charlestown, MA Dr. Bowen's research focuses on the development of advanced PET imaging systems, including dedicated breast PET/CT scanners and hybrid PET-MR technologies. He investigates image acquisition techniques, reconstruction algorithms, attenuation and scatter correction methods, and partial volume correction to improve quantitative accuracy. His work spans hardware design, software development (e.g., the Masamune processing tool), and clinical translation. His recent publications highlight innovations in cardiac and neurological PET quantification, breast imaging, and hybrid PET/MR systems. Themes include attenuation correction in PET/MR, dynamic PET modeling, and the impact of image processing on clinical interpretation. Scientific Recognition: Research featured on the cover of the Journal of Nuclear Medicine Work covered by press outlets Dr. Bowen actively contributes to the scientific community as a reviewer for leading journals including Journal of Nuclear Medicine , Medical Physics , Physics in Medicine and Biology , and IEEE Transactions on Nuclear Science and Transactions on Medical Imaging . His lab, the Bowen Lab, is engaged in ongoing research and is currently recruiting PhD graduate students, indicating active grant support and research momentum. He leads a research team focused on developing tomographic tools for precision medicine. The Bowen Lab is dedicated to creating and refining nuclear imaging technologies to enhance both clinical care and scientific discovery, with a strong emphasis on quantitative, high-resolution imaging across multiple disease domains.

• high resolution MRI
• visual perception
• functional imaging
• +۶ مورد دیگر

Reza Farivar-Mohseni is an Associate Professor at McGill University , affiliated with the Faculty of Medicine and Health Sciences and the Department of Ophthalmology and Visual Sciences . He serves as a Scientist at the RI-MUHC (Montreal General Hospital site), contributing to the Brain Repair and Integrative Neuroscience (BRaIN) Program and the Centre for Translational Biology . Research Interests: Dr. Farivar-Mohseni’s work focuses on cortico-cortical communication, information processing in the brain, and disruptions in neurological disorders like traumatic brain injury. He specializes in advancing non-invasive brain imaging (MRI) for both fundamental and clinical applications, particularly improving concussion detection and diagnosis. Publications: His research spans high-resolution MRI, visual perception, and functional imaging. Key themes include depth-cue invariance in object recognition, gamma-band neural representations, and cortical deficits in amblyopia. Recent studies (2025–2022) address computational neuroscience, vision screening tools, and neural imaging techniques. Labs & Collaborations: He collaborates with the MGH-MRI Research Platform and works within the Centre for Translational Biology , focusing on translating imaging advancements into clinical tools.

• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• Biomedical Imaging Physics
• +۶ مورد دیگر

Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology

• Magnetic resonance spectroscopic imaging
• Tumor micro-environment imaging
• Multinuclear MRI (¹H/²³Na)
• +۴ مورد دیگر

Sandeep Kumar Mishra, PhD, is an Associate Research Scientist in the Department of Radiology & Biomedical Imaging at Yale School of Medicine, where he holds a primary appointment in the Magnetic Resonance Research Center within the Division of Bioimaging Sciences. He completed his doctoral training at Pondicherry University (2017) and finished post-doctoral research at Yale in 2024 before transitioning to his current research-intensive faculty role. Education: PhD, Pondicherry University – 2017 Post-doctoral Associate, Yale University – 2024 Research Focus: Mishra’s work integrates multinuclear magnetic resonance spectroscopy, responsive paramagnetic probes, and nano-constructs to quantify the tumor microenvironment. Major themes include in-vivo mapping of interstitial pH and sodium gradients in gliomas, development of Fe(II)/Co(II)/Ni(II)-DOTA tetraglycinate complexes for simultaneous pH–temperature sensing, and engineering dual-modal nano-agents that couple MR angiography with therapeutic delivery (chemo-photothermal, cryo-ablation, MMP inhibition). Publication Trends: Across 28 peer-reviewed articles (2016-2025) he demonstrates a sustained trajectory in cancer imaging, moving from theranostic nanoparticles toward sophisticated spectroscopic imaging of tumor acid–base and ionic homeostasis, with increasing translational orientation involving rodent glioma and hepatocellular carcinoma models. Collaborations & Affiliations: He is embedded in Yale’s inter-disciplinary MR research ecosystem, collaborating recurrently with faculty in Radiology, Biomedical Engineering, and the Magnetic Resonance Research Center (D. Coman, F. Hyder, P. Herman, J. Verhagen, J. Santana, A. Shewarega). Contact: sandeepkmishra11@gmail.com | Magnetic Resonance Research Center, 300 Cedar Street, New Haven, CT 06519, USA

Roger Tamمشاهده پروفایل

دانشیار

• Medical Imaging
• Machine Learning
• Quantitative Image Analysis
• +۳ مورد دیگر

Roger Tam is an Associate Professor in the School of Biomedical Engineering (SBME) at the University of British Columbia (UBC), with a joint appointment in the Department of Radiology. He is also the Associate Director of Graduate Studies. His research focuses on machine learning and computer vision applied to medical imaging, particularly in personalized medicine and quantitative image analysis. Tam earned his PhD in computer science from UBC in 2004, specializing in computational geometry and visualization. Education: PhD in Computer Science, UBC (2004) MSc in Computer Science BSc (Honors) Research Interests: Medical imaging biomarkers Machine learning applications in healthcare Quantitative image analysis Personalized medicine His work bridges computer science and clinical medicine, emphasizing translational approaches to improve diagnostic accuracy and patient outcomes. Recent Research Trends: Focus on myelin content analysis in neurological disorders (e.g., multiple sclerosis) Development of efficient machine learning models for medical image classification Impact of physical activity on white matter health Labs & Programs: Directs the Engineers in Scrubs program, which integrates engineering principles into biomedical education. Active in collaborative research initiatives like the Centre for Brain Health and the Canadian Prospective Cohort Study (CanProCo).

• Biomedical imaging
• Functional MRI
• MRI Systems biology
• +۳ مورد دیگر

Jozien Goense is an Associate Professor at the University of Illinois at Urbana-Champaign (UIUC), holding joint appointments in the Department of Psychology and Department of Bioengineering. She is also affiliated with the Beckman Institute for Advanced Science and Technology and the Neuroscience Program. Her primary research focuses on biomedical imaging, particularly functional MRI (fMRI) and its applications in understanding neurovascular coupling and cortical layer-specific activity. She is recognized for contributions to high-resolution fMRI methodology and laminar imaging techniques. Her work integrates advanced MRI techniques with neurophysiological studies, often using non-human primate models to validate findings in human studies. She has pioneered layer-specific fMRI approaches to map activity across cortical layers, particularly in the motor and visual cortices. Her expertise includes ultra-high field MRI (7T+), BOLD signal modeling, and software development for neuroimaging analysis (e.g., LayNII). Key research themes include the physiological basis of BOLD responses, neurovascular coupling mechanisms, and the application of laminar fMRI to study brain function in health and disease. She has collaborated extensively with neuroscientists, engineers, and clinicians to advance imaging technologies and their translational potential. Publications highlight her contributions to understanding dopamine and acetylcholine effects on neurovascular responses, resting-state gamma-band abnormalities in schizophrenia, and the development of high-resolution imaging protocols. Her work bridges basic science and clinical applications, including studies on vascular contributions to dementia and neuropharmacology. She has received the Beckman Seed Grant as part of a Bioengineering Faculty team, supporting innovative research initiatives. Her lab employs cutting-edge imaging tools and multidisciplinary approaches to unravel brain function at cellular and systems levels.

• Medical Imaging
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Dr. Neal Bangerter is a Visiting Professor in the Department of Bioengineering at Imperial College London's Faculty of Engineering. He specializes in medical imaging (MRI), artificial intelligence, machine learning, and signal processing. Dr. Bangerter holds adjunct appointments at INSEAD, the University of Utah, and Brigham Young University. His research focuses on ultra-high field MRI, AI applications in healthcare, and data-driven bioscience technologies. He leads the London Collaborative Ultra-High Field Scanner (LOCUS) project and advises companies on AI and innovation strategies. Education: B.S. in Physics (UC Berkeley), M.S. and Ph.D. in Electrical Engineering (Stanford University). Career highlights include roles at McKinsey & Company, Microsoft, and Reactrix, as well as founding BYU's Medical Imaging Research Center. He has pioneered cross-faculty initiatives like the Crocker Innovation Fellowship Program. Research interests include novel MRI pulse sequences, AI in medical imaging, and large-scale health data analysis. His work spans collaborations with Stanford, Oxford, Cambridge, and Siemens Healthcare. He teaches executive education at INSEAD, focusing on bridging technical concepts with business strategies. Key awards include the David Evans Chair at Brigham Young University. His contributions to the UK Biobank Neuroimaging study and development of MRI techniques like RAFO-4 highlight his impact on advancing imaging technologies and AI applications in healthcare.

Paul A. Yushkevichمشاهده پروفایل

استاد

• Biomedical Image Analysis
• Alzheimer's Disease Imaging
• Statistical Shape Modeling
• +۵ مورد دیگر

Paul A. Yushkevich is a Professor of Radiology at the Perelman School of Medicine, University of Pennsylvania , with affiliations in the Bioengineering Graduate Group . He leads the Penn Image Computing and Science Laboratory (PICSL) , focusing on advanced biomedical image analysis techniques. Developed first-of-its-kind computational atlas of the hippocampal formation Led NIH R01-funded research on MRI-derived biomarkers for Alzheimer's disease Created open-source software tools: ITK-SNAP and Convert3D Expert in statistical shape modeling and histology-MRI co-registration Research Focus: Specializes in hippocampal segmentation using high-resolution MRI, with applications in Alzheimer's disease research and cardiac imaging . His work combines differential equations and machine learning for accurate image analysis. Scientific Achievements: First-place MICCAI segmentation challenges (2012, 2013) Over 169 PubMed publications in neuroimaging and computational anatomy Developed DTI-TK toolkit for diffusion MRI analysis Collaborations: Works with Alzheimer's Disease Neuroimaging Initiative (ADNI) and multiple international institutions. Supervises graduate students in biomedical image analysis.

• Positron Emission Tomography (PET) Imaging
• Neurochemistry
• Computational Neuroscience
• +۴ مورد دیگر

Professor Evan Morris is a faculty member at Yale School of Medicine in the Radiology and Biomedical Imaging department, with secondary appointments in Biomedical Engineering and Psychiatry. His work combines advanced kinetic modeling and dynamic PET imaging to visualize neurotransmitter dynamics in the brain. PhD in Chemical Engineering from Case Western Reserve University (1991) Postdoctoral fellowship at Massachusetts General Hospital (1995) Morris specializes in creating "dopamine movies" through PET imaging, revealing transient neurochemical fluctuations related to addiction, Parkinson's disease, and stress responses. His group develops novel parametric imaging methods for faster drug discovery and disease biomarker identification. Recent publications focus on: Nonsteady-state PET modeling Stress-induced opioid receptor connectivity Exercise effects on Parkinson's neuropathology EC50 image quantification Scientific recognitions include: Fulbright Senior Scholar (2015) Yale Graduate Mentor of the Year (2013) He serves as Principal Investigator for studies on nicotine addiction and collaborates across Yale's Morris Lab , PET Core , and Neural Disorders programs, advancing applications in Medical Imaging , Neurochemistry , and Biomedical Engineering .

Markus Axerمشاهده پروفایل

استاد

• Connectome
• Neuroimaging
• Polarization Microscopy
• +۳ مورد دیگر

Prof. Markus Axer is a Professor and Deputy Head of the Structural and Functional Organisation of the Brain (INM-1) at the Institute of Neuroscience and Medicine (INM) within Forschungszentrum Jülich. His research focuses on connectomics, neuroimaging technologies (e.g., 3D-Polarized Light Imaging), and high-performance computing applications in brain architecture analysis. He leads the 'Fiber Architecture' working group, advancing microscopy techniques like scattered light imaging and MRI-histology correlation for studying brain microstructure. His work bridges experimental neuroscience with computational methods, aiming to decode brain organization at meso- and macroscales. Key achievements include developing the HippoMaps atlas of the human hippocampus and improving fiber orientation mapping in brain tissue. Awards include Fellowship in the Royal Netherlands Academy of Arts and Sciences (2024). Research emphasizes cross-modal data integration, with applications in Alzheimer’s disease biomarker validation and primate brain evolution studies. He collaborates with academic institutions like the University of Wuppertal and contributes to international initiatives like the BigBrain Analytics Learning Laboratory.

Mohamed Farhatمشاهده پروفایل

پژوهشگر

• Cavitation & Multiphase flows
• Flow Induced Noise & Vibration
• Fluid-Structure Interaction
• +۵ مورد دیگر

Mohamed Farhat is a Senior Scientist at EPFL's School of Engineering, Department of Mechanical Engineering, where he leads the Research Group on Cavitation and Interface Phenomena. He serves as PhD Director, Lecturer, and Member of EPFL Doctoral Committee (Mechanics), while also representing EPFL at CLUSER association and coordinating activities at the Société Hydrotechnique de France (SHF). His research expertise spans Cavitation & Multiphase flows, Flow Induced Noise & Vibration, Fluid-Structure Interaction, Flow control, Flow instabilities in hydro turbines and pumps, Condition monitoring of Hydraulic Machines, Hemodynamics, and Advanced Instrumentation in Fluid Dynamics. Farhat's work uniquely bridges fundamental fluid mechanics with practical applications across hydropower, marine propulsion, healthcare, and water management sectors. Analysis of his recent publications reveals strong focus on cavitation bubble dynamics, with particular emphasis on measurement techniques for collapsing bubbles, vortex shedding control, hydrodynamic monitoring of hydraulic machinery, and biomedical applications of cavitation phenomena. His work increasingly integrates advanced imaging techniques with computational modeling to understand complex multiphase flow phenomena. 2021: Life Sciences Book Award of the International Academy of Astronautics 2019: 1st Prize Winner of Scientific Image Contest (Swiss National Science Foundation) 2020: EPFL-Rhyming Prize (Best PhD thesis in Fluid Mechanics) 2018: EPFL-EDME Prize (Best PhD thesis in Mechanics) 2015: Edmund Optics Educational Award 2014: APS-DFD Gallery of Fluid Motion Award Farhat has successfully supervised numerous PhD students including Ali Amini, Philippe Ausoni, and Outi Supponen, with research spanning from fundamental bubble dynamics to practical hydraulic machinery applications. His Cavitation Research Group maintains strong collaborations with industry partners in hydropower and medical device sectors. Current research directions include advanced instrumentation for cavitation monitoring, condition-based maintenance of hydraulic machinery, and biomedical applications of cavitation phenomena in therapeutic ultrasound and drug delivery.

Yves Bourgaultمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Numerical Methods
• Finite Element Methods
• +۳ مورد دیگر

Yves Bourgault is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds a MSc and PhD from Laval University. His research focuses on computational fluid dynamics, numerical methods, finite element techniques, and continuum mechanics modeling, with applications in cardiac electrophysiology and ecological systems. Dr. Bourgault has supervised several graduate students, including Edward Boey (co-supervised), Sana Keita, Saint-Cyr Koyagurebo-Ime, and Kak Choon Loy. His work integrates advanced numerical techniques to address complex problems in biomedical engineering, environmental science, and mathematical physics. Key methodologies include finite element methods, deferred correction schemes, and anisotropic mesh adaptation. His research group is part of the Applied Mathematics division at the University of Ottawa, emphasizing interdisciplinary applications. Recent work explores climate change impacts on ecological systems, cardiac tissue modeling using high-resolution MRI data, and robust numerical methods for reaction-diffusion equations. Publications span topics such as bidomain models for cardiac electrophysiology, fluid-structure interaction in heart mechanics, and mathematical modeling of fuel cells. His contributions bridge theoretical numerical analysis with real-world biomedical and environmental challenges.

Amir Shmuelمشاهده پروفایل

استاد

• Neuroimaging
• fMRI
• Visual Perception
• +۵ مورد دیگر

Amir Shmuel is a Professor at McGill University's Faculty of Medicine, holding appointments in the Department of Neurology and Neurosurgery, Department of Biomedical Engineering, and Department of Physiology. He serves as Director of the Brain Imaging Signals Lab and Core Faculty at the McConnell Brain Imaging Centre of the Montreal Neurological Institute. His leadership includes chairing the 2018 International Society for Brain Connectivity conference and securing an $18.7M Canada Foundation for Innovation grant for Quebec's first large-bore 7 Tesla MRI scanner. Dr. Shmuel's research focuses on understanding neuronal mechanisms underlying functional brain imaging signals and visual information processing. His integrative approach combines fMRI, optical imaging, multi-channel neurophysiological recordings, and optogenetics across multiple spatial and temporal scales. Current research priorities include resting-state functional connectivity mechanisms, cortical lamina-resolved neurophysiology, and computational modeling of brain signals. His lab emphasizes parallel model development with experimental data acquisition. Recent publications demonstrate strong trends in multimodal neuroimaging integration, with emphasis on high-resolution fMRI techniques (especially 7T applications), resting-state connectivity analysis across species, and computational modeling of neurovascular coupling. Key subfields include laminar-specific activity mapping, artifact detection in medical imaging using deep learning, and cross-species functional connectivity frameworks. Dr. Shmuel's research is currently funded by the Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council of Canada (NSERC), and the US Department of Defense. His lab maintains active collaborations through initiatives like the International Society for Brain Connectivity and the PRIME-DE database consortium. Operating within the Brain Imaging Signals Lab at the Montreal Neurological Institute, Shmuel's team develops and applies advanced multimodal techniques including simultaneous fMRI-electrophysiology, voltage-sensitive dye imaging, and computational modeling frameworks. The lab maintains strong ties with the McConnell Brain Imaging Centre and participates in major open-science initiatives including the Tanenbaum Open Science Institute.