جست‌وجوی استاد و پژوهشگر

• Biomedical Engineering
• Nanotechnology
• Microelectronics
• +۵ مورد دیگر

Professor Omid Kavehei is a Professor of Intelligent Microsystems in the Faculty of Engineering at the University of Sydney, serving as Deputy Head of the School of Biomedical Engineering. Previously, he held roles as a Research Fellow at the University of Melbourne and Lecturer at RMIT University. His research focuses on biomedical microsystems, nanotechnology, and brain-inspired hardware for healthcare applications, particularly epilepsy monitoring and seizure prediction. Education: PhD (specific institution not explicitly stated) His work bridges nanoelectronics and healthcare, aiming to develop low-power, brain-inspired devices for sensory perception and medical diagnostics. Key interests include neuromorphic engineering, wearable sensors, and AI-driven medical systems. Awards include the Ramaciotti Biomedical Research Award (2021), Microsoft AI for Accessibility Grant (2019), and multiple teaching/research excellence awards from the University of Sydney. His recent work explores neuromorphic cytometry for cell analysis, edge AI for ECG/EEG diagnostics, and closed-loop neurostimulation systems. Collaborations include the Sydney Nano Institute, Brain and Mind Centre, and industry partners like Microsoft. Current research students are advancing topics like bio-inspired algorithms for seizure detection, hardware-friendly machine learning models, and flexible sensor systems for aquatic environments. Grants include funding for neurophysiology platforms, quantum sensors, and semiconductor design. Labs/teams: Involved in the Centre for Drug Discovery Innovation and collaborations across engineering, neuroscience, and clinical domains.

Avery Bermanمشاهده پروفایل

استادیار

• Medical Physics
• Functional Neuroimaging
• Biomedical Engineering
• +۵ مورد دیگر

Avery Berman is an Assistant Professor in the Department of Physics at Carleton University and a Scientist at the University of Ottawa Institute of Mental Health Research (IMHR) at The Royal. His work focuses on advancing functional MRI (fMRI) techniques for high-resolution imaging of brain activity and physiology, with applications in neuroscience and mental health disorders. PhD in Biomedical Engineering and MSc in Medical Radiation Physics from McGill University Postdoctoral research at Harvard Medical School and the Martinos Center for Biomedical Imaging Research Interests include: High-resolution fMRI at 7 Tesla Biophysical modeling of vascular networks Quantitative biomarkers for oxygen metabolism PET-MRI hybrid imaging systems Neurovascular coupling in mental illness Scientific Awards include: NSERC Canada Graduate Scholarship (Master's) CIHR Canada Graduate Scholarship (Doctoral) CIHR Postdoctoral Fellowship (top 10/600 applicants) NSERC Postdoctoral Fellowship (top Physics section recipient) Research Funding from NSERC, CFI, and institutional support from Carleton University and IMHR. His lab develops the open-source BOLDsωimsuite software for fMRI signal modeling and collaborates with Canada-wide vascular training programs.

Vittorio Saggiomoمشاهده پروفایل

دانشیار

• Material Science
• Three Dimensional Printing
• Gold Nanoparticles
• +۱۰ مورد دیگر

Vittorio Saggiomo is an Associate Professor in BioNanoTechnology at Wageningen University & Research , with significant collaborations at University College Dublin and other institutions. His work bridges material science, microfluidics, and open science hardware development. Research Interests include: 3D Printing Innovations Nanocomposite Material Development Open-Source Scientific Hardware Microplastic Detection Systems Algae-based Biopolymers Peptide Hydrogel Engineering Recent Publications demonstrate expertise in: Low-cost microscopy solutions (EnderScope/ESPressoscope) Algae-derived 3D printing resins Optical waveguide biomaterials Open science frameworks for fluidics Key Projects involve: Modular Microfluidic Catalysis Systems Self-optimizing Fluidic Technologies Microalgal Biorefineries for Food/Nutraceuticals Low-Field NMR/MRI Development Academic Activities feature multiple oral presentations on open microfluidics and hardware at international conferences (2025) and workshop organization like the Wageningen Biodiversity Challenge.

Greg Zaharchukمشاهده پروفایل

استاد

• Neuroimaging
• Radiology
• Artificial Intelligence in Medical Imaging
• +۴ مورد دیگر

Greg Zaharchuk is a Professor of Radiology specializing in Neuroimaging and Neurointervention at Stanford University's School of Medicine. His academic roles include leadership in clinical and translational imaging research. He holds an MD from Harvard Medical School, a PhD in Applied Physics from Harvard/MIT, and dual undergraduate degrees in Materials Science & Engineering and German Studies from Stanford. Education: MD, Harvard Medical School (2000) PhD, Harvard/MIT (1999) BS & BA, Stanford (1990) Research Focus: Dr. Zaharchuk's work centers on advancing neuroimaging techniques for cerebrovascular diseases, stroke, and neurodegenerative disorders. He pioneers AI-driven innovations in MRI/PET fusion, low-dose imaging, and deep learning models to enhance diagnostic accuracy and clinical decision-making. Key areas include stroke outcome prediction, amyloid PET reconstruction, and cross-modality imaging integration. Research Trends: His recent articles emphasize AI applications in neuroimaging, such as improving low-field MRI quality and PET reconstruction using MRI priors. He also explores stroke lesion segmentation, CBF quantification, and the ethical implications of AI in neuroradiology. Affiliations & Labs: He leads the Center for Advanced Functional Neuroimaging at Stanford, advancing cutting-edge imaging technologies. His research bridges clinical practice and innovation, addressing challenges in ischemic stroke, Alzheimer's imaging, and radiation dose reduction. Grants & Collaborations: His work is supported by NIH grants and industry partnerships, focusing on multimodal imaging systems and AI-driven diagnostics. Collaborations include international neuroradiology societies and interdisciplinary teams in neurology and computer science.

Ning Chengمشاهده پروفایل

استادیار

• Neurodevelopmental Disorders
• Autism Spectrum Disorder
• Fragile X Syndrome
• +۵ مورد دیگر

Ning Cheng is an Assistant Professor (Teaching & Research) at the University of Calgary's Faculty of Veterinary Medicine, with affiliations to the Alberta Children's Hospital Research Institute (ACHRI) and Hotchkiss Brain Institute (HBI). Their research focuses on neurodevelopmental disorders, particularly autism and Fragile X Syndrome, using rodent models to investigate mechanisms and develop experimental therapeutics. PhD in neuroscience from Johns Hopkins School of Medicine Postdoctoral work on neurological disorders at NIH Research spans molecular mechanisms, neural circuitry, and translational approaches to address social, communication, and behavioral challenges in autism spectrum disorders. The lab utilizes in vivo recording/modulation of brain activity, biochemical analyses, and multidisciplinary collaborations. Recent publications highlight expertise in EEG biomarker discovery, auditory processing abnormalities, ketogenic diet interventions, and ERK pathway modulation in mouse models of neurodevelopmental conditions. Key technologies developed include wireless cortical hemodynamic monitoring systems (TinyIOMS) and open-source electrophysiology tools (OSERR). As part of ACHRI's Precision Medicine & Disease Mechanisms program and HBI's Neurodevelopment/SCNIP strategic initiatives, Cheng contributes to university-wide efforts in Brain and Mental Health (2015-2021) and Child Health and Wellness (2020-2025).

Luca Vizioliمشاهده پروفایل

دانشیار

• Neuroimaging
• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• +۳ مورد دیگر

Luca Vizioli is an Associate Professor in the Department of Radiology at the University of Minnesota, affiliated with the Center for Magnetic Resonance Research. His work focuses on advancing neuroimaging techniques, particularly at ultrahigh magnetic fields (e.g., 10.5T), to enhance spatial and temporal resolution in functional MRI (fMRI). Research interests include RF coil design, noise reduction algorithms (e.g., NORDIC), and applications of deep learning to imaging challenges. Education details are not explicitly stated, though his PhD is noted. His research spans cutting-edge topics like laminar fMRI at UHF, denoising strategies for submillimeter resolution, and the impact of psychedelic substances on brain networks. He collaborates on tools like fMRIPrep Lifespan for developmental neuroimaging preprocessing. Key technical innovations include high-Quality 0.5mm isotropic fMRI leveraging random matrix theory and physics-driven AI, as well as multi-echo distortion correction frameworks. His work bridges engineering (coil design) and neuroscience (cortical processing mechanisms), with implications for understanding both normal brain function and neurological disorders.

Marco Grangettoمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Artificial Intelligence
• +۵ مورد دیگر

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

Jeremy Gordonمشاهده پروفایل

دانشیار

• Medical Physics
• Magnetic Resonance Imaging (MRI)
• Metabolic Imaging
• +۳ مورد دیگر

Dr. Jeremy Gordon is an Associate Professor in the Department of Radiology & Biomedical Imaging at the University of California, San Francisco (UCSF). He holds a PhD in Medical Physics from the University of Wisconsin-Madison (2013) and a BS in Physics & Astronomy from the University of Georgia (2008). Prior to his faculty role, he worked as a postdoctoral scholar (2013–2016) and Senior Bioengineer (2016–2020) at UCSF. His research focuses on developing novel hyperpolarized MRI techniques for metabolic imaging, particularly in pancreatic cancer, multiple sclerosis, Alzheimer’s Disease, and brain perfusion studies. These methods leverage hyperpolarized 13C and x-nuclei to assess metabolic pathways and treatment responses. Key applications include non-invasive metabolic biomarker identification and improved clinical care for cancer and neurological disorders. Major awards include the Distinguished Reviewer Award (2018), RSNA ITARSc Program recognition (2017), and multiple merit awards from the International Society for Magnetic Resonance in Medicine (2013). He is affiliated with the Advanced Imaging Technologies Resource Group and the Hyperpolarized MRI Technology Resource Center at UCSF. Recent work emphasizes multi-center clinical trials, data standardization (DICOM integration), and open-source tools for X-nuclear imaging. His lab collaborates widely, with studies involving metabolic imaging of the brain, abdomen, and heart under various physiological conditions (e.g., glucose challenges).

Dimo Ivanovمشاهده پروفایل

استادیار

• Ultra-high field MRI
• Quantitative MRI techniques
• Brain microstructure imaging
• +۷ مورد دیگر

Dr. Dimo Ivanov is an Assistant Professor at the Department of Cognitive Neuroscience at Maastricht University in the Netherlands. Since 2025, he has also served as Head of the 7 Tesla MR Core structure at the Cooperative Brain Imaging Center and as an Independent Research Group Leader at the Max Planck Institute for Empirical Aesthetics in Frankfurt, Germany. His work bridges physics, neuroscience, and advanced imaging technology. Dr. Ivanov earned his PhD (Dr. rer. nat.) in Physics from the University of Leipzig/Max Planck Institute for Human Cognitive and Brain Sciences in 2012, followed by an M.Sc. in Physics from the International Physics Studies Program at the University of Leipzig in 2007. His educational foundation was completed with studies at the Foreign Language High School in Pleven, Bulgaria. Dr. Ivanov specializes in high-resolution structural, perfusion, and functional mapping of the human cortex and subcortical regions. His research focuses on quantitative MRI techniques to assess tissue vascularization, microstructure, myelination, and iron content. He develops advanced MRI sequences and analysis pipelines for both 7T and 3T systems, with translational applications in neurodegenerative disorders, psychiatric conditions, and neurodevelopmental studies. His work on resting-state and task-based fMRI provides critical insights into neural connectivity and brain dynamics. His publication record demonstrates consistent contributions to ultra-high field MRI methodology, with emphasis on 7T and 9.4T systems. His research spans technical MRI development, quantitative imaging methods, and clinical applications across multiple neurological conditions. Recent work shows increasing focus on translational applications connecting advanced imaging techniques with clinical outcomes. Dr. Ivanov has received numerous scientific awards and successfully secured competitive funding: ERC Marie Curie Program (2023) - 2-year postdoc position Maastricht University-Chinese Science Council PhD Program (2022) Maastricht University Center for Integrative Neuroscience PhD Program (2022) Magnetic Resonance in Medicine issue cover (2017) ISMRM merit award magna cum laude (2013) Trainee abstract travel award from the Organization for Human Brain Mapping (2011) Educational Stipends from the International Society for Magnetic Resonance in Medicine (2009-2011) Dr. Ivanov has successfully mentored postdoctoral researchers and PhD students through competitive funding mechanisms. His research group at the Max Planck Institute for Empirical Aesthetics and his position at Maastricht University support a collaborative environment that bridges physics, neuroscience, and clinical applications. His work with the 7 Tesla MR Core structure provides critical infrastructure for advanced neuroimaging research in Frankfurt. Dr. Ivanov leads the Structural and Physiological Imaging with High-Field MRI research group at the Max Planck Institute for Empirical Aesthetics. His team specializes in pushing the boundaries of ultra-high field MRI technology, developing novel acquisition and analysis methods that enable unprecedented insights into brain structure and function. The group maintains strong collaborations with multiple institutions across Europe and beyond.

• Orthopedic Surgery
• Shoulder Reconstruction
• Elbow Surgery
• +۴ مورد دیگر

Kenneth W. Donohue, MD, MS is an Assistant Professor at Yale University's School of Medicine , affiliated with the Department of Orthopaedics & Rehabilitation . He specializes in shoulder, elbow, wrist, and hand surgery , with dual fellowships in shoulder & elbow surgery and hand surgery , and additional training in advanced elbow surgery at Mayo Clinic. His clinical focus includes rotator cuff repair , shoulder replacement , and upper extremity trauma . Georgetown University (MD, Physiology MS) Drexel University (Orthopedic Residency) Cleveland Clinic (Shoulder & Elbow Fellowship) Baylor College of Medicine (Hand Surgery Fellowship) Mayo Clinic (Advanced Elbow Surgery) Dr. Donohue's research explores shoulder arthroplasty , patient-specific implant design , and outcomes in diabetic surgical patients , with recent studies on semaglutide's impact on surgical outcomes . He emphasizes nonoperative treatment and patient-specific approaches in his clinical practice. His work is published in leading journals including The Journal of Bone and Joint Surgery and JSES International . Dr. Donohue actively contributes to Yale's 3D Collaborative for Medical Innovation , integrating medical device design into clinical applications. He is board-certified in Hand Surgery (Orthopedics) and Orthopedic Surgery by the American Board of Orthopaedic Surgery.

Louis S. Bouchardمشاهده پروفایل

دانشیار

• Physical Chemistry
• Biomedical Engineering
• Nanoscience
• +۹ مورد دیگر

Louis S. Bouchard is an Associate Professor in the Department of Chemistry at the University of California, Los Angeles (UCLA). His interdisciplinary research spans physical chemistry, biomedical engineering, and quantum computing, with a focus on NMR/MRI technologies, immunotherapy, and materials science. He earned a B.Sc. in Physics and Business Management from McGill University, a M.Sc. in Medical Biophysics from the University of Toronto, and a Ph.D. in Chemistry from Princeton University. His postdoctoral work at UC Berkeley with Alex Pines advanced low-field NMR and hyperpolarization methods. Research Interests : Physical & analytical chemistry, materials for immunotherapy, MRI contrast agents, biosensors, quantum control, machine learning in biomedical imaging. Lab Focus : Operando NMR methods, molecular kinetics, tissue engineering, quantum computing, and machine learning algorithms. His group has developed groundbreaking technologies, including: NMR methods for topological insulator surface states 12% 15N hyperpolarization catalysts for MRI Operando NMR in catalytic reactors Multi-channel 3D tissue bioreactors Scientific awards include the Beckman Young Investigator Award (2012), Dreyfus New Faculty Award (2008), and multiple UCLA faculty development grants. Current projects recruit students in machine learning , molecular kinetics , and quantum computing applications to chemistry and biology.

Junzhong Xuمشاهده پروفایل

دانشیار

• Quantitative MRI Methods
• Neurodegenerative Diseases
• Cancer Imaging
• +۳ مورد دیگر

Junzhong Xu is an Associate Professor of Radiology and Radiological Sciences, Biomedical Engineering, and Physics and Astronomy at Vanderbilt University. His research focuses on developing advanced quantitative MRI methods for applications in neurodegenerative diseases and cancer, including MRI cell size imaging and tumor treatment response assessment. He holds affiliations with the Vanderbilt University Institute of Imaging Science (VUIIS) and has expertise in microstructural diffusion MRI and GPU-accelerated imaging tools. Xu’s work spans interdisciplinary collaborations in biomedical engineering and physics. Education: B.S., University of Science and Technology of China M.S., University of Science and Technology of China Ph.D., Vanderbilt University Research Interests: Development of MRI cell size imaging for cancer and neurodegenerative diseases Quantitative assessment of tumor microstructure and treatment response Applications of microstructural diffusion MRI in multiple sclerosis and oncology Key Contributions: Advancement of MRI cytometry for non-invasive disease assessment GPU-accelerated tools for diffusion MRI simulation (MATI) Imaging biomarkers for early detection of neurodegenerative pathology Labs/Teams: Active member of the Vanderbilt University Institute of Imaging Science (VUIIS), collaborating on interdisciplinary imaging projects.

Xingde Liمشاهده پروفایل

استاد

• Neurophotonics
• Translational Biophotonics
• Optical Coherence Tomography (OCT)
• +۳ مورد دیگر

Xingde Li, PhD, is a Professor at Johns Hopkins University with joint appointments in Biomedical Engineering, Electrical & Computer Engineering, and Oncology. He leads the Laboratory of Biophotonics Imaging Technologies (BIT) and focuses on advancing neurophotonics and translational biophotonics for medical applications. Education : PhD in Physics and Biomedical Optics from University of Pennsylvania (1998); BS in Physics from University of Science and Technology of China (1990). Research Interests : Develops cutting-edge imaging technologies like Optical Coherence Tomography (OCT), two-photon fiberscopy, and AI-assisted imaging for disease diagnostics (e.g., cancer, COPD, preterm birth). His work bridges optics, micro-nano technology, and clinical medicine. Grants & Awards : Received grants from the Bisciotti Foundation Translational Fund (2021 and 2018) and has been featured in high-impact media for innovations like the MAGIC imaging capsule. Labs & Teams : Directs the BIT Lab, collaborating with interdisciplinary teams to translate technologies into clinical tools for disease detection and therapy guidance.

Christopher Bidinostiمشاهده پروفایل

استاد

• Nuclear Magnetic Resonance
• Medical Imaging
• Machine Learning
• +۳ مورد دیگر

Dr. Christopher Bidinosti is a Professor in the Department of Physics at the University of Winnipeg. He holds a Ph.D. from the University of British Columbia (2000) and specializes in nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), and machine learning applications in agriculture. His research spans low-field NMR/MRI, hyperpolarized nuclei, and collaborations like the TerraByte project, aimed at advancing agricultural machine learning through open-access plant image datasets. His research interests include TRASE MRI, neutron electric dipole moment (EDM) searches, GPU computing, and magnetics (coil design, shielding). He teaches courses such as Electricity & Magnetism and Optics and Waves. His work bridges physics and applied technology, with a focus on interdisciplinary innovation. Bidinosti collaborates with institutions like UWinnipeg’s Computer Science department and has developed hardware solutions for NMR/MRI systems. His contributions span experimental physics, computational methods, and agricultural technology, leveraging both theoretical and applied approaches.

Brian A. Hargreavesمشاهده پروفایل

استاد

• Magnetic Resonance Imaging (MRI)
• Medical Imaging Technology
• Biomedical Engineering
• +۳ مورد دیگر

Brian A. Hargreaves is a Professor of Radiology at Stanford University, with courtesy appointments in Electrical Engineering and Bioengineering. He focuses on body MRI technology development and applications, including breast, abdominal, and musculoskeletal imaging. His research includes non-contrast breast MRI screening, rapid quantitative knee MRI, MRI near metallic implants, and interventional guidance for surgery. He directs the Body MRI research group and co-directs the IMMERS AR/VR lab for medical visualization. Education: PhD in Electrical Engineering (Stanford, 2001), M.S. (Stanford, 1997), and B.A.Sc. in Engineering Science (Simon Fraser University, 1994). Research interests span MRI hardware/software innovations, clinical translation, and mixed-reality medical applications. He teaches graduate courses and lectures widely at Stanford. Awards include Fellowships from ISMRM and AIMBE. Key projects emphasize improving MRI safety and accuracy for clinical use, particularly in challenging scenarios like metallic implants or interventional procedures. His work bridges engineering and medical practice to enhance diagnostic and surgical tools.