جست‌وجوی استاد و پژوهشگر

Mark Cutkoskyمشاهده پروفایل

استاد

• Robotics
• Biomimetics
• Tactile Sensing
• +۵ مورد دیگر

Mark Cutkosky is the Fletcher Jones Professor in the School of Engineering at Stanford University. His research focuses on bioinspired robotics, tactile sensing, and advanced manufacturing. He leads the Biomimetics and Dexterous Manipulation Lab, developing innovative robotic systems for applications in healthcare, space exploration, and industrial automation. Education: PhD in Mechanical Engineering, Carnegie Mellon University (1985) Research Interests: Cutkosky designs robots and sensors inspired by biological systems, such as gecko-inspired adhesives and whisker-like tactile sensors. His work emphasizes practical applications, including MRI-compatible teleoperators for medical procedures, miniature implantable sensors for cardiac monitoring, and robots for Martian cave exploration. He also explores soft robotics, haptic interfaces, and rapid prototyping tools. Key Projects: ReachBot: A compact robot for extreme environments like Martian lava tubes Gecko-inspired adhesives for space and industrial gripping VITALS: Implantable sensor networks for post-surgical cardiac care Grants & Labs: His lab has pioneered tactile sensor networks and bioinspired design frameworks. Current work includes collaborative projects on surgical robotics, biomedical devices, and materials science.

• Neuroscience
• Primate Behavior
• Neuroimaging
• +۳ مورد دیگر

Ross Muers is a Research Fellow at Scotland's Rural College (SRUC), affiliated with the Dairy Research Centre and the Agriculture & Business Management department. His work focuses on neuroscientific methodologies involving non-human primates, particularly macaque monkeys. Key research areas include optogenetics applications, neuroimaging techniques (e.g., MRI), and behavioral analyses in primate models. His educational background and specific training details are not explicitly mentioned in the provided text. Research contributions emphasize cross-species comparisons in cognitive processes and the development of specialized experimental tools for animal studies. Publications highlight innovations in neuroimaging protocols, optogenetic resources, and custom research equipment for non-invasive primate studies. No scientific awards are noted in the current data. Active involvement in collaborative projects addressing animal welfare and experimental reliability is implied through his work's focus on methodological advancements. Advising roles and student mentorship details are absent from the provided information. Lab affiliations include the Dairy Research Centre, where he contributes to interdisciplinary research at the intersection of agriculture, business management, and biomedical science.

Grégoire Courtineمشاهده پروفایل

استاد

• Neuroscience
• Neuroprosthetics
• Motor Control
• +۳ مورد دیگر

Grégoire Courtine is a Professor at École Polytechnique Fédérale de Lausanne (EPFL), directing the UPCOURTINE lab within the Neuro-X Institute. His research focuses on developing neuroprosthetics and neurorehabilitation strategies to restore motor function after spinal cord injuries and neurological disorders. The lab combines spinal cord stimulation robotic interfaces optogenetics single-cell omics clinical translation to advance treatments for paralysis and movement disorders. Recent work spans both fundamental studies and clinical trials, including interventions for Parkinson’s disease and orthostatic hypotension. Publications highlight innovations in deep brain stimulation neuroprosthetic systems spinal cord mapping autonomic function modulation with a strong emphasis on interdisciplinary approaches bridging neuroscience and engineering.

Dan Xiaoمشاهده پروفایل

استاد

• Magnetic Resonance Imaging
• Artificial Neural Networks
• Biomedical Engineering
• +۳ مورد دیگر

Professor Dan Xiao is a prominent academic in the Department of Physics at the University of Windsor’s Faculty of Science. His research focuses on advancing magnetic resonance imaging (MRI) technologies, integrating artificial neural networks for enhanced data analysis, and developing low-cost, portable MRI systems. He has led initiatives such as the Essex CORe science facility and actively promotes women in science through programs like the USci Network’s Women in Science Initiative. Dr. Xiao emphasizes hands-on learning, mentoring students in projects like the Outstanding Scholars program and hackathons. His work spans biomedical engineering, material science, and porous media analysis, with notable contributions to SPRITE MRI, T2/T1 mapping, and RF coil optimization. He collaborates with industry and government, as seen during a visit by Canada’s science minister Kirsty Duncan to the Essex CORe facility. His publications highlight innovations in MRI hardware design, neural network applications for signal processing, and non-invasive diagnostic methods. Advising students on topics from cell tracking to cement paste characterization, Xiao bridges theoretical research with practical, real-world applications in healthcare and engineering.

Vladimir Milovanovićمشاهده پروفایل

دانشیار

• Electrical Engineering
• Computer Science
• Signal Processing
• +۷ مورد دیگر

Dr. Vladimir Milovanović is an Associate Professor at the Faculty of Engineering, University of Kragujevac, Serbia, holding a position as Chair of the Department of Electrical Engineering and Computer Sciences. His academic career is anchored in interdisciplinary research at the intersection of electrical engineering, computer science, and biomedical applications. His research focuses on hardware design, radar sensor systems, biomedical signal processing, and machine learning applications. Notable areas include CMOS circuit design, FPGA-based implementations, and parameterizable hardware generators for signal processing tasks. He also explores medical imaging analysis using deep learning techniques for conditions like disc hernia diagnosis. Recent work emphasizes radar technology (FMCW transceivers, signal processing architectures), embedded systems (FPGA development, IP core utilization), and high-performance streaming data algorithms. His publications span over two decades, demonstrating expertise in analog/digital circuit design, comparative classifier analysis in biomedical contexts, and hardware-software co-design methodologies. Dr. Milovanović collaborates with industry on energy-efficient MIMO arrays and semiconductor gas sensors. His contributions include open-source HDL models for digital signal processors and comparator circuits optimized for low-power and high-speed applications.

• Applied Informatics in Engineering
• Biomedical Engineering
• Machine Learning
• +۳ مورد دیگر

Tijana Geroski is an Assistant Professor at the Faculty of Engineering, University of Kragujevac, Serbia, affiliated with the Department of Applied Mechanics and Automatic Control. Her work integrates applied informatics in engineering with biomedical applications, focusing on machine learning, medical imaging, and computational modeling. She contributes to interdisciplinary research through platforms like SGABU, designed to bridge education and research in multiscale modeling. Her research spans topics such as predictive modeling for cardiovascular diseases, AI-driven diagnostic systems (e.g., lung and spine imaging analysis), and simulation tools for personalized medicine. Notable projects include developing FPGA-based expert systems for disc hernia diagnosis and optimizing drug delivery via computational fluid dynamics. She also explores the intersection of big data, materials science, and epidemiological modeling, particularly in the context of the SARS-CoV-2 pandemic. Her articles emphasize interdisciplinary collaboration, coupling experimental data with numerical simulations. Recent work highlights advancements in transfer learning for medical imaging, physics-driven deep learning for thermal modeling, and FPGA implementations of Bayesian diagnostic systems. These contributions underscore her commitment to advancing healthcare through computational and AI-based solutions. She collaborates actively with institutes like SARTIK (Steinbeis Advanced Risk Technologies Institute) to enhance biomaterial risk assessment and in-silico clinical trials. Her teaching and research roles reflect a dedication to both academic innovation and practical biomedical engineering applications.

Federico de Martinoمشاهده پروفایل

استاد

• Neuroimaging
• Auditory Perception
• Machine Learning
• +۳ مورد دیگر

Federico de Martino is a Full Professor at Maastricht University's Faculty of Psychology and Neuroscience (FPN), specializing in Sensory Circuits and Neuroimaging. He holds an honorary researcher position at the Center for Magnetic Resonance Research (CMRR) in Minneapolis, USA. His research integrates computational models with high-resolution neuroimaging techniques to study auditory perception and predictive coding in the brain. Education: Graduated in Electronic Engineering (cum laude) from Sapienza University of Rome (2003), earned a PhD in Cognitive Neuroscience from Maastricht University (2008). Career milestones include roles as Post-Doctoral Fellow (2008-2011), Research Associate at CMRR (2009-2012), Assistant Professor (2011-2017), and Associate Professor (2017-2022) at FPN. Research interests focus on ultra-high field MRI (7T+), mesoscopic neural computations, and the functional organization of auditory pathways. Key projects include mapping auditory circuits at sub-millimeter resolution and investigating vascular underpinnings of fMRI signals. Collaborations span institutions like CMRR, University of Glasgow, and Max Planck Institute. Teaching roles include coordinating fMRI data analysis courses at FPN and the Maastricht Science Program. He co-chairs the Maastricht Brain Imaging Center (MBIC) Graduate School and leads FPN's MRI user support group. Active in science outreach as a founding member of the Maastricht Young Academy (MYA) and co-chair of the Growing Up in Science (GUS) initiative. Grants include an NWO VIDI grant (auditory pathway mapping), NIH Brain Initiative funding (laminar fMRI), and an ERC Consolidator grant (predictive listening). Recent publications emphasize high-resolution auditory imaging, predictive coding, and methodological advancements in neuroimaging.

Andreas Schmidtمشاهده پروفایل

پژوهشگر

• Computer Science
• Artificial Intelligence
• Business Management
• +۳ مورد دیگر

Andreas Schmidt is affiliated with the University of Kassel, Germany, and holds a PhD in Computer Science from the University of Koblenz and Landau (2022). His research spans interdisciplinary domains including artificial intelligence, business management, and neuroscience. He has published extensively in journals like NeuroImage, SIAM Journal on Scientific Computing, and Remote Sensing of Environment, demonstrating expertise in computational methods, neural networks, and applied data science. Key research themes include propagation network modeling, home-office productivity analysis, and autonomous driving safety frameworks. His work integrates technical innovation with practical applications, such as optimizing district heating systems and advancing neurovascular coupling studies in primates. While no formal awards are listed, his contributions reflect impactful interdisciplinary collaboration across computer science, environmental science, and medical imaging.

• Neuroscience
• Pain Perception
• Neuroimaging
• +۳ مورد دیگر

Dr. Falk Eippert is a Research Fellow and Group Leader of the Max Planck Research Group Pain Perception at the Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) in Leipzig, Germany. His research focuses on understanding pain mechanisms, neuroimaging techniques for spinal cord and brain function, and placebo effects. He holds a PhD in Systems Neuroscience from the University of Hamburg and completed postdoctoral training at the University of Oxford's FMRIB Centre. Key research interests include the neural basis of pain perception, non-invasive electrophysiological methods for spinal cord activity recording, and high-field MRI applications in CNS studies. His work bridges clinical pain management and fundamental neuroscience, addressing topics like thermosensation modulation, neuroimaging reliability, and therapeutic neuromodulation effects. He has pioneered spinal cord fMRI protocols and developed automated segmentation tools for neuroimaging data. His lab actively contributes to open science initiatives through multi-center collaborations and shared datasets, advancing spinal cord and brain imaging standards.

• Neurology
• Imaging Technologies
• Stroke Research
• +۳ مورد دیگر

Peter Ludewig is a Professor at the University Medical Center Hamburg-Eppendorf (UKE), affiliated with the Department of Experimental Medicine and the Medical Faculty. His research focuses on neurovascular mechanisms, stroke pathophysiology, and advanced imaging techniques like Magnetic Particle Imaging (MPI). He leads interdisciplinary projects combining experimental medicine with clinical applications, particularly in cerebral perfusion analysis and neuroinflammation. Key areas of expertise include: stroke therapy development, immune responses in brain injury, and translational imaging technologies. His work bridges basic science and clinical practice, with contributions to understanding neutrophil dynamics, cytokine regulation (e.g., IL-17A), and novel drug delivery systems using nanobodies. Recent studies address cerebral microvascular remodeling, nanopore sequencing for lymphoma diagnosis, and optimizing MPI tracers for real-time perfusion assessment. He collaborates across departments including Neurology, Radiology, and the Cardiovascular Research Center (CVRC).

• Medical Imaging
• Neuroimaging
• Radiology
• +۴ مورد دیگر

Thomas Lindner is a Professor in the Department of Diagnostic and Interventional Radiology and Nuclear Medicine at the University Medical Center Hamburg-Eppendorf (UKE), affiliated with the University of Hamburg's Faculty of Medicine. His research focuses on advanced neuroimaging techniques, particularly arterial spin labeling (ASL) and MRI-based perfusion analysis, with applications in cerebrovascular diseases, stroke, and neuro-oncology. He leads efforts to standardize ASL protocols and improve diagnostic accuracy for conditions like intracranial aneurysms and gliomas. Lindner's work bridges clinical practice and cutting-edge imaging technology, emphasizing translational research and collaborative initiatives like the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). His studies often involve population-based analyses (e.g., Hamburg City Health Study) and explore socioeconomic factors influencing disease incidence. Lindner also investigates intraoperative MRI methods for pediatric neurosurgery and develops non-contrast-enhanced imaging approaches to reduce patient exposure to contrast agents. His contributions span technical innovations in MRI hardware/software, clinical trial design, and global standards for neuroimaging reporting and data sharing.

• Medical Imaging
• Occupational Health
• Magnetic Resonance Imaging (MRI)
• +۳ مورد دیگر

Michael Kaul is a Professor of Occupational Medicine at the University Medical Center Hamburg-Eppendorf (UKE), affiliated with the Medical Faculty. His research focuses on advanced medical imaging techniques, particularly Magnetic Resonance Imaging (MRI) and Magnetic Particle Imaging (MPI), with applications in tumor diagnostics, cardiovascular imaging, and occupational health. He leads studies on nanoparticle-based contrast agents, real-time imaging solutions for gastrointestinal bleeding, and quantitative MRI analyses for musculoskeletal disorders in athletes. His work bridges clinical practice and engineering innovations, contributing to non-invasive diagnostic tools and interventional therapies. Key research interests include MPI development for real-time vascular imaging, MRI techniques for fetal cardiology, and biomarker discovery in oncology. He collaborates on interdisciplinary projects involving nanotechnology, AI-driven diagnostics, and preclinical models for cardiovascular and respiratory diseases. His contributions span peer-reviewed articles in high-impact journals and partnerships with engineering teams to advance medical imaging hardware and protocols. Dr. Kaul’s academic roles include teaching at the Medical Faculty and supervising clinical research in occupational medicine. His work emphasizes translational research, aiming to improve early disease detection and personalized treatment strategies through cutting-edge imaging technologies.

Romulus Valeriu Flaviu Turcuمشاهده پروفایل

دانشیار

• Nuclear Magnetic Resonance
• Materials Science
• Medical Imaging
• +۳ مورد دیگر

Dr. Romulus Valeriu Flaviu Turcu is an Associate Professor at the Department of Physics, Babeș-Bolyai University, and Director of the National Institute for Research and Development of Isotopic and Molecular Technologies (NIRD-IMT) since 2019. He holds a Ph.D. in Physics from Babeș-Bolyai University, a master’s in Material Science, and a bachelor’s in engineering. With over 17 years in academia and R&D, his interdisciplinary expertise spans Nuclear Magnetic Resonance (NMR), materials science, medical imaging, and high-pressure engineering. His research has been conducted across institutions in Europe and North America, including the Pacific Northwest National Laboratory (USA) and Los Alamos National Laboratory. He also serves as a visiting professor at the University of Rochester’s Ain Center for Entrepreneurship. Dr. Turcu’s work focuses on advanced materials, energy technologies, and innovative instrumentation, with contributions to carbon sequestration studies and biomedical applications of MRI. Key research areas include NMR methodologies for material characterization, development of nanostructured composites, and applications of machine learning in energy systems. His publications reflect a strong emphasis on interdisciplinary approaches, combining experimental physics, computational modeling, and engineering solutions. Dr. Turcu leads strategic initiatives at NIRD-IMT, advancing Romania’s technological capabilities in isotopic and molecular technologies. He has coordinated numerous R&D projects for enterprises and international collaborations, demonstrating expertise in transitioning research into practical innovations.

• Medical Imaging
• Radiology
• Prostate Cancer
• +۷ مورد دیگر

Boris Bloch is an Adjunct Professor at the University of Massachusetts Chan Medical School's T.H. Chan School of Medicine , with appointments in the Radiology department and Urology department. His work focuses on Medical Imaging , particularly Prostate Cancer and Breast Cancer diagnostics using Magnetic Resonance Imaging (MRI) and Diagnostic Imaging techniques. Education: MD in Medicine and Psychology from University of Vienna, Austria Internship at Medical University Vienna, General Hospital Vienna, Austria Residency in Radiology at Harvard Medical School, Beth Israel Deaconess Medical Center, Boston Fellowships in MRI and Breast Imaging His research bridges Medical Informatics and Molecular Biology , with innovations in Radiomics , Image Registration Algorithms , and Biopsy Marker Technology . Bloch has contributed to Radio-Genomics approaches for predicting cancer risk scores and developed 3D-Printed Phantoms for radiation therapy. His work has been cited extensively, with 2019 studies on Prostate Tumor Detection and HER2-Positive Breast Cancer responses to therapy.

• Medical Imaging
• Ultrasound Technology
• Machine Learning
• +۳ مورد دیگر

Di Xiao is a Sessional Lecturer at the University of Waterloo. Their work focuses on advancing medical imaging technologies, particularly in ultrasound and MRI, with an emphasis on real-time processing, deep learning integration, and algorithm optimization. They are affiliated with the Sessional lecturers group. Research interests include ultrasound imaging innovations such as speed-of-sound estimation, beamforming techniques, and sparse-array optimizations. Their work also spans MRI reconstruction, signal processing, and applications of machine learning in medical diagnostics. Notable contributions include developing frameworks for live tissue assessment and minimizing image quality loss in sparse data scenarios. Publications highlight trends in real-time ultrasound systems, neural network-driven imaging enhancements, and sparsification strategies for MRI. The articles collectively emphasize interdisciplinary approaches merging AI with traditional medical imaging challenges. No scientific awards or grants are explicitly mentioned. No lab affiliations or student advising records are provided.