جست‌وجوی استاد و پژوهشگر

Sharmila Majumdarمشاهده پروفایل

استاد

• Medical Imaging
• Osteoporosis
• Osteoarthritis
• +۵ مورد دیگر

Sharmila Majumdar, PhD, is a Professor at the University of California San Francisco (UCSF) and holds joint appointments in the Departments of Bioengineering and Therapeutic Sciences and Orthopedic Surgery at UCSF, and in Bioengineering at UC Berkeley. She serves as Vice Chair of Research in Radiology and Biomedical Imaging and leads the Musculoskeletal Research Interest Group. Her research focuses on advanced imaging techniques (MRI and CT) for musculoskeletal disorders like osteoporosis, osteoarthritis, and lower back pain, with a focus on developing imaging tools for clinical translation. She directs an NIH-funded Center of Research Translation in Osteoarthritis and Imaging, collaborating with UCSF and UC Davis. Education: B.S. in Physics from St. Stephen's College, University of Delhi (1983); M.S. and Ph.D. in Engineering & Applied Physics from Yale University (1984, 1987), followed by postdoctoral training in Radiology at Yale (1988). She joined UCSF in 1989. Research interests include musculoskeletal imaging, quantitative MRI biomarkers, AI applications, and imaging-driven clinical trials. She has led over 20 NIH-funded grants, including longitudinal studies on osteoarthritis progression and imaging technology development. Awards include AIMBE and ISMRM Fellowships, the ISMRM Gold Medal, and UCSF's Excellence in Mentoring Award. Her recent work emphasizes AI-driven imaging solutions, such as digital twin models for knee joints and automated MRI scan prescriptions. She co-leads studies on hip and knee joint degeneration, linking imaging findings to clinical outcomes. Her lab's innovations in low-field MRI and metal artifact reduction aim to improve accessibility and diagnostic precision. Awards: AIMBE Fellow (2004), ISMRM Fellow (2008), ISMRM Gold Medal (2016), UCSF Mentoring Award (2007) Grants: NIH P50, R01, UH3, and multi-institutional collaborations Leadership: Director of NIH Osteoarthritis Imaging Center, Principal Investigator on 15+ grants Labs/Teams: Musculoskeletal Research Interest Group (interdisciplinary faculty/postdocs/students) and the UCSF Core Center for Musculoskeletal Biology and Medicine.

Yun-Sheng Chenمشاهده پروفایل

استادیار

• Photoacoustic Imaging
• Nanoparticles
• Biomedical Engineering
• +۵ مورد دیگر

Yun-Sheng Chen is an Assistant Professor at the University of Illinois at Urbana-Champaign, with affiliations in the Department of Electrical and Computer Engineering, Bioengineering, Biomedical and Translational Sciences, Micro and Nanotechnology Laboratory, and the Beckman Institute for Advanced Science and Technology. His research focuses on photoacoustic imaging, nanotechnology, and biomedical engineering, with applications in cancer diagnosis, therapeutic nanomaterials, and wireless power transfer. Chen’s work integrates optical, acoustic, and nanoscale technologies to develop innovative diagnostic and therapeutic tools. Key areas include dual-modal imaging techniques (photoacoustic/ultrasound), plasmonic nanostructures for tumor detection, and mechanochemical nanoparticles for targeted therapy. He also explores reconfigurable metasurfaces for wireless power systems and wearable technology. Recent contributions include advancements in sparsity-constrained photoacoustic imaging, biomimetic membrane-protected contrast agents, and injectable nanoparticles for deep-tissue therapy. His research bridges engineering and medicine, addressing challenges in precision imaging and therapy guidance.

Seth Smithمشاهده پروفایل

استاد

• MRI techniques
• Spinal cord imaging
• Optic nerve imaging
• +۵ مورد دیگر

Seth Smith is a Professor of Radiology and Radiological Sciences, Biomedical Engineering, and Ophthalmology at Vanderbilt University. He serves as Associate Director of the Vanderbilt University Institute of Imaging Science (VUIIS) and Director of the Center for Human Imaging. His research focuses on advanced MRI methods for detecting and quantifying pathologies in the spinal cord and optic nerve, with significant applications in multiple sclerosis. Ph.D. from Johns Hopkins University B.S. in Mathematics and Physics from Virginia Polytechnic Institute Dr. Smith’s work emphasizes functional connectivity, diffusion tensor imaging, and chemical exchange saturation transfer (CEST) MRI. His recent publications highlight collaborations on spinal cord imaging, neurodegeneration, and quantitative MRI techniques. He contributes to developing standardized protocols for spinal cord MRI and analyzing electronic medical records for disease comorbidities. Honors and awards are not explicitly mentioned in the provided text. Dr. Smith’s leadership roles include directing the Center for Human Imaging and contributing to the Vanderbilt University Institute of Imaging Science, impacting both clinical research and technological innovation in neuroimaging.

Dany Vargheseمشاهده پروفایل

پژوهشگر ارشد

• Inductive Logic Programming
• Explainable AI
• Computer Vision
• +۲ مورد دیگر

Dr. Dany Varghese is a Research Fellow in the Department of Computer Science at the University of Surrey's School of Computer Science and Electronic Engineering. He specializes in Learning and Reasoning using Inductive Logic Programming (ILP), focusing on developing transparent and interpretable machine learning systems. As a Fellow of the Higher Education Academy (FHEA), he maintains strong connections with Jyothi Engineering College in India where he previously served as Assistant Professor (2015-2019). His research develops computationally efficient methods for human-like learning from minimal data, particularly for critical applications like plant disease detection and medical diagnostics. He created the PyGol system - an explainable machine learning framework implementing Meta Inverse Entailment principles that outperforms traditional deep learning approaches in few-shot learning scenarios. Dr. Varghese has developed several influential tools including PyILP (Python interface for ILP systems), InfIntE (for microbial interaction inference), and contributes to the Meta Inverse Entailment framework. His collaborative work spans healthcare diagnostics, agricultural technology, and microbial ecology. Scientific Awards: Fellow of the Higher Education Academy (FHEA) Teaching Experience: Lab coordinator for Data Mining & Machine Learning (University of Surrey) Former Assistant Professor at Jyothi Engineering College (India) Lecturer at Government Engineering College (India)

Ahmed Abdulkadirمشاهده پروفایل

پژوهشگر

• Neuroimaging
• Machine Learning
• Medical Imaging Analysis
• +۵ مورد دیگر

Dr. Ahmed Abdulkadir is a Researcher at the University Clinics UPD, part of the University of Bern, specializing in neuroimaging and machine learning applications in medical diagnostics. He holds a PhD in Vision Science from the University of Freiburg and has extensive experience in roles such as Senior Researcher at Lausanne University Hospital and Visiting Researcher at the University of Pennsylvania. His work focuses on automated MRI analysis for Alzheimer’s disease, schizophrenia, and other neurological disorders, leveraging deep learning techniques to improve diagnostic accuracy and clinical workflows. He has contributed to advancements in volumetric segmentation (e.g., 3D U-Net), brain aging studies, and cross-disciplinary applications of AI in cardiology and thyroid cancer diagnostics. His research emphasizes bridging clinical needs with computational solutions, including tools like Atri-U for cardiovascular imaging. Education: PhD in Vision Lab, University of Freiburg (2012-2016) MSc in Life Science, EPFL Lausanne (2010-2012) BSc in Life Science, EPFL Lausanne (2007-2010) BSc in Systems Engineering, Fachhochschule NTB Buchs SG (2001-2004) Research Interests: Machine learning-driven neuroimaging analysis Automated diagnosis of neurodegenerative diseases Cross-modal data harmonization Deep learning applications in medical imaging Brain aging patterns and genetic correlates Articles Highlight: His work spans Alzheimer’s classification via structural MRI, schizophrenia-related brain network studies, and deep learning models for microbleed detection. Recent focus includes AI tools for thyroid cancer cytology and cardiovascular MRI. Grants/Teams: Collaborates with institutions globally, including Zurich University of Applied Sciences (AI Center), and contributes to large-scale studies like the iSTAGING project on brain aging patterns. No explicit awards listed, but contributions to high-impact clinical tools reflect recognition. Labs/Teams: Active in interdisciplinary teams at University Clinics UPD, Lausanne University Hospital, and the Vision Lab, focusing on translational AI applications in healthcare.

Joshua Wongمشاهده پروفایل

استادیار

• Deep Brain Stimulation
• Artificial Intelligence in Medicine
• Movement Disorders
• +۳ مورد دیگر

Dr. Joshua Wong is an Assistant Professor in the Department of Neurology at the University of Florida, specializing in deep brain stimulation (DBS) and artificial intelligence applications in neurology. He completed his bioengineering degree at UC San Diego, medical degree at New York Medical College, and neurology residency/fellowship in movement disorders at UF. His laboratory is part of the University of Florida’s AI Initiative, focusing on AI-driven improvements in clinical care and medical education for movement disorder patients, particularly those with Parkinson’s disease, essential tremor, and dystonia. His research integrates advanced neurotechnology and AI to address challenges in DBS therapy, such as optimizing stimulation parameters, predicting treatment outcomes, and analyzing motor function through video-based algorithms. Key areas include circadian rhythm influences on DBS efficacy, structural MRI biomarkers for neurocognitive outcomes, and the relationship between basal ganglia anatomy and therapeutic side effects. Dr. Wong’s work also explores cardiovascular risks in cancer survivors, including heart failure and coronary artery disease, highlighting interdisciplinary collaborations between neurology and oncology. His studies emphasize both clinical and technological advancements, aiming to personalize treatments and enhance patient engagement in care planning. He has contributed to numerous peer-reviewed articles on DBS neurophysiology, AI in movement disorder analysis, and cancer survivorship health risks. His lab’s open-source tools, like VisionMD for motor function analysis and BRAVO for DBS data visualization, exemplify his commitment to advancing neurotechnology accessibility.

MASSIMO GRASSIمشاهده پروفایل

دانشیار

• Auditory Perception
• Psychophysics
• Cognitive Psychology
• +۵ مورد دیگر

Massimo Grassi is an Associate Professor at the Department of General Psychology, University of Padova. His research focuses on auditory and audiovisual perception, psychophysics, and the development of experimental tools like the Psychophysics Toolbox and Psychoacoustics-Web. He emphasizes reproducibility in research through initiatives like the PECANS statement and contributes to open science practices. His work spans cognitive psychology, music cognition, and interdisciplinary projects linking art, technology, and perception. Grassi collaborates on tools for sonification in astronomy and studies the impact of music training on cognitive functions. Key research interests include auditory perception mechanisms, cross-modal correspondences, and the application of psychophysical methods to understand sensory and cognitive processes. He has explored topics such as working memory differences between musicians and non-musicians, the role of emotions in peripersonal space development, and the effectiveness of sonification in education and science communication. Grassi's recent work highlights contributions to open science frameworks, the development of free online research tools, and collaborative projects in music psychology and neuroimaging. He is actively involved in advancing methodological standards in psychological research while maintaining a focus on bridging experimental psychology with technological innovation.

• Medical Robotics
• Biomechanical Modeling
• Medical Imaging
• +۳ مورد دیگر

Ferdinand van der Heijden is an Associate Professor in Robotics and Mechatronics, specializing in medical robotics and surgical navigation. He holds an external researcher position at Antoni Van Leeuwenhoek Hospital since 2014. His work integrates robotics, biomedical engineering, and artificial intelligence to address challenges in surgical precision, diagnostic imaging, and patient outcomes. Research focuses include developing advanced imaging techniques for pleural diseases, improving laparoscopic camera accuracy, and leveraging AI for medical diagnostics. He has contributed to over 160 publications, with recent emphasis on pleural plaque quantification, crowd-sourced deep learning for hemorrhage detection, and personalized surgical models. He has supervised 11 academic works, though specific student names are not listed. His collaborations span global institutions, addressing topics like diabetic foot thermography and prostate intervention robotics. Notable projects include the MIRIAM robot for MR-guided needle interventions and biomechanical tongue modeling for post-surgery functional prediction. Key contributions include innovations in image-guided surgery tools, AI-driven diagnostic frameworks, and 3D visualization techniques for surgical planning. His work aligns with UN Sustainable Development Goals related to health and well-being (SDG 3).

• Medical Physics
• Biomedical Engineering
• Radiology and Organ Imaging
• +۶ مورد دیگر

Pete Lally is a Lecturer in Magnetic Resonance (MR) Physics at the Department of Bioengineering, Imperial College London, affiliated with the Faculty of Engineering. His research focuses on developing advanced MRI techniques for rapid, high-resolution imaging, particularly in neonatal brain studies and degenerative diseases at 3T and 7T fields. He leads the London Collaborative Ultra-High Field System (LoCUS) facility and collaborates with the Computational, Cognitive and Clinical Neuroimaging Laboratory and the Centre for Paediatrics and Child Health. Education: PhD in Magnetic Resonance Physics (Imperial College London, 2015–2018) MSc in Physics and Engineering in Medicine (University College London, 2010–2012) MPhys in Physics (Durham University, 2006–2010) Research Interests: Lally specializes in MRI technology advancements, including quantitative imaging, neonatal brain injury biomarkers, and ultra-high-field (7T) imaging for peripheral nerves and degenerative diseases. His work emphasizes clinical translation of techniques like RAFO-4 MRI and QDESS for simultaneous morphology and quantitative mapping. Professional Roles: Current: Lecturer in Bioengineering (2024–present) Prior: Sir Henry Wellcome Postdoctoral Fellow (2020–2024), BRC MR Physics Fellow (2018–2020), and Senior MR Physicist (2018). Labs & Collaborations: He directs the LoCUS facility and collaborates with the Centre for Perinatal Neuroscience and Imperial MRI Physics Collective. His work spans clinical and academic settings, addressing challenges in neonatal care and high-field MRI applications.

Rolf Gruetterمشاهده پروفایل

استاد

• Magnetic Resonance Spectroscopy
• Neuroimaging
• Metabolic Modeling
• +۲ مورد دیگر

Rolf Gruetter is Full Professor at École polytechnique fédérale de Lausanne (EPFL), holding joint appointments in the Laboratory of Functional and Metabolic Imaging (School of Life Sciences) and the Teaching Conference (CCE). His research focuses on advanced biomedical imaging techniques including magnetic resonance spectroscopy and neuroimaging. He teaches courses on biomedical imaging fundamentals and neuroscience. Honors include the Young Investigator Award (1999), ESMRMB Fellowship (2011), and EPFL Teaching Award (2011). Editorial responsibilities span multiple neuroscience and imaging journals including Journal of Neuroscience Research and Frontiers in Neuroenergetics. Research leadership includes supervision of 30+ PhD students and postdoctoral researchers. Laboratory investigations center on brain energy metabolism, hypoglycemia neuroscience, and development of novel MR instrumentation and methodologies.

• Osteoarthritis
• Biomechanics
• Medical Imaging
• +۳ مورد دیگر

Dr. David Wilson is a Professor in the Department of Orthopaedics at the University of British Columbia (UBC), with an Associate Member role in the Department of Mechanical Engineering. He holds a B.Eng. from McGill University and a D.Phil. in Engineering Science from the University of Oxford. His research focuses on joint mechanics, osteoarthritis, and medical imaging, particularly exploring the links between biomechanics and clinical outcomes. He leads the Wilson Mechanics Lab, specializing in synchrotron imaging, open MRI, and advanced cartilage analysis. Education: B.Eng. (McGill), D.Phil. (Oxford). Research Interests: Hip, knee, and spine mechanics; osteoarthritis etiology and treatment; medical imaging techniques (MRI, CT, ultrasound); orthopaedic sports medicine. Key Projects include developing methods for hip cartilage strain measurement using synchrotron imaging and studying the effects of hip deformities on joint mechanics. Collaborations involve radiologists, surgeons, epidemiologists, and computer scientists. His lab’s work aims to improve diagnostic accuracy and clinical interventions for musculoskeletal disorders. Notable contributions include advancements in open MRI applications for impingement assessment and high tibial osteotomy outcomes. Grants and Advising: Supervised numerous graduate students and postdoctoral fellows, including alumni recognized for their contributions (e.g., Marianne Black’s Top 30 under 30 award). Current students include Maryam Mohtajeb, focusing on hip mechanics using open MRI. Labs/Teams: Director of the Wilson Mechanics Lab; collaborates with the Canada Light Source synchrotron and VCHRI. Active in the School of Biomedical Engineering’s research core, focusing on Human Interfacing Devices.

Sossena Woodمشاهده پروفایل

استادیار

• Biomedical Engineering
• Neuroimaging
• Sickle Cell Disease
• +۴ مورد دیگر

Dr. Sossena Wood is an Assistant Professor in Biomedical Engineering at Carnegie Mellon University, with courtesy appointments in Electrical and Computer Engineering and the Neuroscience Institute. She holds a B.S. in Electrical Engineering (2011) and a Ph.D. in Bioengineering (2018) from the University of Pittsburgh, followed by a Presidential Postdoctoral Fellowship at CMU under Dr. Jana Kainerstorfer. Her research focuses on multi-modal, noninvasive imaging techniques to study cerebrovascular diseases like sickle cell disease (SCD), emphasizing neural mechanisms and healthcare equity. Key research areas include hemodynamic imaging (MRI, NIRS, EEG), pain and cognitive deficits in SCD, and inclusive neurotechnology to address biases in medical devices. She leads the Wood Neuro Research Group, collaborating with the CMU-Pitt BRIDGE Center and institutions globally. Awards include the American Society of Hematology Abstract Achievement Award (2020) and recognition as a Women of Color Technology Rising Star (2021). Her lab develops cutting-edge tools like anthropomorphic phantoms and MRI coils, and addresses algorithmic bias in devices like pulse oximeters. Educational contributions include teaching "Fundamentals of MRI and Neuroimaging Analysis" and outreach programs to engage underrepresented youth in STEM. Current collaborations focus on SCD mechanisms, pain biomarkers, and imaging equity. Lab equipment includes 3T MRI systems, EEG setups, thermal stimulators, and advanced NIRS devices. She advises multiple graduate and undergraduate students, with recent alumni including Yuxuan Gu (M.S.) and Alexis Duong (B.S./M.S.).

Lu Luمشاهده پروفایل

استادیار

• Astroparticle Physics
• Multi-messenger Astronomy
• Neutrino Astrophysics
• +۳ مورد دیگر

Dr. Lu Lu is an Assistant Professor in the Department of Physics at the University of Wisconsin-Madison, affiliated with the Wisconsin IceCube Particle Astrophysics Center (WIPAC). Her research focuses on astroparticle physics and multi-messenger astronomy, particularly neutrino astronomy and cosmic ray studies. She leads a research group working on topics such as diffuse neutrino analysis, real-time multi-messenger observations, and sensor design for the future IceCube-Gen2 telescope. Lu holds roles including Physical Sciences Group Representative at the US Scientific Committee on Antarctic Research (US SCAR). Her research interests encompass the origins of high-energy particles, neutrino detection techniques, and the application of spatial computing for science outreach. She has received notable awards, including the IUPAP Early Career Award (2023) and the IceCube Impact Award (2019) for contributions to neutrino astronomy and AR projects. Education & Experience: PhD from University of Leeds and University of Wuppertal, postdoctoral work at Chiba University and IceCube collaboration. Teaching: Courses include Physics 107, 307, and 406, covering modern physics, intermediate laboratory techniques, and particle astrophysics. Grants & Projects: Active in IceCube collaborations, diffuse neutrino analysis, and hardware development for Gen2 sensors. Lu's group includes graduate, undergraduate, and high school students working on neutrino searches, machine learning applications, and hardware innovation. She also engages in outreach through augmented reality projects and real-time data visualization platforms.

Yue Caoمشاهده پروفایل

استاد

• Radiation Oncology
• Medical Imaging
• Artificial Intelligence in Healthcare
• +۵ مورد دیگر

Yue Cao, Ph.D., is a Professor in the Department of Radiation Oncology BME at the University of Michigan. His work focuses on integrating advanced imaging techniques with radiation therapy to improve cancer treatment outcomes. He leads research in MRI-guided radiotherapy, adaptive therapy optimization, and AI-driven predictive modeling for personalized oncology. Key research areas include real-time motion tracking via MRI, biomarker-driven response assessment, and multi-modal imaging fusion. His clinical trials investigate de-escalation strategies using PET/MRI biomarkers in HPV-related oropharyngeal cancers and hepatocellular carcinoma. He has pioneered techniques like sparse sampling MRI for volumetric tracking and neural network-based image reconstruction. Publications emphasize innovations in radiotherapy planning quality assurance, survival prediction using longitudinal imaging, and overcoming interobserver variability in tumor segmentation. His team collaborates on large-scale datasets for brain metastasis detection (e.g., nnU-Net extensions) and liver function assessment through dynamic contrast-enhanced MRI. Current efforts include developing AI-enhanced knowledge-based systems for NRG Oncology trials and exploring synergies between 90Y SIRT and SBRT therapies. His work bridges cutting-edge imaging science with clinical implementation to reduce treatment toxicity while maximizing tumor control.

Ross Daniel Venookمشاهده پروفایل

مدرس ارشد

• Biomedical Engineering
• Medical Device Innovation
• MRI Safety
• +۴ مورد دیگر

Ross Daniel Venook is a Senior Lecturer in the Department of Bioengineering at Stanford University and serves as Associate Director for Engineering at the Stanford Byers Center for Biodesign. He specializes in biomedical engineering education and medical device innovation, co-leading undergraduate laboratory courses including BIOE123 (instrumentation) and BIOE141A/B (capstone design). His teaching focuses on prototyping, systems engineering, and hands-on workshops for medical device development. Ross holds a BS, MS, and PhD in Electrical Engineering from Stanford University, with doctoral research centered on MRI hardware development for interventional applications. His career includes a Biodesign Innovation Fellowship and 15 years at Boston Scientific Neuromodulation, where he led MRI safety initiatives to ensure safe MRI access for patients with implanted devices. He contributed to international standards development through collaboration with the MRI safety community. His research interests emphasize translational engineering solutions for healthcare challenges, particularly in medical device safety and innovation education. Ross actively promotes collaborative, problem-solving environments for students and professionals alike.