جست‌وجوی استاد و پژوهشگر

Noam Ben-Eliezerمشاهده پروفایل

استاد

• Quantitative MRI
• Microstructural MRI
• Neuroimaging
• +۶ مورد دیگر

Professor Noam Ben-Eliezer is a faculty member in the Department of Biomedical Engineering at The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University. He also holds an adjunct position as Assistant Professor of Radiology at the Center for Advanced Imaging Innovation and Research (CAI2R) and is affiliated with the Sagol School of Neuroscience. His research laboratory focuses on developing and applying advanced MRI techniques for biomedical applications. Prof. Ben-Eliezer's work resides at the interface between developing novel methodologies for non-invasive quantitative MRI and their application for investigating tissue structure and function. His research spans multiple areas including: Design of new quantitative and microstructural MRI protocols Deep learning networks for clinical data processing MRI image denoising techniques Accelerated acquisition schemes Customized signal processing algorithms Presurgical EEG analysis in epilepsy patients His laboratory is particularly focused on characterizing the myeloarchitecture of the central nervous system to understand neurodegenerative demyelinating diseases, especially multiple sclerosis. Applications extend to Multiple Sclerosis, personalized cancer treatment, Schizophrenia, Insomnia, Psychedelics in medicine, Muscular Dystrophy, and Neuromuscular diseases. Prof. Ben-Eliezer's recent publications demonstrate a strong trend toward developing more accurate, faster MRI techniques with increasing incorporation of machine learning approaches. Much of his work centers on quantitative T2 mapping across various clinical conditions, particularly in neurology and orthopedics. The Lab for Advanced MRI maintains multiple research projects including myelin quantification, T2 mapping, acceleration techniques, multiple sclerosis pathophysiology, adipose tissue characterization, and deep learning for image segmentation. The lab has developed several software tools available for download, including the EMC platform for quantitative MRI analysis, and continues to push MRI technology boundaries for scientific and clinical applications.

Daniel Elsonمشاهده پروفایل

استاد

• Biomedical Engineering
• Surgical Imaging
• Optical Physics
• +۵ مورد دیگر

Professor Daniel Elson is a leading academic in Imperial College London's Faculty of Medicine , affiliated with the Hamlyn Centre for Robotic Surgery and the Department of Surgery and Cancer . As a Professor of Surgical Imaging and Biophotonics , his research focuses on advancing photonics technology for medical imaging applications in surgery. Research Interests: Multispectral Imaging for intraoperative diagnostics Fluorescence Spectroscopy in cancer surgery Polarization-Resolved Endoscopy for cancer detection Structured Lighting in endoscopic systems Robotic Guidance of optical probes Diffuse Reflectance Spectroscopy for margin assessment Research Trends & Article Analysis: The 15 most recent publications demonstrate a strong focus on real-time surgical diagnostics using machine learning and photonics . Key areas include colorectal cancer margin assessment, brain imaging with polarimetric techniques , and gastrointestinal cancer detection via diffuse reflectance spectroscopy . Current PhD Students: Zepeng Hu Kaizhong Deng Connor Daly Adam Tlemsani Research Group: Elson leads the Surgical Imaging and Biophotonics Group , which develops cutting-edge imaging systems for minimally invasive and robotic-assisted surgery . The group operates from the Bessemer Building (Room 415) at Imperial College's South Kensington Campus .

• Computational Biomechanics
• Biomedical Engineering
• Impact Injury Analysis
• +۴ مورد دیگر

Adam Wittek is a Professor and Deputy Director of the Intelligent Systems for Medicine Laboratory at the Department of Mechanical Engineering, University of Western Australia. He also serves as Mechanical Engineering Programme Chair. His research focuses on computational biomechanics, biomedical engineering, and impact injury analysis, with notable contributions to surgical simulation, abdominal aortic aneurysm (AAA) modeling, and computational grid optimization. He has held roles at Toyota Central R&D Labs, Japan Automobile Research Institute, and ESI Group prior to his academic appointment in 2004. Wittek’s education includes a PhD in Engineering (Chalmers University of Technology, 2000) and an MSc in Applied Mechanics (Warsaw University of Technology, 1992). His work aligns with UN Sustainable Development Goals, particularly in advancing medical technologies and safety innovations. His research interests span finite element analysis, meshless methods, and medical image computing. Recent articles highlight advancements in AAA kinematics, brain shift correction, and computational fluid dynamics in biomedical contexts. He has secured grants totaling over $8M, including ARC and NHMRC funding for projects like 'Brain-skull interface biomechanics.' Recipient of Sir George Julius Medal (2012) and SAE Ralph H. Isbrandt Automotive Safety Award (2004) Editorial roles at Modelling and Australian Journal of Mechanical Engineering Supervised 12 doctoral projects, with active collaborations across 10+ countries Wittek leads the Computational Biophysics for Medicine in 3D Slicer project, advancing open-source tools for clinical applications. His lab develops patient-specific models for neurosurgery and cardiovascular applications.

Ali Sadeghi-Nainiمشاهده پروفایل

دانشیار

• Quantitative Imaging
• Medical Biophysics
• Machine Learning
• +۵ مورد دیگر

Ali Sadeghi-Naini is an Associate Professor and York Research Chair at York University's Department of Electrical Engineering & Computer Science within the Lassonde School of Engineering. He holds cross-appointments at the University of Toronto (Department of Medical Biophysics), Sunnybrook Research Institute, and Odette Cancer Centre. His research focuses on quantitative imaging and biomarker technologies integrated with AI for precision medicine. He earned his PhD in Biomedical Engineering from the University of Western Ontario and completed postdoctoral fellowships supported by CIHR and the Canadian Breast Cancer Foundation. Education: PhD in Biomedical Engineering, University of Western Ontario (2011) MSc in Artificial Intelligence, Tehran Polytechnic University (2006) Research Interests: Dr. Sadeghi-Naini develops AI-driven quantitative imaging tools for cancer therapy monitoring. Key areas include: Ultrasound and MRI-based biomarkers for tumor response prediction Machine learning in digital pathology and radiomics Image-guided therapeutics for personalized oncology Scientific Awards: York Research Chair CIHR Banting Postdoctoral Fellowship Canadian Breast Cancer Foundation Postdoctoral Fellowship Advising & Grants: His work bridges basic science and clinical translation, notably pioneering ultrasound biomarkers for breast cancer chemotherapy response. Recent grants fund projects on AI in radiation oncology and metastasis prediction. Labs: Quantitative Imaging and Machine Intelligence in Medicine (QuantiMB) Lab at York University.

• Medical Image Computing
• Image-Guided Surgery
• Human-Computer Interaction
• +۹ مورد دیگر

Dr. Yiming Xiao is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University, holding the Research Chair in Intelligent & Intuitive Surgical Technology. His work focuses on medical image computing, image-guided surgery, and AI-driven surgical applications. He leads the HealthX Lab, advancing neurotechnology, explainable AI, and virtual/augmented reality systems for surgical planning. Research interests include medical image segmentation , deep learning , VR/AR in surgery , and collateral circulation analysis . Notable projects include PreVISE (VR SEEG planning), CABLD (brain landmark detection), and MedCLIP-SAM (text-driven medical segmentation). Publications emphasize few-shot learning , ultrasound-MRI co-registration , and neurological disorder diagnostics . He actively supervises PhD students in computer science, with a focus on interdisciplinary applications of AI in healthcare. His work bridges computational methods and clinical needs to improve surgical precision and patient outcomes.

Partha Mitraمشاهده پروفایل

استاد

• Neuroscience
• Computational Neuroscience
• Artificial Intelligence
• +۴ مورد دیگر

Partha Mitra is the Crick-Clay Professor of Biomathematics at Cold Spring Harbor Laboratory (CSHL) and holds a Distinguished Visiting Chair at IIT Madras. His research spans theoretical and experimental neuroscience, focusing on brain circuit mapping and AI integration. Neuroscience Quantitative Biology His theoretical work applies statistical physics to machine learning and control theory , while experimental efforts include the Mouse Brain Architecture Project and collaborations on marmoset brain mapping with RIKEN and Monash University. Recent projects involve 3D multimodal histological atlases and DHARANI , a developing human brain atlas. His NIH BRAIN CONNECTS grant (2023) aims to create comprehensive brain wiring diagrams. Scientific Awards NIH BRAIN CONNECTS (2023) NSF INSPIRE (2013) NIH Transformative R01 (2009) George S. Axelby Outstanding Paper (2013) American Physical Society Fellow (2003) Senior Member, IEEE (2005) H.N. Mahabala Chair (2015) Mitra’s lab has mentored numerous researchers, including Saiteja Abbu , Samik Banerjee , and Jaikishan Jayakumar . His work has produced over 200 publications and tools for brain circuit analysis and 3D imaging .

Kendra Davidsonمشاهده پروفایل

استاد

• Medical Image Analysis
• Non-Invasive Diagnostic Methods
• Computational Neuroscience
• +۳ مورد دیگر

Kendra Davidson is a Professor at the Department of Electrical and Computer Engineering at Vanderbilt University School of Engineering. She specializes in non-invasive medical diagnostic methods and medical image analysis, with a focus on white matter imaging, segmentation techniques, and multi-atlas approaches. As a Chancellor Faculty Fellow, she leads the Medical-image Analysis and Statistical Interpretation Lab (MASI Lab) and collaborates across biomedical engineering and neuroscience domains. Education: B.S. in Computer and Electrical Engineering and Applied Math from Lipscomb University Affiliation: Vanderbilt School of Engineering, VISE Affiliate with Bennett Landman Her research integrates advanced computational methods with clinical imaging, spanning fMRI , diffusion tensor imaging , and multi-contrast MRI . Recent publications highlight innovations in vasculature-informed smoothing, 4D diffusion atlases, and scalable processing frameworks for large-scale datasets. Notable awards include the Chancellor Faculty Fellow title. Her work addresses challenges in traumatic brain injury, optic nerve analysis, and lung CT harmonization, with a strong emphasis on reproducibility and cross-validation studies.

Yuankai Huoمشاهده پروفایل

استادیار

• Medical image analysis
• Machine learning

Yuankai Huo is an Assistant Professor in Computer Science at Vanderbilt University, where he leads the biomedical data Representation and Learning laB (HRLB). His research bridges medical image analysis and machine learning, focusing on computational methods for healthcare innovation. Education: Ph.D. in Electrical Engineering, Vanderbilt University (2018) M.S. in Computer Science, Columbia University (2013–2014) Research Focus: Dr. Huo develops AI-driven solutions for medical challenges, including digital pathology segmentation, 3D medical imaging reconstruction, and neuroimaging analysis. His work emphasizes scalable deep learning architectures and cross-domain adaptation (e.g., histopathology to neuroimaging). Publication Trends: Recent articles (2023–2025) demonstrate advancements in: (1) Whole-slide image segmentation for renal and gastrointestinal pathology, (2) Foundation models for multi-modal medical data, (3) Optimization of 3D imaging pipelines, and (4) AI-powered diagnostic benchmarking. Cross-cutting themes include self-supervised learning and computational efficiency. Engagement: Dr. Huo participates in NIH workshops (e.g., grant writing in 2022, short talks in 2023) and organizes seminars through HRLB.

Jack H. Nobleمشاهده پروفایل

استادیار

• Medical Image Processing
• Image Segmentation
• Computer-Aided Surgery
• +۷ مورد دیگر

Jack H. Noble is an Assistant Professor at Vanderbilt University, holding appointments in the departments of Electrical and Computer Engineering, Computer Science, Biomedical Engineering, and Head and Neck Surgery. He directs the Biomedical Image Analysis for Image-Guided Interventions Laboratory (BAGL) and co-chairs the VISE Symposium Series. His research focuses on medical image processing, particularly in computer-aided surgery and cochlear implant optimization. Assistant Professor, Electrical and Computer Engineering (Primary) Assistant Professor, Computer Science Assistant Professor, Biomedical Engineering Assistant Professor, Head and Neck Surgery Director of Graduate Student Recruitment, Vanderbilt School of Engineering Education: Ph.D., Electrical Engineering, Vanderbilt University (2011) M.S., Electrical Engineering, Vanderbilt University (2008) B.E., Electrical Engineering, Vanderbilt University (2007) Research Interests: Dr. Noble specializes in medical image analysis for surgical applications, combining deep learning, statistical shape models, graph search algorithms, and augmented reality to enhance precision in procedures like cochlear implantation. His work bridges engineering and clinical practice, emphasizing patient-specific modeling and real-time intra-operative guidance. Deep learning for medical imaging Statistical shape modeling of ear anatomy Augmented reality in surgery Image-guided cochlear implant programming Neural activation simulation Micro-CT to CT translation Article Trends: His publications (2011–2023) highlight advancements in automated segmentation, deep learning architectures (e.g., Cycle GANs, cGANs), and augmented reality systems. Key themes include cochlear implant optimization, auditory nerve health estimation, and surgical precision through image analysis. Advising & Grants: Dr. Noble mentors graduate students and postdocs, with alumni contributing to fields like medical imaging and surgical robotics. His work is funded by NIH grants R01DC014037 and R01DC022099, focusing on patient-specific cochlear implant models and augmented reality-guided surgery.

Nathan Cahillمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Imaging Analysis
• +۳ مورد دیگر

Nathan Cahill is an Associate Professor in the School of Mathematical Sciences and Associate Dean for Industrial Partnerships in the College of Science at Rochester Institute of Technology (RIT). He holds a DPhil in Engineering Science from the University of Oxford and is the Director of RIT's PhD Program in Mathematical Modeling. His research focuses on computer vision, machine learning, medical imaging analysis, and mathematical modeling. He directs the Image Computing and Analysis Laboratory (ICAL), which develops mathematical models for imaging analysis and computer vision tasks like registration, segmentation, and classification. Dr. Cahill has extensive industrial experience, having worked at Eastman Kodak and Carestream Health, earning 26 US patents in computer vision and medical imaging. He teaches advanced courses in numerical analysis, mathematical modeling, and imaging science. His work bridges academia and industry, with collaborations spanning biomedical engineering, neuroscience, and astrophysics. He advises numerous graduate and undergraduate students, many of whom have pursued PhDs or industry roles in tech and healthcare. His research outputs include influential papers in medical image registration, graph theory applications in social networks, and hyperspectral imaging. He actively contributes to open-source tools for image processing, including MATLAB implementations of segmentation and clustering algorithms. Cahill also holds an Erdős number of 3 and has been recognized for his teaching, offering unique incentives to engage students in applied mathematics and computer science. Key affiliations include the Center for Imaging Science and the PhD Program in Computing and Information Sciences. His current projects involve machine learning applications in healthcare analytics, pulsar signal analysis, and improving wastewater surveillance during pandemics. He maintains an active lab environment fostering interdisciplinary research with collaborators at institutions like the University of Rochester Medical Center and the Mind Research Network.

Benjamin Zwickمشاهده پروفایل

پژوهشگر ارشد

• Computational Biophysics
• Biomedical Engineering
• Medical Image Analysis
• +۵ مورد دیگر

Benjamin Zwick is a Research Fellow in the School of Engineering at The University of Western Australia (UWA), specializing in computational biomechanics and biomedical engineering. His research focuses on applying computational methods to address challenges in neurosurgery, epilepsy, brain cancer, and cardiovascular disease. He holds a BEng(Hons) and PhD from UWA, with industry experience in engineering design. His work emphasizes open science, developing tools like SlicerCBM (for brain biomechanical analysis) and MVox (medical image voxelization). Key projects include brain shift correction using physics-informed neural networks and computational models for tumor resection planning. He has secured grants such as the WA Near-miss Awards for epilepsy surgery planning and neurosurgical applications of machine learning. Zwick teaches courses in biomechanics, cardiovascular biomechanics, and numerical methods at UWA. His expertise spans computational fluid dynamics, finite element analysis, and high-performance computing. Collaborations include teams at Harvard, MIT, and Stanford, with software contributions to platforms like 3D Slicer and GitLab repositories.

• Radiomics
• Medical Imaging
• Artificial Intelligence in Radiology
• +۳ مورد دیگر

Bino Varghese, PhD, is an Associate Professor of Research in the Department of Radiology at the University of Southern California (USC), where he leads the USC Radiomics Laboratory. He holds a B.E. in Medical Electronics from Visveswariah Technological University (2002), a Master's in Biomedical Engineering from Wright State University (2005), and a PhD in Biomedical Sciences from Wright State University (2010). His doctoral research focused on CT-based bone-strength indicators for clinical applications. Dr. Varghese's research centers on multi-modal imaging biomarkers, radiomics, and AI-driven medical image analysis. His work spans: Quantitative imaging for oncology (renal, breast, soft tissue tumors) Neuroimaging harmonization and super-resolution techniques Machine learning for COVID-19 severity prediction and dementia risk assessment Technical validation of radiomic features across imaging platforms His recent publications demonstrate strong focus on AI applications in cancer imaging (renal cell carcinoma, sarcomas, breast lesions) and neurodegenerative diseases, with methodological emphasis on deep learning, feature standardization, and multi-scanner validation. Research consistently addresses clinical translation through phantom studies and biomarker development. Awards: USC's Mark and Mary Stevens Neuroimaging and Informatics Institute: Big Data to Knowledge Science Rotations for Advancing Discovery (RoAD-Trip) Award (2017) At the USC Radiomics Laboratory, Dr. Varghese develops computational methods to maximize diagnostic utilization of imaging data, with ongoing work in neuroimaging harmonization, radiomic feature robustness, and AI-based diagnostic tools across multiple disease domains.

Kwame Kuttenمشاهده پروفایل

مدرس ارشد

• Biomedical Imaging
• Computational Anatomy
• Neuroscience
• +۳ مورد دیگر

Kwame Kutten is a Senior Lecturer in the Department of Biomedical Engineering at Johns Hopkins University, affiliated with the Whiting School of Engineering. He supports quantitative education within the department and leads the Gateway Computing courses. His research focuses on biomedical imaging and computational methods to study brain structural changes in disease contexts. Education: PhD in Biomedical Engineering (Johns Hopkins University, 2017); BS in Biomedical Engineering (Rensselaer Polytechnic Institute, 2009). Research Interests: Image analysis, computational anatomy, neuroscience, and their applications in understanding neurological disorders. His work combines advanced imaging techniques with algorithmic approaches to study brain connectivity and morphology. Publications: Recent work includes advancements in cortical segmentation (2024), amygdalar-hippocampal shape analysis in bipolar disorder (2021), and diffusion tensor imaging visualization (2020). His research spans neuroimaging techniques, computational methods, and clinical applications. Teaching & Support: Provides critical quantitative course support and develops computing curriculum for engineering students.

Silvester Czannerمشاهده پروفایل

استاد

• Medical Image Analysis
• Computer Graphics
• Machine Learning in Medicine
• +۷ مورد دیگر

Prof. Silvester Czanner is a Professor and Head of the School of Computer and Engineering Sciences at the University of Chester. He holds a PhD in Mathematics from Comenius University (1998) and a Docent (Habilitation) in Applied Informatics from Slovak University of Technology (2024). With extensive global experience in academia and industry, he has led teaching and research programs, secured EU and international grants, and served on funding evaluation panels. His research focuses on computational methods for medical imaging, computer graphics, and AI-driven healthcare solutions. Education: PhD in Mathematics (1998, Comenius University), Docent in Applied Informatics (2024, Slovak University of Technology), and additional degrees in Mathematics, Computer Science, and International Relations. He has held roles at Harvard/MIT, Carnegie Mellon, and the University of Aizu, Japan. Research Interests: Medical Image Analysis (segmentation, registration), AI in Medicine (predictive modeling, deep learning), Virtual Reality for Medical Training, and interdisciplinary applications in surgery and diagnostics. His work bridges computational techniques and clinical practice, enhancing patient outcomes through innovative tools. Grants & Awards: Over 15 grants from EPSRC, EU H2020, and others. Recognitions include Senior Fellow HEA, Fellow RSA, and Senior Member ACM. He chairs the University Internal Quality System Assessment Board at Pan-European University and advises Slovak governmental bodies. Labs & Teams: Leads interdisciplinary teams in medical imaging, VR/AR applications, and computational biology. Collaborates with global institutions on projects like glaucoma detection algorithms and virtual paranoia environments.

• Medical Imaging
• Biomedical Engineering
• Machine Learning in Healthcare
• +۳ مورد دیگر

Yao Chen is an Associate Research Scientist in the Department of Genetics at Yale School of Medicine. Her research focuses on leveraging advanced imaging technologies and machine learning to address challenges in dermatology and nuclear medicine, particularly in the context of chronic graft-versus-host disease (cGVHD) and prostate cancer diagnostics. She develops innovative methods for skin lesion segmentation, motion correction in PET imaging, and biomechanical assessment of skin conditions. Her work integrates hyperspectral imaging, Raman spectroscopy, and deep learning to improve disease characterization and diagnostic accuracy. Key contributions include validating devices like the Myoton for cGVHD assessment and enhancing reproducibility in clinical measurements. Yao Chen collaborates across disciplines to translate biophotonics and imaging innovations into clinical practice, with a focus on improving patient care through data-driven solutions. Research interests span medical imaging, machine learning applications in healthcare, and translational biophotonics. Her recent studies emphasize AI-driven solutions for motion correction in PET scans and automated segmentation of skin lesions in cGVHD. These efforts aim to reduce human error and enhance diagnostic precision in complex medical scenarios. Labs/Teams: Yao Chen is part of the Genetics Department’s research group at Yale School of Medicine, collaborating with teams focused on biomedical imaging and clinical research. Her work contributes to interdisciplinary initiatives at the intersection of engineering, AI, and medicine.