جست‌وجوی استاد و پژوهشگر

Alexandra Golbyمشاهده پروفایل

استاد

• Neurosurgery
• Medical Imaging
• Image-guided Surgery
• +۴ مورد دیگر

Alexandra Golby, MD is a Professor of Neurosurgery and Radiology at Harvard Medical School, and Haley Distinguished Chair in the Neurosciences at Brigham and Women's Hospital. She directs the Golby Lab, a surgical brain mapping laboratory focused on advanced imaging technologies for neurosurgical applications. Her clinical expertise centers on brain tumor and epilepsy surgery, with a focus on lesions near critical brain structures. Dr. Golby holds multiple leadership roles including Director of Image-guided Neurosurgery and Co-Director of AMIGO at Brigham and Women's Hospital. Her research integrates disciplines such as computer science, applied mathematics, and biomedical engineering to improve surgical planning and intraoperative decision-making. Notable innovations include technologies for real-time tumor resection monitoring and low-cost neuronavigation systems (e.g., NousNav) for low-resource settings. Dr. Golby completed her BA at Yale University and MD at Stanford University School of Medicine, followed by neurosurgery residency at Brigham and Women's Hospital. Dr. Golby's translational work emphasizes global health equity, including Fulbright-supported initiatives to develop locally adapted medical technologies in Rwanda and Morocco. Her research spans image-guided neurosurgery, brain-computer interface applications, and neuro-oncology advancements.

Gareth Barkerمشاهده پروفایل

استاد

• Magnetic Resonance Imaging (MRI)
• Neuroscience
• Psychiatry
• +۳ مورد دیگر

Professor Gareth Barker is a Professor of Magnetic Resonance Physics at King’s College London, affiliated with the Department of Neuroimaging within the Institute of Psychiatry, Psychology & Neuroscience (IoPPN). He leads research in quantitative MRI techniques, including T1/T2 mapping, diffusion tensor imaging, and magnetisation transfer measurements, with applications to neurological and psychiatric disorders. His work focuses on developing MRI protocols for patient populations and advancing near-silent acquisition methods. He chairs doctoral student progress committees and contributes to research ethics governance. Education: PhD in Physics from the University of Nottingham (1986), BSc in Engineering (1982). Career milestones include roles at the University of Florida (1986-1988) and University College London (1988-2002) before joining King’s College London in 2002. He co-leads the Wellcome EPSRC Centre for Medical Engineering, focusing on medical imaging innovation. Research Interests: Neuroscience applications of MRI, neuroimaging technology development, psychiatric disorder diagnostics, and translational neuroimaging. Key projects include the IMAGEN study (adolescent brain development), c-VEDA cohort (child vulnerability), and STRATA schizophrenia research. Scientific Contributions: Over 600 peer-reviewed publications, including seminal work on MRI technique optimisation (e.g., 2006 Brain and Cognition paper on study design). Awards include Fellowship in the International Society for Magnetic Resonance in Medicine. Grants/Teams: Principal Investigator on grants for accelerated MRI in Alzheimer’s, silent MRI for MS, and deep learning in imaging. Collaborates with institutions globally on neuroimaging initiatives.

Matthew Dimmockمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Radiation Physics
• Diagnostic Imaging
• +۳ مورد دیگر

Matthew Dimmock is a Senior Lecturer in Radiography (Diagnostic Imaging) at Keele University's School of Allied Health Professions and Pharmacy. He holds a PhD in Nuclear Physics from the University of Liverpool (2008), with postdoctoral research at Monash Centre for Synchrotron Science and the Australian Synchrotron. His research focuses on X-ray phase contrast imaging for low-dose cancer detection, forensic imaging analytics, and novel diagnostic techniques. He has held academic positions at Monash University (2013-2022) and currently leads projects at Keele. Key collaborations include work with Monash Biomedical Imaging, the Victorian Institute of Forensic Medicine, and ANSTO Australian Synchrotron. He has secured over £2.8M in grants, including NHMRC Synergy Grants and Leidos Collaboration funding. His publications span medical physics, imaging technology, and educational innovations in radiography. Education: PhD in Nuclear Physics (University of Liverpool, 2008). Postdoctoral appointments: Monash Centre for Synchrotron Science (2008-2012), Australian Synchrotron (2012-2013). Research interests include radiation detector design, phase-contrast imaging for cancer diagnosis, forensic imaging applications, and medical education through virtual reality simulations. He has contributed to advancements in breast cancer imaging, proton therapy planning, and pediatric radiation safety. His work emphasizes low-dose imaging techniques and data-driven approaches in clinical and forensic contexts. Grants and collaborations highlight his role in interdisciplinary projects: Co-applicant for a £2.7M NHMRC Synergy Grant (2022), Co-Investigator in forensic machine learning initiatives (2022), and Principal Investigator for X-ray phase contrast studies (2016). His educational innovations include haptic VR simulations for radiography training and virtual patient clinics. Labs/Teams: Active in the Keele School of Allied Health Professions, collaborating with Australian and international institutions on synchrotron-based imaging and forensic science. His research group focuses on translating cutting-edge imaging technologies into clinical practice.

• Artificial Intelligence
• Medical Imaging
• Healthcare Data Analysis
• +۵ مورد دیگر

Dr. Maria Luisa Davila Garcia is a Lecturer in Artificial Intelligence at the School of Computing and Digital Technologies, Sheffield Hallam University, and affiliated with the Industry and Innovation Research Institute. She holds a PhD, MSc in AI, and BSc. Her work focuses on applying AI to healthcare, particularly in medical imaging and data analysis. Collaborations include projects with NHS departments, Innovate UK, and the Medical Research Council (MRC). Education: PhD, MSc Artificial Intelligence, BSc Research interests span AI-driven solutions for personalized healthcare, early disease diagnosis (e.g., papilloedema, skeletal dysplasias), and tackling health inequalities through data analysis. She develops algorithms for image processing in reproductive medicine, ophthalmology, and pediatric radiology. Publications highlight contributions to sperm cell classification, morphological analysis, and skeletal dysplasia detection via machine learning. Collaborations emphasize translating AI techniques into clinical applications. Grants/Projects: Innovate UK-supported work on optic nerve analysis, NHS collaborations for live-cell imaging. Her involvement in the Industry and Innovation Research Institute underscores a focus on bridging academic research with industrial and healthcare innovation.

Joseph Schmidtمشاهده پروفایل

دانشیار

• Attention-Memory Interaction
• Visual Cognition
• Eye Tracking
• +۳ مورد دیگر

Joseph Schmidt is an Associate Professor of Psychology at the University of Central Florida, where he directs the Attention and Memory Laboratory. His research integrates eye-tracking and EEG/ERP methodologies to study attention-memory interactions. Key focus areas include: Neural and behavioral correlates of visual working memory Attention guidance in real-world contexts Visual search errors in medical imaging Oculomotor patterns in neurological disorders His work bridges cognitive psychology and neuroscience, examining how memory representations shape attentional selection. Current projects investigate cognitive expectations in visual search, EEG classification methods, and attentional deficits in clinical populations.

• Radiology
• Oncologic Imaging
• Medical Imaging
• +۳ مورد دیگر

Prof. Emanuele Neri is an Associate Professor of Radiology at the Department of Translational Research, University of Pisa , where he chairs the 1st Academic Unit of Radiology at Pisa University Hospital. He also leads the Post Graduate School of Radiology and the Master in Oncologic Imaging programs at the University of Pisa. His work focuses on advancing imaging techniques in oncology, geriatric radiology, and the integration of artificial intelligence (AI) into clinical practice. Neri is actively involved in European radiology societies, contributing to guidelines and recommendations for AI implementation and standardized reporting in oncologic imaging. His research interests span radiomics, MRI/CT advancements, and AI-driven diagnostics. He has authored numerous systematic reviews and meta-analyses addressing topics like pancreatitis-associated myocarditis, liver imaging with LI-RADS, and PSMA-targeted PET imaging for brain metastases. Neri emphasizes the ethical and practical challenges of AI adoption in radiology, advocating for patient-centered approaches and rigorous validation of new technologies. Educations: Not explicitly listed in provided texts. Grants/Awards: None explicitly mentioned, though his prolific publication record suggests sustained research funding. Labs/Teams: Leads academic units in radiology and oncologic imaging at Pisa University Hospital.

• X-ray imaging and simulation
• Non-destructive testing by ionising radiation
• Inverse problems
• +۹ مورد دیگر

Dr. Franck Vidal is a Senior Lecturer and Acting Director of Research at the School of Computer Science & Electronic Engineering at Bangor University, where he is also a member of the Visualization and Medical Graphics (VMG) Group and the Research Institute of Visual Computing (RIVIC). His educational background includes: PhD in Computer Science from Bangor University (2008) Postgraduate Certificate in Higher Education (2016) Diplôme d'études approfondies Images et Systèmes from Institut National des Sciences Appliquées de Lyon (2003) Master of Science in Computer-Aided Graphical Technology Applications from Teesside University (2002) Dr. Vidal's research focuses on X-ray imaging and simulation, non-destructive testing by ionising radiation, inverse problems, optimisation and artificial evolution, high performance computing, computer vision, image analysis and machine learning, and image-based simulation. His work has significant applications in medical imaging and medical physics, including X-ray, CT, MRI, PET and radiotherapy. He applies evolutionary algorithms to solve complex inverse problems in medical imaging. His recent publications demonstrate a strong focus on GPU-accelerated X-ray simulation technologies, particularly through the gVirtualXRay library, which enables real-time simulation of X-ray images and CT volumes. His work bridges the gap between computational physics, medical imaging, and practical clinical applications, with significant contributions to PET reconstruction algorithms using the Fly algorithm. Among his notable achievements: 2nd place in the Eurographics 2009 Medical Prize for ImaGINe-S David Duce Prize for the Best Short Paper (2019) Best Poster Presentation award (2022) Development of the gVirtualXRay open source library Multiple publications on evolutionary algorithms for medical imaging Dr. Vidal has supervised multiple research students including Julien Lavauzelle and Adrien Dutertre from ENSTA ParisTech. He has secured funding for significant projects including Fly4PET (focused on PET reconstruction for radiotherapy) and RAMPVIS (visualization for pandemic response). He actively collaborates with institutions worldwide, including INRIA in France and the University of California, San Diego. He leads the development of several innovative projects including gVirtualXRay (a GPU-based X-ray imaging library), Fly4Arts (evolutionary art using the Fly algorithm), and RASimAs (a regional anaesthesia simulator and assistant), demonstrating his ability to translate theoretical research into practical applications across medical and non-medical domains.

Xiang Fangمشاهده پروفایل

دانشیار

• Supply Chain Management
• Healthcare Analytics
• Game Theory
• +۲ مورد دیگر

Xiang Fang serves as Associate Dean of Faculty and Associate Professor of Business Analytics at the University of Colorado Denver Business School. Her research spans supply chain management, healthcare analytics, and game theory, with publications in journals like Production and Operations Management and Decision Sciences . She received the Dean’s Research Fellowship (2017-2022) and William Nasgovitz Research Award (2016). Recent articles focus on logistics optimization, machine learning in radiology, and sustainable supply chains. Her work integrates quantitative methods across domains: 60% of recent publications apply game theory to supply chains, while 30% utilize deep learning for healthcare solutions. Key trends include drone routing algorithms and livestream e-commerce strategies. Awards: Outstanding Reviewer, Production and Operations Management (2018) Roger L. Fitzsimonds Scholarly Achievement Award (2013) Izzet Sahin Research Award (2010)

Giovanna Castellanoمشاهده پروفایل

دانشیار

• Computational Intelligence
• Computer Vision
• Deep Learning
• +۷ مورد دیگر

Giovanna Castellano is a Full Professor at the University of Bari Aldo Moro, Italy, where she serves in the Department of Computer Science. She coordinates the Computational Intelligence Laboratory (CILab) and has been active in academia since at least May 2015. Her extensive publication record with 354 publications demonstrates her significant contributions to the fields of Computational Intelligence, Computer Vision, and Explainable AI. Dr. Castellano earned her PhD in Computer Science from the University of Bari Aldo Moro between September 1988 and March 1993. Her academic journey has led her to become a prominent researcher in artificial intelligence applications across various domains including healthcare, education, and digital art analysis. Her research interests span a wide range of computational intelligence topics, with current focus on Computer Vision, Deep Learning, Explainable AI (XAI), and Fuzzy Systems. Dr. Castellano's work demonstrates a strong interdisciplinary approach, applying AI techniques to solve real-world problems in healthcare diagnostics, precision agriculture, educational analytics, and cultural heritage preservation. Her research consistently emphasizes model interpretability, recognizing the importance of transparent AI systems, particularly in critical domains like healthcare where trust in AI decisions is paramount. Analysis of her recent publications reveals a consistent trajectory toward more interpretable and explainable AI systems across multiple application domains. Her work spans medical imaging analysis, stress prediction in educational contexts, art analysis, and precision agriculture. A notable trend is the integration of large language models with computer vision techniques to enhance both performance and explainability of AI systems, particularly in medical and artistic contexts. Excellent Presentation Award at 1st International Conference on Soft Computing and Intelligent Systems 2002 Dr. Castellano leads the Computational Intelligence Laboratory (CILab) at the University of Bari, where she supervises research projects and collaborates with numerous researchers including Gabriella Casalino, Gennaro Vessio, and Gianluca Zaza. Her research group has secured various grants for projects in healthcare AI, educational data mining, and digital art analysis. The CILab research team works on diverse projects including medical diagnostics using AI, analysis of digital art collections, educational data mining, and precision agriculture applications. Their work often involves collaboration with healthcare institutions, cultural organizations, and agricultural technology companies, demonstrating the practical impact of their theoretical research.

• Biomaterials
• Biotribology
• Cell and Tissue Engineering
• +۴ مورد دیگر

Dr. Mansour Youseffi is a Senior Lecturer (Reader) in Biomaterials at the University of Bradford's School of Engineering, part of the Faculty of Engineering and Digital Technologies. He leads modules in Materials Science and Engineering, Rehabilitation and Prosthetics, and other biomedical-focused courses. His research spans biomaterials, biotribology, cell and tissue engineering, and medical device development, with a focus on applications in orthopedics, cancer treatment, and regenerative medicine. Since 1992, he has secured over £740k in research funds, supervised 25 PhD graduates, and authored 25 books, 5 patents, and 250+ publications. His work includes advancements in engineered biomaterials for cancer immunotherapy, medical imaging techniques, and smart prosthetics. He currently advises 3 PhD students and collaborates with interdisciplinary teams in Advanced Materials Engineering and Medical Engineering research groups. Key research topics include biocompatible materials for implants, AI-driven medical imaging analysis, and biomaterials for tissue engineering. His contributions address global health challenges, including cancer diagnostics and disparities in healthcare access across regions like the UK and Saudi Arabia.

Santiago Romaníمشاهده پروفایل

دانشیار

• Computer Vision
• Pattern Recognition
• Machine Learning
• +۳ مورد دیگر

Santiago Romaní is an Associate Professor of Computer Engineering at the University Rovira i Virgili (URV), Tarragona, Spain. He holds an MSc in Computer Science from the Technical University of Catalonia (UPC, 1991) and a PhD in Computer Sciences from UPC (2006). He leads the RIVI-ITAKA research group and is affiliated with the Department of Computer Engineering and Mathematics at URV's Sescelades Campus. Education: MSc in Computer Science, Technical University of Catalonia (1991) PhD in Computer Sciences, Technical University of Catalonia (2006) Research Focus: Specializes in Computer Vision, Pattern Recognition, Machine/Deep Learning, and Medical Image Analysis. His work integrates artificial intelligence for automated image recognition and medical diagnostics. Notable projects include developing an automatic system for radiological measures in nuclear facilities (2006–2010) and founding the URV spin-off company FLUX (2011–2014). Professional History: Technical development lead for ENRESA collaboration (2006–2010) Co-founded FLUX (2011) as a URV spin-off (2012–2014) Joined the Intelligent Robotics and Computer Vision (IRCV) group (2016) Current Activities: Focused on teaching, with prior contributions to two PhD projects in medical image analysis and AI.

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

Live Eikenes is a Professor at the Norwegian University of Science and Technology (NTNU) in the Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences. Her research focuses on neuroimaging techniques, traumatic brain injury (TBI), brain tumor classification using AI, and white matter microstructure analysis in both clinical and developmental contexts. She leads studies integrating PET/MRI, diffusion tensor imaging (DTI), and radiomics to advance diagnostics and prognosis in neuro-oncology and neurological disorders. Her work spans from preterm birth effects on brain development to the clinical impact of TBI, including studies on mild TBI outcomes and biomarker validation. She collaborates on initiatives like the HUNT study, investigating population-based imaging correlates of neurological conditions. Her contributions include developing AI models for tumor classification and improving PET/MRI accuracy through deep learning-based corrections. Key research areas include neuro-oncology (e.g., glioma diagnosis), traumatic injury outcomes, and the application of advanced imaging in clinical settings. Her interdisciplinary approach bridges radiology, neuroscience, and biomedical engineering.

• Medical Imaging
• Image Reconstruction
• Computed Tomography (CT)
• +۵ مورد دیگر

Jerome Liang is a distinguished Professor at Stony Brook University, holding academic positions across multiple departments including Radiology, Biomedical Engineering, Electrical and Computer Engineering, and Computer Science within the Renaissance School of Medicine. His research focuses on advanced imaging technologies, particularly in low-dose CT reconstruction, virtual colonoscopy, and AI-driven diagnostic tools. Education: PhD in Physics (City University of New York), Postdoc at Duke University He leads the Laboratory for Imaging Research and Informatics (IRIS), pioneering innovations like virtual colonoscopy systems and radio-genomics research. Notable awards include IEEE Fellow and SUNY Chancellor's Entrepreneur Award for commercializing medical imaging solutions. Recent work emphasizes AI applications in radiology and spectral CT analysis, with over 130+ peer-reviewed articles. His grants include NIH-funded projects on virtual colonoscopy and colorectal polyp research. Labs/Teams: IRIS Lab (Imaging Research and Informatics), collaborations with BNL and Cold Spring Harbor Lab.

Thomas Fevensمشاهده پروفایل

استاد

• Deep Learning
• Medical Image Processing
• Computer-Aided Medical Diagnosis
• +۵ مورد دیگر

Thomas G. Fevens is a Professor in the Department of Computer Science and Software Engineering at Concordia University, and an Adjunct Professor in the Department of Surgery at McGill University. He holds a PhD in Computing and Information Science from Queen's University (1999), with prior degrees in Astrophysics and Physics. His research focuses on medical imaging applications of deep learning, computational geometry, and surgical software innovation. Notable work includes computer-aided diagnosis systems for breast cancer using cytological images, 3D surgical planning algorithms, and motion tracking frameworks for ACL injury assessment. Education: PhD in Computing and Information Science, Queen's University (1999) MSc in Computing and Information Science, Queen's University (1994) MSc in Physics (General Relativity), Queen's University (1993) BSc in Astrophysics (Honours), Queen's University (1990) Research emphasizes interdisciplinary applications of AI in healthcare, including: Medical image segmentation using U-Net architectures Deep learning for tumor localization Kinect-based motion analysis systems Generative adversarial networks (GANs) for medical imaging Publications demonstrate contributions to both core computer science (mobile ad hoc networks, computational geometry) and clinical applications (surgical planning, cancer diagnosis). Active in industry collaboration through the ML-MVP lab, focusing on translating AI research into clinical tools.

Hadjiefthymiades Stathesمشاهده پروفایل

استاد

• IoT Security
• Cloud Computing
• Machine Learning
• +۷ مورد دیگر

Hadjiefthymiades Stathes is a Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His primary research division focuses on Computer Systems and Applications. He specializes in IoT security, cloud computing, machine learning, and edge computing, with notable contributions to data analytics, trust management, and cybersecurity in distributed systems. Research Interests: His work spans IoT security protocols, optimization of cloud storage systems, anomaly detection in Kubernetes environments, and predictive analytics for sensor networks. He has explored trust models in pervasive computing, energy-efficient node authentication, and hardware-accelerated mixed reality interfaces. His recent projects include frameworks for Earth observation data accessibility and next-generation internet experimentation platforms. Publications: His recent articles highlight advancements in IoT data reduction techniques, trust sustainability in infrastructure, and the application of Markov chains for multivariate event correlation. He has also contributed to the design of experiment description languages for mobile IoT and emergency response platforms for radiological incidents. Awards & Grants: No scientific awards or grants were explicitly mentioned in the provided texts. Labs & Teams: His research involves collaborations on projects like EO4EU-AI (Earth observation with extended reality interfaces) and TRACE (logistics integration systems). He is affiliated with the university's advanced systems research groups focusing on edge computing and secure IoT ecosystems.