جستجوی استاد راهنمای دانشگاهی

Saeed Sharifمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Innovative Tele-Health
• Medical Technology
• +۹ مورد دیگر

Dr Saeed Sharif is an Associate Professor in the Computer Science department at the School of Architecture, Computing and Engineering , University of East London . He leads the Intelligent Technologies Research Group and serves as Course Leader for MSc Computer Science programs. With a PhD in artificial intelligence from Brunel University London , his work bridges Artificial Intelligence , Medical Technology , and Smart Infrastructure . His research interests span Medical Image Analysis , Intelligent Diagnosis Systems , Big Data Analysis , and IoT Security . Collaborations with NHS Trusts and global institutions focus on improving healthcare systems through machine learning and biomedical signal processing. 2018-2022: £268,000 KTP project with Innovate UK Multiple Research Internship Schemes at UEL (2017-2021) European Research Centre collaboration (2015) As a technical committee member for conferences like IEEE CIT and a journal Guest Editor , he drives academic discourse. He has supervised numerous PhD students and served on examination panels, while maintaining 53+ publications in venues including IEEE Access , Applied Soft Computing , and Computer Methods and Programs in Biomedicine .

• Artificial Intelligence
• Internet of Things
• Medical Imaging
• +۳ مورد دیگر

Begoña García-Zapirain Soto is a researcher at the School of Engineering , University of Deusto , with a focus on applying artificial intelligence, machine learning, and IoT systems to healthcare challenges. Her work spans medical imaging, biomedical signal processing, and eHealth technologies , addressing aging populations, chronic disease management, and preventive care. University of Deusto, School of Engineering, Computing, Electronics and Communication Technologies Researcher role since 2000 with continuous publication activity Research Interests include: AI/ML for disease classification (e.g., AMD, ischemic heart disease, breast cancer) Smart healthcare systems (e.g., air quality monitoring, gluten detection) Geriatric technologies (e.g., frailty/sarcopenia assessment, loneliness monitoring) Medical imaging segmentation (CT, MRI, dermoscopy) Elderly exergames (FRED platform) Signal processing for clinical applications Article Trends show consistent work in AI healthcare applications (2011-2025), with recent emphasis on quantum machine learning (2023), serverless architectures (2023), and 3D printing (2023). Her publications cover medical imaging (8+ papers), pressure ulcer analysis (4+ papers), and serious games (7+ papers). Scientific Awards : Premio de Investigación UD-Grupo Santander 2007 Advising includes Oscar Javier Bastidas Jossa (2024), Zabit Hameed (2023), and Ornela Bardhi (2022). Key grants involve EU projects and EIP on AHA collaborations.

Lazar Kopanjaمشاهده پروفایل

دانشیار

• Computer Vision
• Image Processing
• Shape Analysis
• +۴ مورد دیگر

Lazar Kopanja serves as Associate Professor and Acting Dean of the Faculty of Information Technologies at Alfa BK University in Belgrade, Serbia, a position he has held since 2017. His interdisciplinary work bridges computer science and materials engineering with significant contributions to nanomaterials characterization. His educational foundation includes: Bachelor's degree in Computer Science from the Faculty of Science and Mathematics, University of Novi Sad Master's degree in Mathematics Teaching Methodology from the same institution Doctorate in Magnetic Properties of Nanoparticles from the University of Belgrade's Faculty of Technology and Metallurgy Dr. Kopanja's research centers on computational shape analysis and image processing , developing algorithms for quantifying nanoparticle morphology in TEM and confocal microscopy. His work has pioneered novel circularity and ellipticity measures for complex nanostructures, with applications spanning magnetic materials, biomedical imaging, and nanomedicine. The integration of computer geometry with materials science represents his signature interdisciplinary approach. Analysis of his 13 SCI-indexed publications reveals a consistent trajectory from foundational shape orientation algorithms (2005-2006) toward applied nanomaterials characterization (2016-2018), increasingly incorporating biological systems. His recent work demonstrates sophisticated fusion of image processing with magnetic property analysis in hematite and iron oxide nanostructures. As project manager for bilateral Serbia-Slovakia and Serbia-Belarus research initiatives, he has secured international funding for collaborative work in nanoparticle synthesis and characterization. His technical leadership extends to mentoring high school students for programming competitions and developing web applications through industry partnerships.

Murk Bottemaمشاهده پروفایل

دانشیار

• Medical Imaging Analysis
• Signal and Image Processing
• Mathematical Modelling of Biological Systems
• +۳ مورد دیگر

Murk Bottema is an Associate Professor at Flinders University, affiliated with the College of Science and Engineering, the Medical Device Research Institute, and the Torrens Resilience Initiative. He holds a BA in Physics from the University of Colorado and a PhD in Mathematics from Oregon State University. Research focuses on mathematical modelling of medical systems, medical image analysis, and biomedical engineering Teaching includes Methods of Applied Mathematics, Several Variable Calculus, and Mathematics 1A Professional memberships: Australian Mathematical Society, Society for Industrial and Applied Mathematics, American Mathematical Society Media expertise in medical imaging and signal processing His recent publications highlight applications of mathematical models in ophthalmology, particularly retinal detachment analysis via Optical Coherence Tomography (OCT). Research spans medical imaging, signal processing, and biomechanical modelling. Prof. Bottema serves as Chair of Flinders' University Scholarships Committee and maintains active collaborations in digital health and biomedical engineering domains.

Şükriye KARAمشاهده پروفایل

پژوهشگر

• Computer Vision
• Robotics
• Fuzzy Logic
• +۳ مورد دیگر

Şükriye KARA is a Research Assistant at the Department of Electrical and Electronics Engineering, College of Engineering and Architecture, Eskisehir Osmangazi University, Turkey. Her research focuses on Computer Vision and Robotics , with additional contributions to Fuzzy Logic and Signal Processing . She holds a Bachelor's (2006) and Master's degree (2008) from Selcuk University, and a second Master's (2014) from New York University. Email: karas@ogu.edu.tr Office: Room B-306, Electrical and Electronics Engineering Department Research Themes: Her recent work in Medical Imaging includes breast cancer diagnosis using convex hull algorithms (2021) and wavelet domain feature extraction for movement classification (2018). Earlier contributions (2008-2010) focus on mathematical decompositions in Fuzzy Topology , particularly continuity analysis in ideal topological spaces. Academic Affiliations: Active in the Control and Automation Systems Research Group at Eskisehir Osmangazi University, she collaborates on projects related to electrical engineering and robotics.

• Respiratory physiology
• Exercise intolerance
• Dyspnea mechanisms
• +۶ مورد دیگر

Dennis Jensen, PhD is an Associate Professor in the Department of Kinesiology and Physical Education within the Faculty of Education at McGill University and a Scientist with the Translational Research in Respiratory Diseases Program at the Research Institute of the McGill University Health Centre (RI-MUHC) . He additionally holds an appointment in the Department of Medicine, Division of Respiratory Medicine, MUHC . His research centres on understanding and alleviating activity-related breathlessness (dyspnea) and exercise intolerance in health, aging, obesity, and chronic cardiorespiratory disease—especially COPD. Research Interests Neuro-physiological mechanisms of dyspnea during exercise Cardiopulmonary exercise testing methodology and reference values Therapeutic interventions for exertional breathlessness (pharmacologic, sensorial, rehabilitative) COPD phenotyping via imaging, spirometry, and wearable sensors Sex- and age-related differences in ventilatory control and exercise tolerance Pulmonary vascular structure–function relationships and dysanapsis Dr Jensen directs the Clinical Exercise & Respiratory Physiology Laboratory (CERPL) , whose mission is to advance mechanistic understanding and evidence-based management of breathlessness in chronic pulmonary disease. The lab integrates whole-body cardiopulmonary testing, neuro-ventilatory measurements, advanced thoracic imaging, and digital health technologies to develop novel diagnostics and therapies. Recent Publications Trend Between 2024 and 2025, Dr Jensen has led or co-authored more than 30 peer-reviewed papers. These studies collectively chart a trajectory from mechanistic elucidation of dyspnea pathways (neural respiratory drive, pulmonary vascular dysfunction) to the creation of normative reference equations, deep-learning imaging tools, and pragmatic clinical interventions such as inhaled menthol and sensor-guided rehabilitation. The work spans bench-to-bedside translation and population-level epidemiology (CanCOLD, Canadian Respiratory Research Network). Scientific Awards No specific honours or awards are listed in the provided text. Supervision & Funding While explicit lists of trainees and grants are not supplied, Dr Jensen’s extensive publication record, leadership of CERPL, and involvement in large multicentre cohorts imply active graduate supervision and sustained competitive funding from national and international agencies. Laboratory & Collaborative Teams Dr Jensen heads the Clinical Exercise & Respiratory Physiology Laboratory (CERPL) at RI-MUHC. He collaborates widely with the CanCOLD Collaborative Research Group , the Canadian Respiratory Research Network , and interdisciplinary teams spanning kinesiology, respirology, radiology, biomedical engineering, and data science.

Magnus Borgaمشاهده پروفایل

استاد

• Medical Image Analysis
• Biomedical Imaging
• Quantitative MRI
• +۴ مورد دیگر

Magnus Borga is a Professor and Vice Dean at the Faculty of Engineering, Linköping University, affiliated with the Department of Medical Technology. His research focuses on Medical Image Analysis , particularly quantitative MRI for non-invasive biomarkers like fat infiltration in organs. He earned an MSc in Applied Physics and Electrical Engineering (1991) and a PhD in Computer Vision (1998), becoming a Professor in 2008. Research centers on automated methods for anatomical and functional imaging analysis, validated against invasive techniques. Key projects include Dixon MRI optimization, radiomics , and body composition profiling for metabolic disorders. Funding includes grants from the Swedish Research Council (VR), Vinnova, and LiU Cancer. Publications highlight trends in fat quantification across diseases, muscle composition in chronic pain, and AI-driven segmentation . Collaborations span the Dallas Heart Study , UK Biobank , and AMRA Medical AB , where he served as CTO. He teaches Neural Networks and Learning Systems at Linköping University.

• Medical Image Segmentation
• Domain Adaptation
• 3D MRI
• +۳ مورد دیگر

Asbjørn Marco Sinius Munk is a Research Fellow at the Department of Computer Science, University of Copenhagen , affiliated with the Pioneer AI (P1AI) research group. His work focuses on medical image analysis and domain adaptation in deep learning. Research Interests: Domain adaptation for medical imaging 3D MRI segmentation Foundation models in biomedical applications Self-supervised learning for brain imaging Deep learning frameworks for clinical data Recent research highlights include theoretical guarantees in domain adaptation (MDD-UNet) and alignment of 3D MRI with tabular data using CLIP-inspired models. His publications demonstrate strong applications of AI in healthcare imaging domains. Contact: asmu@di.ku.dk

Yue Liuمشاهده پروفایل

پژوهشگر

• brain image analysis
• deep learning
• machine learning
• +۳ مورد دیگر

Yue Liu is a Visiting Scholar at Vanderbilt University and a fourth-year PhD student at her home institution, Northeastern University, China. She is affiliated with the Medical-image Analysis and Statistical Interpretation (MASI) Lab , focusing on interdisciplinary research bridging biomedical engineering and computational methods. Her work intersects with themes like fMRI harmonization , diffusion tensor imaging (DTI) , and reproducibility in neuroimaging . Education : Bachelor of Science, Northeastern University, China (2016) Currently pursuing PhD at Northeastern University, China Research Interests : As part of the MASI Lab, Yue Liu engages in cutting-edge research in brain image analysis , leveraging deep learning and machine learning to advance medical imaging techniques. Her work aligns with broader efforts in image segmentation , quantitative connectivity , and multi-modal data harmonization . Lab Affiliation : She is connected to the MASI Lab , which develops tools for neuroimaging analysis, including open-source frameworks like the Java Image Science Toolkit (JIST) . The lab emphasizes reproducibility, scalability, and integration of diffusion MRI and BOLD signal processing in white matter studies.

• Refractive Error Development
• Myopia
• Accommodation and Binocular Vision
• +۱۰ مورد دیگر

Dr. Katrina Schmid is an Associate Professor at Queensland University of Technology (QUT) , affiliated with the School of Clinical Sciences under the Faculty of Health . Her work bridges clinical optometry with cutting-edge research in refractive error development , myopia control , and ocular surface disorders , while also contributing to teaching pedagogy and authentic assessment in optometric education. PhD (Queensland University of Technology) BAppSc(Opt)(Hons) (Queensland University of Technology) Dr. Schmid’s research spans diverse areas including binocular vision , amblyopia , meibomian gland dysfunction , and the GABAergic system in eye growth control . She has coordinated units like Foundations of Optometric Practice , emphasizing professional identity and evidence-based competencies. Her scientific accolades include the Classic Paper Award (OPO, 2014) and the JLloyd Hewett Award (CXO, 2020) . Her 2021 rankings (#25 globally in refractive error research, #115 in optometric author impact) underscore her international influence. Classic Paper Award, OPO, 2014 JLloyd Hewett Award, CXO, 2020 QUT Medal recipient As an Editorial Board Member for journals like IOVS , TVST , and Nature Scientific Reports , she shapes academic discourse. Her teaching expertise includes ocular anatomy , pharmacology , and research methods , with a focus on student mentoring and examination validation.

• Machine Learning
• Deep Learning
• Computer Vision
• +۷ مورد دیگر

Guy Hembroff serves as an Associate Professor in the College of Computing at Michigan Technological University, where he directs both the MS in Health Informatics program and the interdisciplinary PhD in Computational Science & Engineering program. His academic leadership spans computational health sciences and biomedical data science initiatives. Education: PhD in Computational Science and Engineering, Michigan Technological University MPA in Public Administration, Northern Michigan University BS in Finance and Economics, Michigan Technological University Hembroff's research focuses on human health applications of artificial intelligence, particularly in medical image analysis, natural language processing, and cybersecurity. His work bridges clinical needs with computational solutions through projects like fracture risk prediction from radiographs, lung nodule classification, and mental health intervention modeling. The Biomedical Data Science (BDS) Lab he founded integrates expertise across medicine, data science, and cybersecurity to tackle healthcare challenges. His recent publications demonstrate strong trends in applying deep learning to medical imaging (35%), natural language processing for health literacy (20%), and mental health analytics (15%), with growing emphasis on generative AI applications since 2023. Key methodological approaches include multitask learning, transformer models, and ensemble techniques tailored for healthcare data. Doctoral Advising: Harikrishnan C. (Medical Image Segmentation using AI) Chethana Kadirimangalam (Mental Health Intervention Modeling) Abel Reyes (Medical Image Segmentation and Computer Vision) Tim Van Wagner (Blockchain for Patient Data Auditing) Hembroff leads multiple externally funded research initiatives including the Tick-Talk geographical tick disease monitoring system (MI-SAPPHIRE grant), FHIR-enabled health information exchanges, and mental health intervention optimization models. His BDS Lab maintains active collaborations with Henry Ford Hospital and nonprofit healthcare networks for real-world implementation of health informatics solutions. The Biomedical Data Science Lab operates as a multidisciplinary hub combining clinical expertise with computational innovation. Current projects span physical and mental health domains, with particular focus on disease progression modeling, clinical decision support systems, and secure health data architectures. The lab's work directly informs both graduate curriculum development and healthcare industry partnerships.

Ron Kikinisمشاهده پروفایل

استاد

• Medical Image Computing
• Image Processing
• Biomedical Engineering
• +۳ مورد دیگر

Prof. Ron Kikinis is a Professor of Medical Image Computing at the University of Bremen (Department of Computer Science, Faculty of Mathematics and Computer Science) and holds leadership roles at Fraunhofer MEVIS and Harvard Medical School’s Surgical Planning Laboratory. He also serves as a Professor of Radiology at Harvard Medical School. His academic journey includes a Medicine degree from the University of Zurich (1982) and radiology residency at Zurich University Hospital, complemented by computer vision research at ETH Zurich. His research focuses on medical imaging algorithms, interdisciplinary computational methods in healthcare, and software development for biomedical applications. Notable contributions include pioneering work in virtual endoscopy, surgical planning systems, and open-source platforms like 3D Slicer. He leads multidisciplinary teams addressing complex imaging challenges, with over 316 peer-reviewed publications. Key areas of interest include segmentation, registration, visualization, and high-performance computing in medical imaging. His work bridges clinical needs with technological innovation, emphasizing reproducible AI pipelines and data commons for translational research. He collaborates globally on initiatives such as the NCI Imaging Data Commons and advancing low-cost medical imaging solutions. Prof. Kikinis’ contributions span academic leadership, clinical translation, and software ecosystem development, making him a pivotal figure in medical image computing and translational imaging research.

Susanne Suterمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Prof. Dr. Susanne Suter is a Professor in Data Science at the Institute for Data Science, FHNW School of Computer Science, University of Applied Sciences and Arts Northwestern Switzerland. Her research focuses on Artificial Intelligence, Machine Learning, Deep Learning, and Explainable AI, particularly in medical applications such as Computer Vision, Signal Processing, and Digital Health. She has led projects including Smart Hospital and Hospital @ Home, receiving the Prix D'Excellence SanteNeXt 2023. Her publications emphasize AI-driven solutions in healthcare, spanning medical imaging, real-time clinical decision systems, and high-performance computing. Recent work explores ophthalmic imaging, multimodal ICU data platforms, and tensor-based data compression for visualization. Awards include the Prix D'Excellence SanteNeXt 2023 for innovative healthcare solutions.

Ahmad Nassrمشاهده پروفایل

استاد

• Spinal Surgery
• Cervical Spine Pathology
• Motion Preservation Surgery
• +۷ مورد دیگر

Ahmad Nassr, M.D., is a Professor of Orthopedic Surgery and Neurosurgery at Mayo Clinic School of Medicine, with primary appointments as a Consultant in the Department of Orthopedic Surgery. He holds joint appointments in Physiology & Biomedical Engineering and Pediatric and Adolescent Medicine. His clinical expertise focuses on spinal disorders, including cervical spine pathology, motion preservation surgery, and osteoporosis treatment in the spine. Dr. Nassr completed his Mechanical Engineering degree at MIT, followed by medical training at the University of Pennsylvania, residency at Rush University, and spinal surgery fellowship at the University of Pittsburgh. His research emphasizes improving spinal surgery outcomes through innovations in bone quality assessment, fusion techniques, and minimizing complications. Notable contributions include studies on riluzole efficacy in spinal cord injury, teriparatide effects on lumbar fusion, and genomic screening for adjacent level degeneration. He has authored over 150 peer-reviewed publications and holds leadership roles in organizations like the Cervical Spine Research Society and Lumbar Spine Research Society. Dr. Nassr has received numerous awards, including the 2025 Coventry Award for Best Paper, 2022 Charles Kuntz Scholar Award, and multiple best research paper accolades from spinal societies. He leads the Spine Surgery Research program at Mayo Clinic and mentors trainees nationally. His clinical practice spans complex spinal surgeries, robotic spine procedures, and minimally invasive techniques. Dr. Nassr also serves as an FDA Orthopedic Device Panel member and chairs committees for spine trauma and surgical education.

David J. Fleetمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Image Processing
• +۷ مورد دیگر

David J. Fleet is a Professor in the Department of Computer Science and the Department of Computer and Mathematical Sciences at the University of Toronto. He serves as a CIFAR AI Chair and Faculty Member at the Vector Institute, with additional affiliations at the UofT Robotics Institute. PhD in Computer Science (1990), University of Toronto Research focuses on computer vision, deep learning, visual neuroscience, and computational biology Key contributions in motion analysis, stereopsis, 3D reconstruction, and diffusion models Research Interests: His work explores mathematical foundations for visual motion analysis, human pose estimation, and 3D structure determination in electron cryo-microscopy. Recent projects involve deep learning and diffusion models for video and image synthesis. Scientific Recognition: Recipient of the Koenderink Prize, CIFAR AI Chair, and multiple best paper awards at NeurIPS, CVPR, and BMVC. Former Chair of UTSC's Computer and Mathematical Sciences Department (2012–2017). Students and Collaborations: Mentored numerous PhD and MSc students, including Ali Punjani (co-founder of Structura Biotech) and Sara Sabour (Google Brain). Collaborates with institutions like Google DeepMind (2020–present) and PARC (1999–2003).