جست‌وجوی استاد و پژوهشگر

Tim Rosenowمشاهده پروفایل

پژوهشگر ارشد

• Medical Physics
• MRI
• Preclinical MRI
• +۷ مورد دیگر

Dr. Tim Rosenow is a Senior Research Fellow at the Centre for Microscopy, Characterisation & Analysis and an Adjunct Senior Research Fellow at the School of Human Sciences at The University of Western Australia. He serves as the Facility Fellow for the 9.4T preclinical MRI at the National Imaging Facility, University of Western Australia node. His work bridges physics, medical imaging, and clinical applications with a focus on developing novel imaging techniques. Education: PhD in Paediatrics, 'Quantitative analysis of chest computed tomography in early cystic fibrosis lung disease' (2017) Graduate Certificate in Paediatric Respiratory Science (2011) Bachelor of Science in Physics/Computer Science (2007) Dr. Rosenow's research centers on preclinical MRI and medical image analysis techniques. He has designed, validated, and patented methods for disease quantification in respiratory conditions. His current work focuses on novel MRI techniques to measure physiological processes in health and disease. His research spans biomedical engineering, physics, and clinical medicine, with applications in cystic fibrosis, brain imaging, and disease modeling. Analysis of his recent publications reveals a strong focus on advanced imaging techniques across multiple domains. His work ranges from preclinical MRI development to clinical applications in cystic fibrosis and neuroscience. The publications demonstrate expertise in both technical MRI development and clinical applications, with collaborative work spanning multiple institutions globally. Dr. Rosenow is currently an investigator on an active research project 'Amplifying the response to radiotherapy for children with aggressive brain cancer' (2024-2027), funded by the Department of Health (Western Australia). He works with the Bruker BioSpec 94/30 US/R MRI equipment at the Centre for Microscopy, Characterisation & Analysis, focusing on applications in magnetic resonance imaging, nuclear magnetic resonance imaging, and in vivo studies.

Michael Lustigمشاهده پروفایل

دانشیار

• Computational Magnetic Resonance Imaging (MRI)
• Medical Imaging
• Signal Processing
• +۳ مورد دیگر

Michael Lustig is an Associate Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. His research focuses on computational imaging methods in magnetic resonance imaging (MRI), with emphasis on compressed sensing, motion correction, and machine learning applications in medical imaging. He has contributed significantly to the development of open-source tools like SparseMRI and reconstruction algorithms such as ENLIVE and DSLR+. PhD in Electrical Engineering, Stanford University (2008) MSc in Electrical Engineering, Stanford University (2004) BSc in Electrical Engineering, Technion, Israel Institute of Technology (2002) Lustig’s work spans advanced MRI techniques including ultra-short echo time (UTE) imaging , low-rank reconstruction , and physics-informed neural networks . He has pioneered methods for integrating RF motion sensing via beat pilot tones during MRI scans and developed tools for memory-efficient large-scale image reconstruction. His recent publications focus on MRDust (wireless implantable interfaces), Twstr coils (discrete-component-free MRI hardware), and Resonet (noise-trained off-resonance correction). These works reflect trends in self-supervised learning, contact-free motion detection, and hardware-software co-design for diagnostic imaging. Scientific Awards & Fellowships International Society for MR in Medicine Gold Medal (2025) Pioneer Award (2023) ISMRM Fellow (2017) Electrical Engineering Outstanding Teaching Award (2016) Bakar Spark Award (2015) Okawa Research Grant (2014) Sloan Research Fellow (2013) Hellman Fellow (2012) Lustig advises PhD students like Frank Ong and leads research at the MikLab , contributing to open-source software platforms such as SigPy and DeepInPy . His work bridges computational methods, hardware innovation, and clinical translation in MRI.

Kannan Ramchandranمشاهده پروفایل

استاد

• Information Theory
• Signal Processing
• Artificial Intelligence
• +۵ مورد دیگر

Kannan Ramchandran is the Gilbert Henry Gates Endowed Chair Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. He holds a Ph.D. from Columbia University (1993) and has been at UC Berkeley since 1999. His research focuses on information theory, signal processing, machine learning, and AI, with contributions to distributed coding, federated learning, and robust video transmission. He leads the Berkeley Audiovisual Signal Processing and Communication Systems (BASiCS) Lab and is affiliated with the Simons Institute and CLIMB. Awards include the IEEE Kobayashi Award (2017) and the NSF CAREER Award (1997). He teaches courses like EE 229A (Information Theory) and has advised numerous students in areas like coding theory and distributed systems. Education: B.E., City College of New York (1982) M.S., Columbia University (1984) Ph.D., Columbia University (1993) Research Interests: Distributed source coding, federated learning frameworks, robust communication systems, and AI-driven signal processing. His work bridges theory and practice, with impactful contributions to video compression, error-resilient systems, and modern machine learning paradigms. Key Contributions: Pioneered DISCUS (Distributed Source Coding using Syndromes), developed SAFFRON for group testing, and advanced federated learning methodologies. His lab’s work on spectrum-blind sampling and diffusion models for MRI reconstruction highlights ongoing innovation. Awards: IEEE Fellow (2004), Okawa Grant (2000), and multiple teaching awards. His research has been published in top venues like IEEE Transactions on Information Theory and Neural Information Processing Systems.

Müjdat Çetinمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Data Science
• +۱۲ مورد دیگر

Müjdat Çetin is a Professor of Electrical and Computer Engineering and serves as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science at the University of Rochester. He previously held faculty positions at Sabancı University and was a Research Scientist at MIT, with visiting roles at Boston University, Northeastern University, and MIT. Education: PhD in Electrical Engineering, Boston University, 2001 MS in Electrical Engineering, University of Salford, 1995 BS in Electrical Engineering, Boğaziçi University, 1993 His research lies at the intersection of signal processing, machine learning, and data science, with applications in biomedical imaging, radar, and brain-computer interfaces. He develops probabilistic and deep learning models for robust information extraction from noisy and complex data. His work emphasizes computational imaging, sparse representations, and multimodal data fusion. The recent publications reflect a strong trend toward integrating Bayesian methods and deep learning in imaging sciences, particularly in medical image reconstruction, neuroimaging analysis, and radar systems. His group actively explores transformer architectures, federated learning, and model-based deep learning for solving inverse problems in imaging. Scientific Awards and Honors: IEEE Fellow IEEE Signal Processing Society Best Paper Award IET Radar, Sonar and Navigation Premium Award Elsevier Signal Processing Best Paper Award Turkish Academy of Sciences Distinguished Young Scientist Award (GEBİP) ODTÜ Mustafa Parlar Foundation Research Incentive Award TÜBİTAK Career Award Boston University Best Engineering Research Award Professor Cetin has advised numerous PhD and Master’s students and led significant research grants in data science and imaging. He has served as a Senior Area Editor for IEEE Transactions on Image Processing and IEEE Transactions on Computational Imaging, and held editorial roles in several top journals. He has chaired major conferences including ICASSP, ICIP, and IVMSP workshops. He leads a multidisciplinary research group focused on data science and imaging, collaborating with neuroscientists and medical researchers. The team develops novel algorithms for brain-computer interfaces, medical image analysis, and remote sensing systems, often integrating machine learning with physical models of data acquisition.

Luca Danielمشاهده پروفایل

استاد

• Electrical Engineering
• Biomedical Engineering
• Computer Science
• +۵ مورد دیگر

Luca Daniel is a Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Research Laboratory of Electronics (RLE) and the Department of Electrical Engineering and Computer Science. His research focuses on computational modeling, simulation, and design of complex systems, with applications in electronics, biomedical engineering, and machine learning. He leads the Computational Prototyping Group, which develops algorithms and software for model order reduction, integral equation solvers, and uncertainty quantification. Professor Daniel holds a Ph.D. from the University of California, Berkeley (2003), and has authored numerous influential papers in top conferences and journals. His work has been recognized with awards such as the APS Best Paper Award (2023), EPEPS Best Conference Paper Award (2020), and the ACM Outstanding Ph.D. Dissertation Award in EDA (2016). His research interests include electromagnetic simulation, biomedical systems, stochastic methods, and machine learning robustness. He has advised over 20 PhD students and developed open-source tools like FastMaxwell and CAPLET for electromagnetic analysis. His teaching spans courses on modeling, simulation, signals and systems, and numerical methods.

Micheal Daviesمشاهده پروفایل

استاد مهمان

• Signal and Image Processing
• Sparse Representations
• Compressed Sensing
• +۳ مورد دیگر

Micheal Davies is a Professor of Signal and Image Processing at the University of Edinburgh and a Visiting Professor at the Laboratoire de Physique (LPENSL) at ENS Lyon (2023-2024). His research focuses on sparse representations, compressed sensing, machine learning, and inverse problems. He holds a First-Class Honours degree in Engineering from the University of Cambridge (1989) and a PhD in Nonlinear Dynamics from University College London (1993). His collaboration with the SiSyPh team at ENS Lyon centers on self-supervised deep learning methods for scientific imaging. Key contributions include advancing theoretical frameworks for inverse problems and algorithmic strategies in imaging. He delivered talks at the MLSP seminar and lectures for master's students in physics and computer science. Awards: Fellow of the Royal Academy of Engineering (2017), Fellow of the Royal Society of Edinburgh (2018). Research Impact: Pioneered work in compressed sensing for MRI and sparse signal processing. His visit strengthens interdisciplinary research at ENS Lyon, bridging physics, AI, and signal processing. Current projects with J. Tachella aim to eliminate ground-truth data reliance in scientific imaging.

Nikos Komodakisمشاهده پروفایل

استاد

• Deep Learning
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Nikos Komodakis is a Professor in the Computer Science Department at the University of Crete, Greece, where he develops efficient, scalable and mathematically well-grounded algorithms for analyzing visual data including static natural images, video, and medical image data. His research spans deep learning, computer vision, machine learning, and artificial intelligence with significant contributions to self-supervised learning, few-shot learning, and knowledge distillation techniques. His work demonstrates a strong theoretical foundation combined with practical applications, particularly in medical imaging. Komodakis has published extensively in top-tier computer vision venues including CVPR, ICCV, ECCV, and IEEE Transactions on Image Processing. His recent publications (2022-2025) show a growing emphasis on medical image analysis applications while maintaining strong contributions to fundamental computer vision problems. Notable contributions include novel approaches for unsupervised representation learning that surpass state-of-the-art methods, effective techniques for knowledge distillation (such as the QUEST framework), and innovative frameworks for few-shot visual learning. Komodakis serves on the editorial boards of prestigious journals including the International Journal of Computer Vision, Computer Vision and Image Understanding Journal, and Computational Intelligence Journal. He has been a frequent area chair for major computer vision conferences including CVPR, ICCV, ECCV, and BMVC. Spyros Gidaris received the Ponts Foundation Best Thesis Prize and the University Paris-Est Best Thesis prize under Komodakis' supervision Sergey Zagoruyko received the AFRIF 2018 Thesis Prize for his PhD work supervised by Komodakis His research group has developed influential techniques including Online Bag-of-Visual-Words Generation for Unsupervised Representation Learning, which surpassed previous state-of-the-art methods. The group maintains active GitHub repositories for many of their publications, demonstrating commitment to reproducible research. Current research directions include advancing medical image analysis through deep learning, improving self-supervised learning frameworks, and developing more efficient neural network architectures.

• Medical Imaging
• Biomedical Engineering
• Cancer Research
• +۳ مورد دیگر

Dr. Gene Kim is a Professor of Biomedical Engineering in Radiology at Weill Cornell Medicine . His research focuses on developing advanced magnetic resonance imaging (MRI) techniques for cancer diagnosis and treatment monitoring, particularly in breast and head/neck cancers. The Kim Laboratory investigates tumor microenvironment through dynamic contrast-enhanced (DCE-MRI) and diffusion MRI (dMRI) methodologies. Ph.D. in Biomedical Engineering, University of Southern California Postdoctoral Training in Cancer Imaging, University of Pennsylvania Key research areas include: Tumor vascular properties and water exchange kinetics Quantitative dMRI for cell viability assessment Adipose-tissue interactions in breast cancer Development of active contrast encoding (ACE-MRI) for comprehensive imaging Recent publications demonstrate expertise in high-resolution 3D imaging , deep learning applications , and temporal resolution optimization . The lab has received continuous funding from the National Cancer Institute through grants like R01CA219964 and UG3/UH3CA228699 . Notable methodological contributions include: Golden-angle radial sparse parallel (GRASP) MRI POMACE framework for cell size measurement ACE-MRI for integrated T1/flip angle quantification

Rongrong Wangمشاهده پروفایل

دانشیار

• Applied and Computational Harmonic Analysis
• Machine Learning
• Neural network training and generalization
• +۶ مورد دیگر

Rongrong Wang serves as Associate Professor in both the Department of Computational Mathematics, Science and Engineering (CMSE) and Department of Mathematics at Michigan State University, based in the Engineering Building with contact email wangron6@msu.edu . Her academic journey includes: B.S. in Mathematics and B.A. in Economics from Peking University, Beijing Ph.D. in Applied Mathematics from University of Maryland College Park under John Benedetto and Wojciech Czaja Postdoctoral fellowship at University of British Columbia with Ozgur Yilmaz and Felix Herrmann Her research spans Applied and Computational Harmonic Analysis , Machine Learning , and Compressed Sensing with focus areas including neural network training dynamics, learning theory, tensor analysis, and inverse problems. She investigates theoretical foundations of deep learning while developing applications for medical imaging and signal processing. Recent publications (2024-2025) demonstrate strong interdisciplinary work at the intersection of deep learning theory and medical imaging, particularly exploring edge-of-stability phenomena in neural networks and diffusion-guided reconstruction techniques. Her work also advances tensor decomposition methods and in-context learning mechanisms in language models. Professor Wang actively recruits self-motivated graduate and undergraduate students with backgrounds in mathematics, computer science, or electrical engineering for research opportunities in her lab.

Dongdong Chenمشاهده پروفایل

استادیار

• Machine Learning
• Computer Vision
• Image Processing
• +۴ مورد دیگر

Dongdong Chen serves as an Assistant Professor in the Department of Computer Science at Heriot-Watt University's School of Mathematical and Computer Sciences in Edinburgh, United Kingdom. His research profile indicates active supervision of PhD students with full scholarships available, demonstrating his established position within the academic community. Current research outputs show consistent publication activity from 2020 through 2024 with increasing impact. Dr. Chen's research interests center on machine learning applications for imaging systems, with particular expertise in image processing, computer vision, computational imaging, and inverse problems. His work bridges theoretical machine learning with practical applications, especially in medical imaging contexts. The fingerprint analysis of his publications reveals strong connections to Deep Learning (31%), Unsupervised Learning (25%), and Inverse Problems (43%), indicating a cohesive research trajectory focused on unsupervised frameworks for imaging challenges. Analysis of his 15 most recent publications (2021-2025) shows a clear evolution toward unsupervised and equivariant learning approaches for inverse problems, with increasing focus on diffusion models and medical applications. The research demonstrates strong theoretical foundations combined with practical implementations, particularly in MRI and medical diagnostics. Citation metrics indicate significant impact, with several papers exceeding 40 Scopus citations. His notable scientific achievements include: IES Best Student Paper Award (2014) MICCAI'18 BIA Best Paper Nomination (2018) Dr. Chen actively mentors PhD students and has attracted research attention through media coverage of his ICCV paper on Equivariant Imaging. His research contributes to UN Sustainable Development Goals through imaging technology development. Current activities include recruiting PhD students for projects in Edinburgh with full scholarship support, indicating ongoing research expansion and team development.

• Magnetic Resonance Imaging
• Deep Learning
• Compressed Sensing
• +۳ مورد دیگر

Mehmet Akcakaya is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on machine learning for medical imaging, especially MRI reconstruction using deep learning and compressed sensing. He leads NIH-funded projects and is accepting PhD students. His research interests include physics-informed deep learning , compressed sensing , and MRI reconstruction . He develops algorithms for high-resolution, accelerated MRI with applications in cardiac and brain imaging. Key challenges addressed are reference-free reconstruction and robustness in inverse problems. Recent work shows a trend toward self-supervised and unsupervised deep learning for MRI, with innovations in cycle-consistent learning and diffusion models for inverse problems. There is increasing emphasis on few-shot and zero-shot adaptation to handle limited training data. Dr. Akcakaya leads the NIH-funded project Robust and Efficient Learning of High-Resolution Brain MRI Reconstruction and collaborates on the Center for Mesoscale Connectomics . He is accepting PhD students and mentors research in medical imaging and machine learning. His work is conducted at the University of Minnesota's Center for Magnetic Resonance Research, leveraging interdisciplinary collaborations in biomedical engineering and neuroscience.

• Machine Learning
• Deep Learning for Medical Applications
• Medical Imaging
• +۳ مورد دیگر

Chenyang Shen is an Assistant Professor in the Department of Radiation Oncology at the University of Texas Southwestern Medical Center. He earned his B.S. from Yangzhou University and his M.S. and Ph.D. from Hong Kong Baptist University, followed by a medical physics residency at UT Southwestern. His clinical focus includes RefleXion and EROC TPOD, while his research integrates machine learning and deep learning into medical imaging, image processing, and intelligent treatment planning for radiotherapy. Education: B.S. (Yangzhou University), M.S. and Ph.D. (Hong Kong Baptist University) Dr. Shen’s research spans medical physics , AI-driven treatment optimization , and image-guided radiation therapy . His work emphasizes deep learning for dose calculation, real-time adaptive planning, and synthetic CT generation from CBCT/MRI data. Key publications highlight innovations in PET-guided liver motion tracking and AI for brachytherapy applicator digitization . He has received the ASTRO Basic/Translational Science Award for junior investigators and AAPM Best in Physics (Therapy) . His grants and collaborations focus on improving radiation dose accuracy and enabling biology-guided therapy via PET-Linac systems. Labs and teams he works with include the Division of Medical Physics and Engineering at UT Southwestern.

Philippe Delachartreمشاهده پروفایل

استاد

• Medical Imaging
• Signal Processing
• Image Processing
• +۷ مورد دیگر

Philippe Delachartre is a Professor at INSA Lyon (University of Lyon) in the Department of Electrical Engineering and researcher at CREATIS (Center for Research and Applications in Image and Signal Processing). He obtained his MS (1990) and PhD (1994) in Signal and Image Processing from INSA Lyon, joining the faculty in 1995 as Associate Professor before being promoted to Professor. His research focuses on medical image processing including: Advanced signal processing for ultrasound and MRI Motion estimation and segmentation algorithms Hypercomplex signal theory applications Deep learning for medical image analysis Real-time data acquisition systems With 30+ years of experience, he's contributed to over 100 publications. Recent publications (2018-2025) show strong focus on: Deep learning applications in medical signal classification Advanced segmentation methods (phase-field, CNN) Mathematical frameworks using hyperquaternions 3D ultrasound analysis for neurology and dermatology Cardiac motion estimation algorithms Research grants include: Regional project on emboli classification with deep learning (€170k) ANR LabCom project on Doppler ultrasound (€300k) Dermis characterization contract with Institut Pierre Fabre (€45k) Prostate segmentation project (€21k) Image denoising research (€100k) He has supervised 9 PhD students and leads research activities at CREATIS laboratory focusing on innovative medical imaging solutions.

John A. Lincolnمشاهده پروفایل

استاد

• Multiple Sclerosis
• Neuroimmunology
• Neurodegeneration
• +۷ مورد دیگر

John A. Lincoln, MD, PhD is a Professor of Neurology at The University of Texas Health Science Center at Houston (UTHealth), where he serves as Director of the MRI Analysis Center and Co-Director of the Neurology Residency Program. He joined the Multiple Sclerosis research group at UTHealth in 2011 after previously serving on the faculty at the University of Medicine and Dentistry of New Jersey (UMDNJ) from 2008-2011. Dr. Lincoln earned his Bachelor of Science in Genetics from Texas A&M University in 1992, followed by a Master's in Microbiology and PhD in Immunology from Texas Tech University. He completed his medical degree at Texas Tech University Health Sciences Center in 2002, followed by a Neurology residency at UT Health Science Center at Houston and a Neuroimmunology fellowship at UMDNJ. His research focuses on neurodegenerative processes in multiple sclerosis, with particular emphasis on glymphatic system dysfunction, cerebrovascular reactivity, spinal cord evaluation, and bladder-related neurological complications. Recent publications demonstrate his integration of advanced MRI techniques with machine learning approaches to predict disease severity and understand MS pathology. Analysis of his publication record from 2008-2024 reveals a consistent research trajectory focused on MS mechanisms and clinical manifestations, with increasing emphasis on neuroimaging biomarkers and computational approaches in recent years. His work spans clinical neurology, neuroimmaging, immunology, and rehabilitation aspects of MS. As Director of the MRI Analysis Center, Dr. Lincoln oversees advanced neuroimaging research and clinical applications. His leadership in the Neurology Residency Program demonstrates his commitment to training the next generation of neurologists specializing in complex neurological disorders. Dr. Lincoln's clinical expertise includes diagnosing and managing multiple sclerosis and immune-mediated neurological disorders, with special attention to neurological complications such as bladder dysfunction and trigeminal neuralgia in MS patients. His multidisciplinary approach bridges basic science discoveries with clinical applications to improve patient outcomes.