جست‌وجوی استاد و پژوهشگر

Tim Curtisمشاهده پروفایل

استاد

• Diabetic Retinopathy
• Vascular Physiology
• Ocular Therapeutics
• +۳ مورد دیگر

Tim Curtis is a Professor at Queen's University Belfast's School of Medicine, Dentistry and Biomedical Sciences, where he leads research on diabetic retinopathy mechanisms and novel therapeutics. His work focuses on mitochondrial dynamics, vascular physiology, and neuroprotection in ocular diseases. He serves as Academic Lead for the Advanced Imaging Core Technology Unit and has directed multiple projects on retinal endothelial dysfunction funded by organizations including Diabetes UK and Wellcome Trust. Research spans molecular pathways in diabetic retinopathy, TRP channel pharmacology, and mitochondrial quality control. Recent work investigates therapeutic targets like TRPV4 channels and mitophagy activators. Articles demonstrate consistent focus on retinal neurovascular unit protection through metabolic modulation and ion channel regulation. Professor Curtis mentors graduate students in vascular physiology, diabetic retinopathy, and ocular therapeutics. His laboratory employs interdisciplinary approaches including live-cell imaging, electrophysiology, and 3D electron microscopy to study retinal pathophysiology.

• Ophthalmology
• Retinal Biology
• Neuroscience
• +۳ مورد دیگر

Cheryl Mae Craft is a Professor of Ophthalmology at the University of Southern California. Her research focuses on retinal biology, photoreceptor development, and retinal degenerative diseases. She has contributed extensively to understanding mechanisms underlying retinoblastoma, retinitis pigmentosa, and other vision-related disorders. Her work integrates molecular biology, genetics, and advanced imaging techniques to study cellular pathways in retinal cells. Key research areas include: Photoreceptor differentiation and degeneration Role of arrestins and metalloproteinases in retinal health Neuroprotective mechanisms in retinal diseases Her publications highlight studies on retinal Müller glial cells, dopaminergic signaling, and inflammatory responses in retinal pathologies. While no specific awards are listed, her contributions have advanced translational research in ophthalmology. Advising and grants: No formal advising or grant details provided in the text. Labs/teams: Not explicitly mentioned, but her work suggests involvement in retinal research groups at USC.

Georgina Gyarmatiمشاهده پروفایل

استادیار

• Cardiovascular Diseases
• Renal Physiology
• Intravital Microscopy
• +۳ مورد دیگر

Georgina Gyarmati, MD, PhD is an Assistant Professor of Research in the Department of Physiology and Neuroscience at the Keck School of Medicine, University of Southern California. She serves as Assistant Director of the Confocal Microscopy Core and maintains an active research profile in cardiovascular and renal physiology. Her work bridges basic science discovery with therapeutic translation through advanced imaging technologies. Her educational background includes an MD from Semmelweis University Medical School (Budapest), an MPH from Johns Hopkins University, and postdoctoral training in renal physiology at USC. Her research focuses on neuro-endothelial cells (NECs) and macula densa cell functionality, revealing novel neuron-like properties in renal sensory systems. She pioneered the concept of macula densa cells as "ganglion-like mini-brains" within nephrons, establishing a kidney-brain axis for systemic communication. Analysis of her 15 most recent publications (2020-2024) shows consistent focus on intravital multiphoton imaging to study glomerular dynamics, endothelial function, and cellular mechanisms in kidney disease. Key themes include NEC characterization, salt sensing pathways, nanomedicine delivery, and vascular remodeling in hypertension and genetic disorders. Her work frequently appears in high-impact journals like JCI Insight and Journal of the American Society of Nephrology. As Assistant Director of the Multiphoton Microscopy Core, she has advanced intravital imaging applications for quantitative visualization of physiological parameters across multiple organs including kidney, brain, liver, spleen, and skin. Her technical expertise enables single-cell resolution studies of endothelial remodeling, podocyte function, and immune cell interactions in disease models.

Justin W.L. Wanمشاهده پروفایل

استاد

• Scientific Computing
• Medical Image Processing
• Computational Finance
• +۴ مورد دیگر

Justin W.L. Wan is a Professor at the David R. Cheriton School of Computer Science, University of Waterloo. He holds a Canada Research Chair in Scientific Computing and is currently Co-Director for the Computing and Financial Management Program. He earned his BSc from Hong Kong (1992), MA (1995) and PhD (1998) from UCLA's Applied Math Program. Research interests include: Scientific computing Medical image processing (CT scans, cell segmentation, image registration) Computational finance (option pricing, regime switching, hedging parameters) Computer graphics simulation of natural phenomena Development of robust multigrid methods for PDEs Recent publications span: 2021: Multigrid methods for mean-field games and optimal mass transport 2020: Deep learning approaches for high-dimensional finance and CT artifact reduction 2019: Multigrid for Monge-Ampere equations and multi-asset options 2018: Cellular image segmentation algorithms Research team includes: PhD students: Hossein Aboutalebi, Andrew Na, Connor Tannahill, Chris West Master's students: Ying Kit (Marco) Hui, Shujie Liu, Lufan Wang Administrative roles held: Director, Centre of Computational Mathematics for Industry and Commerce (2010-2015) Associate Director, Cheriton School of Computer Science (2015-2018) Director of Graduate Studies (2019-2021) Professional service: Secretary, Canadian Applied and Industrial Mathematics Society (2015-2020) Organizer, CAIMS Annual Meeting (2021) Journal editor and program committee member

Steve Presseمشاهده پروفایل

استاد

• Biophysics
• Statistical Physics
• Machine Learning
• +۵ مورد دیگر

Steve Presse is a Professor at Arizona State University (ASU), affiliated with the School of Molecular Sciences and the Department of Physics. He holds roles in the Biodesign Center for Mechanisms of Evolution, Mechanisms of Evolution Researchers, and the Center for Biological Physics. His research focuses on integrating Bayesian inference, machine learning, and statistical physics to study living systems, with experimental work on bacterial hydrodynamics and predation. Education: Ph.D. in Chemical Physics from MIT (2008). Research Interests: Bayesian methods, ML/AI in biophysics, single-molecule analysis, and stochastic processes. Experimental studies include bacterial interactions and predator-prey dynamics. Research Trends: Recent work emphasizes single-molecule reaction-diffusion, Bayesian inference for binding kinetics, and deep learning applications in biophysical data. Themes include superresolved tracking, fluorescence microscopy analysis, and modeling complex biological systems. Lab & Collaborations: The Pressé Lab actively recruits postdocs, graduate, and undergraduate students. Collaborations focus on developing computational tools for data modeling, as seen in his textbook Data Modeling for the Sciences .

Élodie Puybareauمشاهده پروفایل

استادیار

• Medical Image Analysis
• Mathematical Morphology
• Deep Learning
• +۴ مورد دیگر

Élodie Puybareau is Assistant Professor at EPITA and head of the Image Major program. Her research develops deep learning and mathematical morphology techniques for medical imaging challenges including brain tumor segmentation, white matter hyperintensity quantification, and pulmonary vessel anomaly detection. She actively contributes to MICCAI Grand Challenges (BraTS, VALDO, MyoPS) where her work on multimodal segmentation and uncertainty quantification has received recognition. Current projects investigate few-shot learning for pulmonary embolism detection and E3 ubiquitin ligase complexes in protein degradation pathways. Puybareau supervises PhD candidates in medical image analysis and serves as reviewer for leading journals including Medical Image Analysis and IEEE Transactions on Medical Imaging. She co-leads MorphoNet, a French research network exploring mathematical morphology in deep learning.

• Machine Learning
• Computer Vision
• Medical Imaging
• +۲ مورد دیگر

Prof. Thomas Lampert holds the Chair of Data Science and Artificial Intelligence at Télécom Physique Strasbourg and ICube Laboratory, University of Strasbourg. He is also an Expert at the Council of Europe on AI. His research specializes in machine learning applications for medical imaging and remote sensing, with focus on computer vision, time-series analysis, and domain adaptation techniques. Current projects include developing constrained clustering methods for satellite imagery and stain-invariant segmentation for histopathology. Lampert completed his PhD at University of York and has held research positions at multiple international institutions. He maintains strong industry collaborations and leads projects addressing challenges in healthcare informatics and environmental monitoring through advanced computational methods.

• Developmental Biology
• Skeletal Biology
• Paleontology
• +۳ مورد دیگر

Claire E. Clarkin is an Associate Professor specializing in Developmental and Skeletal Biology at the University of Southampton. Her research focuses on integrating evolutionary perspectives with modern biomedical challenges, particularly addressing sex bias in osteological research and applying paleontological methods to contemporary bone pathology studies. She is affiliated with the University's Highfield Campus and collaborates with international teams on vertebrate paleobiology projects. Research Interests: Her work spans Paleontological anatomy of Mesozoic vertebrates Sexual dimorphism in skeletal biology Bone microstructure analysis using advanced imaging techniques Policy frameworks for inclusive skeletal research Publications Trends: Recent work emphasizes interdisciplinary approaches combining paleontology with modern biomedical research, particularly in addressing gender disparities in clinical studies and applying computational methods like persistent homology to bone analysis. Notable collaborations include spinosaurid dinosaur discoveries in the UK and studies on synthetic biomaterials safety. Awards: Recognized for contributions to understanding Paget's disease bone vascular networks (2018). Currently supervising six PhD students across skeletal biology and biomedical research programs.

Vivek Venkatachalamمشاهده پروفایل

استادیار

• Experimental Biophysics
• Neurophysiology
• Calcium Imaging
• +۴ مورد دیگر

Vivek Venkatachalam is an Assistant Professor of Physics at Northeastern University's College of Science, specializing in experimental biophysics and neurophysiology. His research focuses on developing tools to measure and manipulate whole-brain activity in freely behaving C. elegans, integrating advanced neurotechnology and microscopy techniques to study neural development and behavior. He earned a PhD in Physics from Harvard University (2012) and dual SB degrees in Electrical Science and Engineering and Physics from MIT (2006). His lab, based in Northeastern's Interdisciplinary Science and Engineering Complex, collaborates with the Institute for Chemical Imaging of Living Systems and the Biology Department. Key research areas include developmental neurophysiology of C. elegans from birth to adulthood, optogenetic manipulation, calcium imaging, and quantitative behavior analysis. The lab pioneered whole-brain imaging tools for C. elegans, enabling studies of neural activity patterns and their relationship to behavior. Current projects explore sensory integration, developmental decision-making, and neural circuit remodeling. His team includes PhD students Joshua Whitener, Mahdi Torkashvand, James Yu, and Maedeh Seyedolmohadesin, along with postdoc Dr. Jinmahn Kim. Venkatachalam's work bridges physics, biology, and engineering, with applications in understanding fundamental nervous system principles.

Thomas Grafمشاهده پروفایل

استاد پژوهشی

• Macrophage
• Cancer biology
• Reprogramming
• +۱۶ مورد دیگر

Thomas Graf is a Research Professor at the Centre for Genomic Regulation (CRG) in Barcelona, Spain, and an ICREA Research Professor since 2006. His academic career spans multiple prestigious institutions including Albert Einstein College of Medicine, European Molecular Biology Laboratory, and Max Planck Institute. Research focus: Cancer biology, Reprogramming, Genetic engineering, Molecular biology Key contributions: Hematopoietic stem cells, Transdifferentiation, Computational biology His recent publications demonstrate expertise in inverse imaging problems, diffusion models, and biomedical applications spanning cardiology and protein structure analysis. Scientific accolades include the Paul Ehrlich Prize and membership in Academia Europaea. 1989: Paul Ehrlich and Ludwig Darmstaedter Prize 1983: Wilhelm Warner Foundation Prize Other: Leukemia Research Award, German Society for Microbiology and Hygiene Prize, Academia Europaea membership

Nassir Navabمشاهده پروفایل

استاد

• Computer Assisted Interventions
• Augmented Reality
• Medical Imaging
• +۳ مورد دیگر

Prof. Dr. Nassir Navab is a full professor and director of the Chair for Computer Aided Medical Procedures (CAMP) at the Technical University of Munich (TUM), Germany. He also serves as an adjunct professor of computer science at Johns Hopkins University (USA) and holds secondary appointments at TUM’s Medical School. He is internationally recognized for his pioneering work in computer-assisted interventions, augmented reality, medical imaging, computer vision, and machine learning. Education: PhD from INRIA and University of Paris XI, France Postdoctoral Fellowship at MIT Media Laboratory Research Interests: Prof. Navab's research bridges the gap between computer science and medicine. His core interests include: Robotic Imaging Systems : Developing robotic platforms for intraoperative imaging Augmented Reality in Surgery : Creating AR systems for surgical navigation Medical Image Computing : Advanced algorithms for medical image analysis Machine Learning in Healthcare : Deep learning applications in medical imaging Computer Vision : 3D reconstruction and scene understanding Research Trends: His recent publications demonstrate a strong focus on deep learning applications in medical imaging, particularly in 3D volumetric analysis, real-time surgical guidance systems, and automated diagnostic tools. The work spans from fundamental algorithm development to clinical translation, with significant contributions in areas like neural network architectures for medical image segmentation, pose estimation for robotic surgery, and augmented reality systems for intraoperative navigation. Scientific Awards: IEEE Fellow (2022) MICCAI Society Enduring Impact Award (2021) IEEE ISMAR 10 Year Lasting Impact Award (2015) Fellow of MICCAI Society (2012) SMIT Technology Award (2010) Siemens Inventor of the Year (2001) Over 50 best paper awards at international conferences Leadership & Service: General Chair: MICCAI 2015, ISMAR 2001/2005/2014 Founding Board Member: IPCAI (2010-2021) Editorial Board Member: IEEE TMI, MedIA Steering Committee Member: IEEE ISMAR (since 2001) Board of Directors: MICCAI Society (2007-2012, 2014-2017) Laboratories & Teams: Prof. Navab leads the Laboratories for Computer Aided Medical Procedures (CAMP) at TUM, a world-renowned research group focused on developing cutting-edge technologies for computer-assisted surgery and medical interventions. The lab has produced numerous award-winning PhD students who have gone on to become leaders in the field. He also directs the biannual Medical Augmented Reality school series at Balgrist Hospital in Zurich, Switzerland, which has become a premier educational event in the field.

• Diabetes
• Insulin Secretion
• Cell Signalling
• +۴ مورد دیگر

Peter Thorn is the Chair in Molecular and Cellular Physiology at the University of Sydney's School of Medical Sciences and a member of the Charles Perkins Centre. He leads the Peter Thorn Laboratory, which focuses on diabetes research through three primary projects: 1) Vascular targeting of insulin secretion using advanced imaging and molecular biology, 2) Disease mechanisms in prediabetes and diabetes using human and mouse islets, and 3) Engineering stem cell therapies for diabetes cure. His research integrates fluorescence imaging, electron microscopy, and molecular techniques to study pancreatic beta-cell function. Education includes: PhD from University of Edinburgh (1983-1986) MSc Neurophysiology from University of Edinburgh (1982-1983) BSc Zoology/Psychology from University of Nottingham (1979-1982) Research spans diabetes mechanisms, insulin secretion regulation, and beta-cell polarization, with strong emphases on cellular signaling pathways, islet vasculature interactions, and stem cell engineering. Recent work demonstrates beta cells structurally polarize insulin secretion toward capillaries, with disruptions in diabetes models. Key approaches include live-cell imaging, proteomics, and biomaterial interfaces for cell therapies. Publications since 2020 show concentrated focus on: 1) Beta-cell structural/functional polarization in diabetes, 2) Molecular controls of insulin granule dynamics, 3) Stem cell-derived beta-cell maturation, and 4) Advanced imaging techniques for islet analysis. Machine learning applications enhance spatial analysis of insulin secretion patterns. Current grants include: 'The endocrine synapse: A novel mechanism for insulin secretion' (Bioscientifica Trust) 'Novel microcluster approach for beta cell function' (JDRF) 'Optimizing implant surfaces for diabetes treatment' (NSW Boosting Innovation) 'NHMRC Project Grants on beta-cell apical domains and insulin granule aging' Collaborations involve Drs. James Hudson, Enzo Porello, and international teams for stem cell engineering and diabetes therapeutics.

• Biomedical Engineering
• Paediatrics and Reproductive Medicine
• Computation Theory and Mathematics
• +۳ مورد دیگر

Priya Rani is a Lecturer in the School of Engineering at RMIT University. Her research focuses on interdisciplinary areas including Biomedical Engineering, Artificial Intelligence, and Neurosciences. She actively supervises research projects such as developing peripheral electronics for neural recording systems and applying machine learning for clinical delirium diagnosis. Dr. Rani’s work bridges computational methods with healthcare applications, emphasizing robust machine learning models and medical imaging analysis. She is affiliated with RMIT’s City Campus and contributes to advancing technologies for sustainable and intelligent systems. Research Interests: Her primary areas include computational theory, biomedical engineering, and AI-driven solutions for healthcare challenges. Notable projects involve semantic segmentation using attention-augmented networks and damage monitoring in advanced materials. Dr. Rani’s research trends highlight machine learning robustness, medical image analysis, and smart home technologies for aging populations. Supervision Focus: Neural recording systems, clinical AI applications Key Projects: Cortical neural interfaces, delirium prediction algorithms Technical Expertise: Machine learning, image processing, medical diagnostics Collaboration and Impact: Her work addresses real-world challenges through interdisciplinary approaches, with publications spanning medical IoT, nanosensors, and composite materials analysis. Dr. Rani’s contributions emphasize practical applications of AI and computational methods in healthcare and engineering domains.

Ullrich Kötheمشاهده پروفایل

دانشیار

• Machine Learning
• Neural Networks
• Explainable AI
• +۶ مورد دیگر

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

Elizabeth Holmمشاهده پروفایل

استاد

• Computational Materials Science
• Microstructural Evolution
• Machine Learning in Materials
• +۳ مورد دیگر

Elizabeth Holm is Professor and Department Chair of Materials Science and Engineering at the University of Michigan. Her research employs computational methods to study microstructural evolution, with expertise spanning atomic-scale molecular dynamics to continuum-scale modeling. Education includes a PhD in Materials Science and Scientific Computing from University of Michigan. Research focuses on computational materials science with emphasis on grain boundary dynamics, machine learning applications in materials, and additive manufacturing characterization. Recent publications develop AI methods for microstructure prediction, grain growth modeling, and automated analysis of material images. Work integrates machine vision with physics-based models to understand material behavior across scales. National Academy of Engineering Member (2025) ASEE Mike Ashby Outstanding Materials Educator Award (2022) AIME Honorary Member (2022) ASM Edward DeMille Campbell Memorial Lecturer (2021) The Minerals, Metals & Materials Society Fellow (2019) Professor Holm leads research on computational design of hierarchical materials and has held leadership positions including President of The Minerals, Metals & Materials Society (2013).