Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.
Prof. Marianne Pouplier is a Professor at the Institute of Phonetics and Speech Processing (IPS) at Ludwig Maximilian University of Munich. Her research focuses on speech production mechanisms, particularly coarticulation, phonetic universals, and language-specific variations. She investigates articulatory timing, speech errors, and cross-linguistic differences in consonant clusters using advanced methodologies like real-time MRI and electromagnetic articulography. Her work bridges theoretical models (e.g., Articulatory Phonology) with empirical data, emphasizing the interplay between phonological representation and motor execution. Key contributions include studies on nasal coarticulation, larynx dynamics, and the role of articulatory effort in speech production. Selected publications highlight her expertise in analyzing speech motor control through interdisciplinary approaches. She collaborates internationally, contributing to projects like the Bavarian Archive for Speech Signals (BAS). No awards or grants are explicitly listed, but her extensive publication record underscores her scholarly impact.
Axel Haase is a Carl von Linde Senior Fellow at the Technical University of Munich (TUM) and Director of the Institute of Medical Engineering (IMETUM). He holds a professorship in Experimental Physics (Biophysics) at the University of Würzburg. His research focuses on magnetic resonance imaging (MRI), including co-inventing the FLASH MRI technique and advancing biomedical applications like cardiac and neurological studies. He previously served as President of the University of Würzburg (2003–2009) and President of the European Society of Magnetic Resonance in Biology and Medicine (ESMRMB). Education: Diploma in Physics (1977), PhD (1980) from University of Giessen, Habilitation in Biophysical Chemistry (University of Frankfurt). Leadership Roles: Max Planck Institute of Biophysical Chemistry (1978–1989), Postdoc at University of Oxford (1982). Research Interests: MRI技术创新，包括快速成像技术、医学成像应用、生物医学工程。His work has led to patents and significant advancements in MRI methodologies. Awards: 包括Bavarian Academy of Sciences Fellow (2001)、ISMRM金质奖章 (1991)、DFG Heisenberg Fellowship (1987)等。 Labs & Teams: Director of IMETUM at TUM, leading interdisciplinary research in medical engineering and imaging technologies.
Dr. Daniel Keeser is a Research Fellow and Research Group Leader at the Department of Psychiatry and Psychotherapy of the University of Munich (LMU) and affiliated with the NeuroImaging Core Unit Munich (NICUM). His work focuses on clinical deep phenotyping and multimodal neuroimaging, integrating advanced MRI, EEG, and non-invasive brain stimulation methods to study severe mental and neurological disorders. Research Interests: Elucidating neurobiological mechanisms of schizophrenia, major depressive disorder, and Alzheimer's disease through multimodal neuroimaging and neuromodulation. His recent publications highlight methodologies like resting-state fMRI, diffusion tensor imaging, and transcranial magnetic stimulation combined with MRI, emphasizing personalized treatment strategies. Collaborative affiliations include the University Hospital of LMU Munich and the Clinical Deep Phenotyping (CDP) Working Group. Affiliations: NeuroImaging Core Unit Munich (NICUM) Department of Psychiatry and Psychotherapy, University of Munich (LMU)
Prof. Dr. Florian Knoll is a full professor in Computational Imaging at the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He leads the Computational Imaging Lab, focusing on machine learning applications in medical imaging, particularly accelerating MRI through innovative reconstruction algorithms and translating them into clinical practice. His research emphasizes improving MRI speed, artifact robustness, and accessibility, alongside developing quantitative biomarkers for disease processes. Knoll's work is funded by NIH grants, including projects on machine learning for musculoskeletal imaging, MR fingerprinting, and deep learning frameworks for MRI reconstruction. He is a key figure in open science initiatives, co-creating the fastMRI dataset with Facebook AI, providing public access to over 1300 knee and 7000 brain MRI scans. He currently serves as deputy editor of Magnetic Resonance in Medicine and chairs the ISMRM Reproducible Research Study Group. His contributions extend to reproducible research, maintaining GitHub repositories with code for image reconstruction techniques (e.g., AGILE, gpuNUFFT) and educational materials. He teaches medical imaging fundamentals at FAU, integrating theoretical and practical insights for students and researchers. Grants: NIH R01EB024532, R21EB027241, P41EB017183, R01EB029957 Labs/Teams: Computational Imaging Lab, fastMRI initiative Software: GitHub repositories for MRI reconstruction (e.g., github.com/FlorianKnoll )
Cornelius Faber is a University Professor in the Department of Radiology at the University of Münster, Germany, where he leads the Experimental Nuclear Magnetic Resonance research group. His work focuses on developing and implementing novel MRI techniques that extend the boundaries of magnetic resonance imaging in terms of spatial and temporal resolution, sensitivity, and specificity for physiological, structural, and molecular changes. He actively participates in the "Cells in Motion" interdisciplinary research initiative at the university. Professor Faber's research spans multiple critical areas in medical imaging and biomedical science. His primary expertise lies in MRI cell tracking , enabling visualization of cellular dynamics in vivo. He has made significant contributions to infection imaging , developing methods to detect and characterize microbial infections using MRI. His work on MR methodology development has advanced quantitative imaging techniques, while his research on multimodal integration in MR and MRI contrast mechanisms has provided deeper insights into molecular and cellular processes. His research bridges physics, engineering, and biomedical applications, with particular relevance to inflammation, cancer, neurological disorders, and cardiovascular disease. Analysis of Professor Faber's extensive publication record reveals a clear evolution from fundamental MRI technique development toward increasingly sophisticated applications in disease models. His recent work demonstrates a strong trend toward multimodal imaging approaches that combine MRI with complementary techniques such as mass spectrometry, optical imaging, and PET. This integration creates comprehensive diagnostic platforms that provide both anatomical and molecular information. A notable pattern is the focus on cellular dynamics, particularly immune cell behavior in inflammatory conditions and tumor microenvironments, with applications spanning neuroscience, oncology, and cardiology. Professor Faber leads a multidisciplinary research team of approximately 15 members, including scientists, doctoral students, technicians, and medical students. His laboratory is deeply integrated with the University of Münster's research infrastructure, particularly the Multiscale Imaging Centre. The group's work contributes significantly to advancing preclinical MRI methodologies while maintaining strong clinical relevance, with numerous publications in high-impact journals across medical imaging, neuroscience, and biomedical engineering disciplines.
Bjoern Menze is a Professor and Rudolf Mößbauer Tenure Track Chair at the Technical University of Munich (TUM), leading the Image-based Biomedical Modeling Group within the Munich School of Bioengineering. His research focuses on medical image computing, tumor growth modeling, and computational physiology, with applications in clinical neuroimaging and personalized radiotherapy design. He holds a Ph.D. in Computer Science from Heidelberg University and has held positions at ETH Zurich, INRIA Sophia Antipolis, MIT, and Harvard Medical School. His academic journey includes a postdoc at MIT’s CSAIL and Harvard Medical School, followed by roles at ETH Zurich and INRIA. His work bridges biomedical imaging with machine learning, emphasizing model-driven analysis of physiological processes. He has been a visiting professor at Maastricht University and contributes to initiatives like the Center for Translational Cancer Research at TUM. Key research areas include tumor growth modeling, quantitative imaging biomarkers, and integrating mathematical models with clinical data. His awards include the MICCAI Young Scientist Award (2014), Leopoldina Fellowship (2009), and DFG Research Fellowship (2008). He advises on medical AI, leads interdisciplinary projects, and publishes extensively in top journals like Nature Neuroscience and IEEE Transactions on Medical Imaging. His lab’s work spans applications such as glioblastoma radiotherapy optimization, whole-body bone lesion detection, and neural connectivity imaging. Collaborations include institutions like Harvard, MIT, and ETH Zurich. He emphasizes translating computational methods into clinical practice for personalized healthcare solutions.
Leonid Goubergrits is a Professor of Cardiovascular Modeling and Simulation at the Einstein Center Digital Future and Charité – Universitätsmedizin Berlin . With a background in applied mathematics and physics from the Moscow Institute of Physics and Technology, he has dedicated his career to applying computational fluid dynamics (CFD) to cardiovascular medicine since immigrating to Germany in 1995. His work bridges fundamental research and clinical applications, aiming to integrate numerical models into everyday medical practice to enhance diagnostics and reduce invasiveness. Doctorate at Technische Universität Berlin (2000) Habilitation at Technische Universität Berlin (2016) His research spans blood flow modeling in coronary vessels, cerebral aneurysms, heart valves, and the aorta, alongside artificial organ development and blood damage modeling . He leads a research group at Charité and the German Heart Center Berlin, focusing on patient-specific simulations and their translation to clinical settings. Recent publications highlight his work on deep learning integration for hemodynamic analysis, 4D Flow MRI validation , and medical device optimization using computational models. His team’s research includes virtual therapy planning for aortic valve replacements, hemolysis modeling , and pulmonary artery pressure sensors . Leonid actively contributes to education, redesigning TU Berlin’s Fluid Mechanics in Medicine curriculum and fostering interdisciplinary collaboration between engineers, physicians, and computer scientists. His vision emphasizes the digital transformation of medicine through computational modeling and simulation.
Dr. Markus Zimmermann is a researcher at the Institute of Neuroscience and Medicine (INM-4: Physics of Medical Imaging) at the Research Center Jülich. His work focuses on advancing quantitative MRI techniques, particularly in water content mapping, multiparametric imaging, and ultrahigh-field MRI applications. He contributes to developing methods for eddy current characterization, multi-exponential relaxometry, and rapid whole-brain protocols. His research addresses neurological and medical imaging challenges, including cerebral pathologies and neurobiological implications. Key areas of expertise include MRI parameter estimation, medical imaging algorithms, and the integration of advanced imaging techniques for clinical and neuroscience applications. His projects often involve collaborations to validate methodologies using in vivo/ex vivo experiments and super-resolution reconstruction. Dr. Zimmermann’s work aims to enhance diagnostic precision and understanding of brain physiology through innovative MRI technologies.
Prof. Dimitrios Karampinos is a Professor at the Technical University of Munich (TUM), leading the Experimental Magnetic Resonance Imaging group within the TUM School of Medicine and Health. He specializes in developing novel MRI techniques for quantitative biomarker discovery, focusing on musculoskeletal, metabolic, and oncological applications. His career includes a PhD from the University of Illinois (2008), postdoctoral research at UCSF (2009–2012), and leadership roles at TUM since 2012. Prof. Karampinos has pioneered advancements in MRI reconstruction, signal modulation, and biomarker validation for clinical translation. Educations: BSc in Mechanical Engineering (National Technical University of Athens, Greece), PhD in Biomedical Engineering (University of Illinois, Urbana-Champaign, 2008). Research Interests: Development of MRI measurement techniques, quantitative biomarkers for disease diagnosis, and improving therapy monitoring. Key areas include musculoskeletal disease imaging, metabolic disorder assessment, and oncology applications. His work emphasizes translating research into clinical practice through innovations like accelerated imaging, artifact correction, and AI-driven analysis. Awards: ERC Starting and Proof of Concept Grants (2015, 2019), TUM Supervisory Award (2020), ISMRM Junior Fellow (2011). Grants: Multiple ERC grants for MRI method development. Labs/Teams: Leads the Experimental Magnetic Resonance Imaging group at TUM, collaborating on clinical and technical MRI advancements.
Max Planck Institute for Human Cognitive and Brain SciencesGermany
Harald E. Möller is a Professor and Head of the Nuclear Magnetic Resonance Research and Development Unit at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig. With a career spanning over four decades, he has held academic positions including Honorary Professor at the University of Leipzig and leadership roles in institutions like Duke University Medical Center and the University of Münster. His research focuses on advancing MRI methodologies, biophysical imaging principles, and their applications in neurology and neuroscience. Education: 1979-1985: Chemistry & Physics studies at Universities of Dortmund and Münster 1985: M.Sc. (Diploma) in Chemistry 1988: PhD in Physical Chemistry (summa cum laude) 2000: Habilitation in Physical Chemistry 2002: Habilitation in Biophysical Chemistry Research Interests: Development of novel MRI methods Quantitative tissue characterization Myelin sheath imaging Cerebral blood flow dynamics High-field MRI hardware
Professor Linnea Hesse serves as Head of Department and Head of Research Unit Wood Physics at the University of Hamburg's Faculty of Mathematics, Computer Science and Natural Sciences, Department of Biology, Institute of Wood Sciences. Her research focuses on the biomechanical properties of plant structures, particularly examining how plant form relates to function with applications in biomimetic engineering. Her research interests span plant biomechanics, wood physics, and biomimetics, with particular expertise in advanced imaging techniques for studying plant structures. Dr. Hesse specializes in using magnetic resonance imaging (MRI), micro-computed tomography (µ-CT), and light microscopy to analyze plant tissues, especially in large and opaque specimens that present challenges for conventional imaging methods. Her work bridges botanical science with engineering applications, seeking to understand plant structure-function relationships that can inspire bio-inspired design. Analysis of her publications reveals a consistent focus on plant biomechanics and imaging techniques, with particular emphasis on seed dispersal mechanisms, vascular bundle organization in climbing plants, and methodological approaches for studying plant structures. Her research demonstrates strong interdisciplinary collaboration, particularly with Thomas Speck and colleagues at the Plant Biomechanics Group in Freiburg. Dr. Hesse leads the Wood Physics research unit at the University of Hamburg, where her team investigates plant structural properties with applications in biomimetic design. Her laboratory work combines advanced imaging techniques with biomechanical analysis to understand how plant structures function in their natural environments and how these principles can be translated into engineering solutions.
Professor Martin Bendszus serves as the Medical Director of the Department of Neuroradiology at Heidelberg University Hospital. He has held this position since 2007 and is a leading figure in advanced neuroimaging techniques. His academic career includes medical studies in Bonn, specialized training in Neuroradiology at the University of Würzburg (2003-2007), and a professorship in Neuroimaging at the University of Würzburg. Professor Bendszus's research focuses on innovative imaging methods, particularly in Magnetic Resonance Imaging (MRI). He pioneered Magnet Resonance Neurography for diagnosing peripheral nervous system disorders and made significant contributions to Dental-MRI as a radiation-free diagnostic tool for dental conditions. His expertise also extends to brain and spinal cord diagnostics, including aneurysms, strokes, and arteriovenous malformations. His work bridges clinical practice with advanced imaging research, resulting in numerous high-impact publications across neurology, radiology, and oncology. His publication record shows consistent high-impact contributions, with recent work spanning stroke intervention, neuropathic pain, brain tumor imaging, and advanced MRI techniques. The research trends demonstrate his leadership in translating imaging advances into clinical practice, particularly in time-sensitive neurological conditions where imaging guides critical treatment decisions. Kurt-Decker-Preis Röntgen-Preis Coolidge-Award Lucien-Appel-Award Hermann-Holthusen-Ring der Deutschen Röntgengesellschaft Professor Bendszus leads major multicenter clinical trials and collaborative guideline development efforts, including work with the Response Assessment in Neuro-Oncology (RANO) group and European Association for Neuro-Oncology (EANO). His research has secured significant funding for advancing neuroimaging techniques and their clinical applications. He collaborates extensively with neurologists, neurosurgeons, oncologists, and radiologists to improve diagnostic and therapeutic approaches for neurological conditions. His department serves as a reference center for complex neuroradiological cases and trains the next generation of neuroradiologists. Professor Bendszus maintains active leadership roles in professional societies and regularly contributes to shaping clinical guidelines in neuroimaging and stroke care.
Prof. Fritz Schick is a faculty member at the University of Tübingen , serving as the Deputy Head of the Division 'Pathophysiology of Prediabetes' at the Helmholtz Diabetes Center. He also holds a permanent professorship as Head of the Section on Experimental Radiology within the Department of Diagnostic and Interventional Radiology at Tuebingen University Hospital. His work focuses on developing non-invasive methods to characterize tissue composition and function in pre-diabetes and diabetes. M.D. (1989) and Physics (1990) graduate from University of Tübingen Research Interests : Schick's research spans quantitative MRI and MRS for applications in musculature, liver, adipose tissue, and bone marrow . His work bridges medical physics with metabolic disease analysis , emphasizing non-invasive diagnostics and cohort studies. Publication Trends : Recent articles highlight his expertise in MRI-based quantification of metabolic tissues, including studies on visceral adipose tissue distribution (German National Cohort), hepatokines in NAFLD, and deep learning for diabetes detection from whole-body MRI. Collaborations span radiology, endocrinology, and computational biology. Scientific Awards : Technologie-Transfer-Handwerk Professor Adalbert Seifriz-Preis (2003) Stipend of Siemens AG, Ernst-von-Siemens-Stipend (1992-1994) Dr.-Friedrich-Förster-Award, Department of Physics (1991) Affiliations : Active in the Helmholtz Diabetes Center and German Center for Diabetes Research , with leadership roles in experimental radiology and metabolic imaging.
Max Planck Institute for Human Cognitive and Brain SciencesGermany
Ying Jing is a doctoral researcher at the Max Planck Institute for Human Cognitive and Brain Sciences, affiliated with the Methods and Development Group Brain Networks and the International Max Planck Research School NeuroCom (IMPRS NeuroCom). Her research focuses on advanced neurostimulation techniques, particularly transcranial magnetic stimulation (TMS), combined with functional magnetic resonance imaging (fMRI) to investigate brain networks, motor cortex mapping, and functional connectivity. She explores applications in clinical neuroscience, including TMS-guided therapies for neurological and psychiatric conditions such as depression and motor disorders. Key research interests include neuro-cardiac coupling mechanisms, spatial attention modulation via TMS, and the predictive role of resting-state fMRI in rTMS outcomes. Her work bridges experimental neurophysiology, computational modeling, and clinical translation. She is supported by the China Scholarship Council as a stipend holder. Notably, her studies address discrepancies between task-based activation and TMS hotspots, refine targeting strategies for subthalamic nuclei in rTMS therapy, and model TMS effects on cortical-subcortical circuits. Her contributions advance both fundamental neuroscience and neuromodulation-based interventions. No scientific awards or funded grants are explicitly stated. She collaborates within the Methods and Development Group Brain Networks, contributing to cutting-edge neuroimaging and stimulation methodologies.
