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.
Justin P. Haldar is a Professor in the Ming Hsieh Department of Electrical and Computer Engineering at the University of Southern California (USC), with a joint appointment in the Department of Biomedical Engineering. He co-directs the Biomedical Imaging Group and serves as Director of the Signal and Image Processing Institute. His affiliations include the Dornsife Cognitive Neuroscience Imaging Center, the Brain and Creativity Institute, and the Dynamic Imaging Science Center. Education : B.S. and M.S. in Electrical Engineering (2004, 2005), Ph.D. in Electrical and Computer Engineering (2011) from the University of Illinois at Urbana-Champaign. His research focuses on computational imaging, inverse problems, and magnetic resonance imaging (MRI), with an emphasis on constrained image reconstruction, parameter estimation, and novel data acquisition strategies. His work combines physical modeling, high-dimensional signal structures, and fast computational algorithms to address MRI's limitations in speed, noise, and cost. Recent publications analyze challenges like the 'hidden noise' problem in MR reconstruction (2025) and innovations in dynamic imaging. His research has enabled faster MRI exams and next-generation imaging techniques by exploiting dimensionality's 'blessings' while mitigating its 'curses.' Scientific awards : NSF CAREER Award (2014) IEEE ISBI Best Paper Award (2010) IEEE EMBC First-Place Student Paper Award Haldar's leadership roles include Chair of the IEEE Signal Processing Society's Technical Committee on Computational Imaging and editorial positions at IEEE Transactions on Computational Imaging and Magnetic Resonance in Medicine . He actively mentors students and develops novel MRI approaches at USC's Michelson Center for Convergent Bioscience.
Amir Shmuel is a Professor at McGill University's Faculty of Medicine, holding appointments in the Department of Neurology and Neurosurgery, Department of Biomedical Engineering, and Department of Physiology. He serves as Director of the Brain Imaging Signals Lab and Core Faculty at the McConnell Brain Imaging Centre of the Montreal Neurological Institute. His leadership includes chairing the 2018 International Society for Brain Connectivity conference and securing an $18.7M Canada Foundation for Innovation grant for Quebec's first large-bore 7 Tesla MRI scanner. Dr. Shmuel's research focuses on understanding neuronal mechanisms underlying functional brain imaging signals and visual information processing. His integrative approach combines fMRI, optical imaging, multi-channel neurophysiological recordings, and optogenetics across multiple spatial and temporal scales. Current research priorities include resting-state functional connectivity mechanisms, cortical lamina-resolved neurophysiology, and computational modeling of brain signals. His lab emphasizes parallel model development with experimental data acquisition. Recent publications demonstrate strong trends in multimodal neuroimaging integration, with emphasis on high-resolution fMRI techniques (especially 7T applications), resting-state connectivity analysis across species, and computational modeling of neurovascular coupling. Key subfields include laminar-specific activity mapping, artifact detection in medical imaging using deep learning, and cross-species functional connectivity frameworks. Dr. Shmuel's research is currently funded by the Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council of Canada (NSERC), and the US Department of Defense. His lab maintains active collaborations through initiatives like the International Society for Brain Connectivity and the PRIME-DE database consortium. Operating within the Brain Imaging Signals Lab at the Montreal Neurological Institute, Shmuel's team develops and applies advanced multimodal techniques including simultaneous fMRI-electrophysiology, voltage-sensitive dye imaging, and computational modeling frameworks. The lab maintains strong ties with the McConnell Brain Imaging Centre and participates in major open-science initiatives including the Tanenbaum Open Science Institute.
Ina Fichtner is a Professor at the Faculty of Digital Transformation of University of Applied Sciences HTWK Leipzig since 2022. Previously, she led the MINT department at the Institute for Applied Training Science (IAT) in Leipzig for 13 years (2009–2022), focusing on integrating mathematics, informatics, and natural sciences into sports research. Her work bridges computer science , biomechanics , and sports informatics , with extensive projects on athlete movement analysis, data systems (IDA), and digital tools for elite sports. PhD in Computer Science (2007) from TU Dresden and Leipzig University Diplom in Mathematics and Computer Science (2002) from Jena, Dresden, and Sheffield Her research spans data science , sports technology , and applied informatics , particularly in ski jumping , dive analysis , and athlete biomechanics . She has co-authored numerous publications in theoretical computer science and applied sports informatics , including studies on 3D body scanning , inertial sensors , and force-velocity profiling . She served as Alumni Representative and Treasurer of the Friends' Association at HTWK Leipzig, with memberships in German Mathematical Society and German Sports Science Association .
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.
Edward V. DiBella, PhD, is a Professor in the Department of Radiology & Imaging Sciences at the University of Utah School of Medicine, where he serves as director of the Utah Center for Advanced Imaging Research (UCAIR). He holds adjunct faculty appointments in Bioengineering and is affiliated with the Center for Arrhythmia Research and Management (CARMA) and the Experimental Therapeutics Program in the Huntsman Cancer Institute. Dr. DiBella leads the Cardiovascular MRI Group, focusing on developing advanced imaging techniques for cardiac applications. Dr. DiBella's educational background includes a Master's degree from the University of Vermont and a PhD from the Georgia Institute of Technology, followed by postdoctoral training at the University of Utah Department of Radiology. His research spans over two decades of contributions to medical imaging science. His primary research interests center on improving MRI acquisition, reconstruction, and post-processing techniques, with particular emphasis on cardiac, cancer, and stroke applications. Dr. DiBella's work addresses fundamental challenges in medical imaging including motion artifacts, image reconstruction from undersampled data, quantitative perfusion analysis, and advanced techniques for cardiac imaging. His group has pioneered radial simultaneous multi-slice (SMS) approaches for myocardial perfusion MRI that enable whole-heart coverage without gating. Analysis of Dr. DiBella's recent publications reveals a strong focus on deep learning applications in MRI reconstruction, quantitative myocardial perfusion techniques, and advanced diffusion imaging for stroke recovery prediction. His work demonstrates a consistent trajectory toward more efficient, accurate, and clinically applicable imaging methods that address real-world challenges in cardiac MRI. Dr. DiBella has mentored numerous graduate students and postdoctoral researchers who have gone on to successful careers in academia and industry. His laboratory maintains active collaborations with cardiology, electrophysiology, and bioengineering departments, reflecting the interdisciplinary nature of modern medical imaging research. Current projects in Dr. DiBella's Cardiovascular MRI Group include Deep Learning for Radial SMS Reconstruction, Diffusion imaging for predicting stroke recovery, Quantitative myocardial perfusion, and Radial SMS for myocardial perfusion MRI. The group's work is particularly relevant to heart failure, coronary artery disease, and atrial fibrillation diagnosis and management, addressing the fact that heart disease remains the leading cause of death worldwide.
Professor Jared Tanner is Professor of the Mathematics of Information at the University of Oxford's Mathematics Institute and a Fellow of Exeter College. Previously, he held positions at the University of Edinburgh (2007-2012) as Professor, Reader, and Lecturer in Mathematics, University of Utah (2006-2007) as Assistant Professor, and Stanford University (2004-2006) as an NSF Postdoctoral Fellow. His research focuses on extracting models from high-dimensional data to reveal essential information, with specific contributions including sampling theorems in compressed sensing using stochastic geometry, efficient algorithms for matrix completion, and theoretical understanding of deep neural networks. Recent interests include neural network initialization techniques to preserve geometric and information-theoretic properties, as well as network pruning methods. Professor Tanner has supervised numerous doctoral students at Oxford and Edinburgh, including Alireza Naderi, Thiziri Nait Saada, Ilan Price, Giuseppe Ughi, Charles Millard, Michael Murray, Simon Vary, Bernadette Stolz, Bogdan Toader, Rodrigo Mendoza-Smith, Ke Wei, Bubacarr Bah, and Andrew Thompson, many of whom have gone on to prestigious positions in academia and industry. His publication record spans over two decades with significant contributions to compressed sensing, matrix completion, and more recently deep learning theory. His work demonstrates a consistent progression from foundational theoretical work to practical applications in signal processing and machine learning. As an academic leader, Professor Tanner serves as Founding Editor-in-Chief of Information and Inference: A Journal of the IMA and has held editorial positions at several prestigious journals including Applied and Computational Harmonic Analysis and IEEE Signal Processing Letters . He has organized numerous conferences and workshops including Prospects in Mathematics and the FoCM Computational Harmonic Analysis workshop.
Douglas Noll is the Ann and Robert H. Lurie Professor in the Department of Biomedical Engineering at the University of Michigan , with additional appointments as Professor of Radiology and affiliate of multiple research institutes including the Michigan Alzheimer’s Disease Research Center, Michigan Neuroscience Institute, and Michigan Institute for Imaging Technology and Translation (MIITT). He co-directs the Functional MRI Laboratory and leads the NeuroImaging Core at the Michigan Alzheimer’s Disease Research Center. Prof. Noll’s research focuses on MRI technology development , particularly for functional MRI (fMRI) . His group specializes in high-speed MRI acquisition , signal processing , and image reconstruction to map brain function. Key areas include artifact elimination , physiological modeling , and quantitative imaging for neurological and psychiatric disorders. Recent collaborations apply MRI to histotripsy therapy monitoring. The 15 most recent articles reflect his work in dynamic MRI reconstruction , artifact correction , and cross-vendor protocol standardization . Topics span B0 shimming , histotripsy targeting , and non-Cartesian sampling , emphasizing spatiotemporal modeling and machine learning integration for accelerated imaging. Scientific Awards : Ann and Robert H. Lurie Professor. Research Collaborations : Functional MRI Laboratory, Michigan Alzheimer’s Disease Research Center, Michigan Institute for Imaging Technology and Translation (MIITT).
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
Dr. Daniel Sodickson is a Professor of Radiology, Neuroscience and Physiology, and Biomedical Engineering at New York University. He serves as Vice Chair for Research in Radiology and directs the Bernard and Irene Schwartz Center for Biomedical Imaging and the Center for Advanced Imaging Innovation and Research. His work focuses on advancing biomedical imaging techniques to improve healthcare, particularly in MRI, PET, and CT technologies. He leads multidisciplinary teams developing methods for rapid, continuous imaging leveraging parallel imaging, compressed sensing, and AI. Dr. Sodickson’s research interests include AI-driven medical imaging, low-field MRI applications, and novel imaging sensors. He co-directs Tech4Health, fostering translational healthcare technologies. Notably, he pioneered the fastMRI initiative, an open dataset and benchmarks for accelerated MRI using machine learning. Key achievements include developing the fastMRI public dataset for knee and prostate imaging, and contributions to global AI events like the World’s Largest AI for Business Summit. His work addresses challenges in imaging speed, cost, and accessibility, with a focus on clinical translation. Dr. Sodickson has received prestigious recognition, including being named a Fellow of the National Academy of Inventors. His lab collaborations and grants emphasize interdisciplinary innovation, aiming to bridge imaging technology with clinical practice.
Dandan Liu serves as Associate Professor of Biostatistics at Vanderbilt University Medical Center, holding dual leadership roles as Executive Director of the Vanderbilt Biostatistics Data Coordinating Center (VBDCC) and Director of the Vanderbilt Institute for Clinical and Translational Research (VICTR) Methods Program. Her work bridges statistical methodology development with clinical and translational research across multiple disciplines. Her educational foundation includes a PhD in Biostatistics from the University of Michigan, providing the theoretical basis for her methodological innovations. Dr. Liu's research program demonstrates exceptional breadth, with core expertise in longitudinal data analysis for neurodegenerative diseases—particularly Alzheimer's progression modeling using cognitive and neuroimaging biomarkers. She maintains parallel research streams in parasitology (focusing on Eimeria and Toxoplasma pathogenesis in poultry) and environmental statistics (carbon footprint assessment and sustainable systems modeling). Her methodological contributions span predictive modeling for ordinal outcomes, robust signal processing techniques, and high-dimensional 'omics data analysis. Analysis of her 2023-2025 publications reveals three dominant thematic clusters: neurodegeneration research (comprising 40% of output, featuring amyloid biomarker studies and cognitive trajectory modeling), parasitology applications (35%, emphasizing vaccine development and infection dynamics), and environmental sustainability (25%, including geospatial optimization and life cycle assessment). This distribution reflects her strategic focus on high-impact biomedical problems while maintaining methodological versatility. No scientific awards were documented in the provided source material. While specific doctoral advising activities and grant portfolios weren't detailed, her extensive publication record across diverse domains indicates active mentorship of research teams and successful acquisition of collaborative funding through VBDCC and VICTR channels. She directs two major research infrastructure units: the Vanderbilt Biostatistics Data Coordinating Center (VBDCC), which provides statistical leadership for multi-center clinical trials, and the VICTR Methods Program, which develops innovative approaches for translational research design and analysis. These centers facilitate cross-departmental collaboration between biostatisticians and domain scientists across Vanderbilt's medical and engineering schools.
Jarmo Malinen is a Lecturer at the Department of Mathematics and Systems Analysis, Aalto University , School of Science. He leads the speech modeling group and participates in the Comspeech consortium, collaborating with institutions like the University of Helsinki and Turku University Hospital. His research spans mathematical systems theory, numerical analysis, and computational speech science, focusing on MRI-based vocal tract modeling, glottal flow dynamics, and speech acoustics. He develops physical models for speech production and contributes to educational technologies like the STACK system. Key publication trends include applications of finite element methods to vowel formants, inverse filtering for glottal flow estimation, and eigenvalue problem solutions for coupled systems. His work intersects applied mathematics, biomedical imaging, and speech physiology. Malinen advises master's and PhD students in mathematical modeling and speech research, though specific student names are not listed in the provided texts. He has developed tools for MRI data post-processing and vocal tract resonance optimization.
Jérôme Yerly serves as a Research Staff Scientist at the Translational MR Imaging Section of the Center for Biomedical Imaging (CIBM), jointly affiliated with Lausanne University Hospital (CHUV) and the University of Lausanne (UNIL). His work focuses on translating advanced MRI methodologies into clinical diagnostics and therapeutic assessment. His academic credentials include: Bachelor in Electronic Engineering from University of Applied Sciences of Western Switzerland, Fribourg (2004) MSc and PhD in Electrical and Computer Engineering from University of Calgary Dr. Yerly specializes in developing nonlinear reconstruction techniques to enhance cardiac and neuroimaging applications. His research leverages compressed sensing and parallel imaging to accelerate scan times while improving spatial and temporal resolution. Current projects target coronary artery disease assessment through coronary endothelial function imaging, extending his doctoral work on stroke neuroimaging where he pioneered sparse acquisition strategies for rapid MRI. No information is documented regarding student supervision or research grant funding. He operates within CHUV's Department of Diagnostic Radiology and Interventional Radiology as part of CIBM's collaborative network, which integrates École Polytechnique Fédérale de Lausanne (EPFL), University of Lausanne, CHUV, and Geneva University Hospitals to advance biomedical imaging innovation.
Martin Uecker is a Professor at the Institute of Biomedical Imaging at TU Graz. His research focuses on advanced MRI reconstruction techniques, real-time imaging, and open-source software tools like the Berkeley Advanced Reconstruction Toolbox (BART). He specializes in developing methods for fast and accurate medical imaging, including applications in cardiac MRI, fetal brain imaging, and disease monitoring. His work emphasizes reproducibility, quantitative imaging, and clinical translation. Key research areas include generative models for MRI reconstruction, model-based inversion of the Bloch equations, and interactive real-time MRI systems. His team collaborates on projects involving hardware-software integration, such as portable MRI scanners and MRI-guided interventions. Notable contributions include advancements in multi-echo radial FLASH techniques, motion-resolved T1 mapping, and Bayesian uncertainty estimation in imaging. Uecker’s publications highlight innovations in accelerating MRI acquisition and reconstruction, with applications in pulmonary function assessment, neonatal imaging, and cardiovascular diagnostics. His work bridges theoretical physics, computational methods, and clinical practice, fostering open-source frameworks to democratize access to cutting-edge imaging tools.
Stephen J. Riederer, Ph.D., is a Professor of Radiology at Mayo Clinic, holding dual appointments in the Department of Radiology and the Department of Physiology & Biomedical Engineering. He leads the Magnetic Resonance Laboratory, focusing on advancing MRI physics and clinical applications. His research emphasizes high-resolution prostate MRI, super-resolution T2SE imaging, and contrast-enhanced magnetic resonance angiography (CE-MRA). Dr. Riederer has developed fast-scanning techniques, real-time signal processing, and parallel acquisition methods, many of which are now industry standards. Education: B.A. in Mathematics, University of Wisconsin-Madison SM in Nuclear Engineering, MIT Ph.D. in Medical Physics, University of Wisconsin-Madison Research Interests: Dr. Riederer’s work bridges MRI physics and clinical implementation. Key areas include: Prostate cancer imaging via high-resolution T2SE and DCE-MRI Super-resolution MRI for improved anatomic detail Real-time MRI scanning and interactive triggering Parallel acquisition techniques and coil array optimization Publications & Impact: Over 300 peer-reviewed articles highlight his contributions to MRI innovation. Recent work focuses on AI-driven prostate MRI quality assessment and coil array improvements. His methods are widely adopted in commercial MRI systems. Awards & Leadership: Gold Medal (International Society for Magnetic Resonance in Medicine, 2002) President, Society of Magnetic Resonance Angiography (2008) George M. Eisenberg Professor I, Mayo Clinic (2024) Advising & Grants: Mentor to over two dozen doctoral students. Active in training via courses at the Mayo Clinic Graduate School. Leads grants on prostate MRI super-resolution and spatiotemporal imaging, funded by NIH and the U.S. Army. Labs & Affiliations: Part of the Center for Advanced Imaging Research, collaborating across radiology, biomedical engineering, and oncology. Facilities include state-of-the-art MRI scanners and imaging laboratories.
