Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.
Douglas C. Noll is the Ann and Robert H. Lurie Professor of Biomedical Engineering and Professor of Radiology at the University of Michigan. He holds key roles as Co-Director of the Functional MRI Laboratory, Co-Lead of the NeuroImaging Core at the Michigan Alzheimer’s Disease Research Center, and collaborator at the Michigan Institute for Imaging Technology and Translation (MIITT). His affiliations include the Michigan Neuroscience Institute, Center for Computational Medicine and Bioinformatics, and Michigan Concussion Center. His research focuses on advancing MRI and fMRI technologies to study brain function and neurological disorders. Key projects include rapid image acquisition, artifact elimination, physiological modeling, and MRI-guided therapies like histotripsy. Recent work emphasizes pre-clinical MRI-guided focused ultrasound systems and collaborations with neuroscientists to map brain organization in health and disease. Notable contributions include developing the Oscillating Steady State Imaging (OSSI) technique, the TOPPE framework for MRI sequence prototyping, and tools like FieldMapNet MRI for off-resonance correction. His lab addresses challenges in high-resolution fMRI, real-time motion compensation, and translational imaging for clinical applications. Current efforts span improving MRI hardware-software integration, advancing non-invasive brain therapies, and applying machine learning to enhance image reconstruction and artifact correction. Collaborations bridge engineering, neuroscience, and clinical medicine to tackle complex neurological conditions like Alzheimer’s and brain tumors.
Yonghyun Ha is an Associate Research Scientist in the Department of Radiology & Biomedical Imaging at Yale School of Medicine. His research focuses on advancing magnetic resonance imaging (MRI) technologies, particularly in low-field MRI systems, RF pulse design, and imaging hardware innovation. He collaborates with experts like Duy Phan, Haifan Lin, and Nikhil Malvankar, contributing to projects such as RF pulse distortion compensation and novel RF coil development. Research Interests: His work spans low-field MRI systems , RF engineering , imaging algorithm optimization , and hardware design . He explores applications like point-of-care imaging and cost-effective MRI solutions. Articles Trends: Recent publications address gradient-free imaging, field-cycling magnets, and deep learning for data compression. His work bridges engineering and clinical needs, emphasizing practical MRI advancements. Advising & Grants: No formal advisees are listed, but his collaborations suggest involvement in interdisciplinary research teams. No specific grants are mentioned, but his projects imply funding through institutional or NIH channels. Labs/Teams: Active in Yale’s Radiology & Biomedical Imaging department, contributing to MRI technology development and translational research initiatives.
Daniela Calvetti is the James Wood Williamson Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. Her research focuses on large-scale scientific computing, computational inverse problems, uncertainty quantification, and predictive modeling in neuroscience, metabolism, and cellular physiology. She holds a PhD from the University of North Carolina-Chapel Hill. Her work integrates advanced mathematical techniques with biomedical applications, including brain energy metabolism modeling, MEG/EEG source reconstruction, and computational methods for medical imaging. Notable contributions include Bayesian hierarchical algorithms for inverse problems and interdisciplinary collaborations bridging mathematics with neuroscience and physiology. Recent research highlights include developing sparsity-promoting Bayesian models for tomography, computational frameworks for neuromuscular control variability, and predictive models of disease dynamics like post-pandemic COVID-19 recurrence. Her methodologies emphasize statistically inspired preconditioning and adaptive meshing techniques to enhance computational efficiency in solving complex inverse problems. Dr. Calvetti has published extensively across computational science, inverse problems, and biomedical applications. She leads a research group advancing interdisciplinary computational methods with applications in neuroscience, virology, and metabolic systems.
Rebecca Feldman is an Assistant Professor in Medical Physics and Physics at the Irving K. Barber Faculty of Science, University of British Columbia Okanagan. Her research integrates MR physics, engineering, and medical research to advance MRI pulse sequences and hardware for clinical translation, particularly in neurological disorders. University: University of British Columbia Okanagan Academic Rank: Assistant Professor PhD: University of Western Ontario Research Interests: Dr. Feldman specializes in technical innovation in MRI (accelerated imaging, spectroscopic imaging, non-proton imaging) and translational research applying MRI to neurological disease detection, characterization, and treatment. Her work leverages ultra-high-field (7T) MRI for enhanced resolution of brain structures like hippocampal subfields and perivascular spaces. Publications: Recent work includes advancements in self-supervised medical imaging backbones (MedMAE), segmentation of venous structures in epilepsy, automated MRI pulse design via neural networks, and clinical applications of 7T MRI in neurosurgical planning and psychiatric disorders like major depressive disorder. Teaching: Currently teaches courses in physics, including physics of waves.
Dr. Chris A. Flask is a Professor at the Case Western Reserve University School of Medicine, with joint appointments in Radiology, Pediatrics, and Biomedical Engineering. He serves as Co-Director of the Imaging Research Core and Associate Director of the Medical Scientist Training Program, while also contributing to the Cancer Imaging Program at the Case Comprehensive Cancer Center. Research Focus Quantitative Magnetic Resonance Imaging (MRI) MRI Physics and Pulse Sequence Design Lung Imaging in Cystic Fibrosis Kidney Imaging in Polycystic Kidney Disease, Sickle Cell Disease, and Diabetic Nephropathy Liver Imaging for Inflammation and Fibrosis Scientific Recognition Distinguished Investigator Award 2023 from The Academy for Radiology and Biomedical Imaging Research Reviewer with Distinction for Magnetic Resonance in Medicine Semi-Finalist for ISMRM Young Investigator Award 2013 His recent publications focus on pH-responsive imaging agents, MR fingerprinting techniques, and applications in pediatric and adult diseases. Dr. Flask's work bridges technical MRI innovation with clinical translation across multiple organ systems.
Ravinder R. Regatte, PhD is a Professor at NYU Grossman School of Medicine , affiliated with both the Department of Radiology and the Department of Orthopedic Surgery . His academic work focuses on advanced MRI techniques for musculoskeletal and metabolic imaging. Key Research Interests: Musculoskeletal MRI Quantitative Imaging Biomarkers Deep Learning for Image Reconstruction MR Fingerprinting Metabolic Profiling in Diabetes Email: Ravinder.Regatte@nyulangone.org
G. Wilson Miller is an Associate Professor of Radiology and Medical Imaging and Biomedical Engineering at the University of Virginia. He holds a PhD in Nuclear Physics from Princeton University (2000) and a BS in Mathematics from the University of Maryland (1993). His research focuses on MRI technique development, hyperpolarized gas imaging, and MR-guided focused ultrasound surgery. Key research areas include: Hyperpolarized noble gas MRI to map lung oxygen levels (PAO2) and microstructure Development of fast spiral pulse sequences for 3D PAO2 imaging RF coil design for hyperpolarized gas imaging MRI-guided focused ultrasound for non-invasive surgical interventions Notable projects include collaborations with the physics department to build a helium-3 hyperpolarizer and studies on focused ultrasound for blood-brain barrier opening. His work has advanced applications in lung cancer treatment planning, COPD phenotyping, and neurological disorders.
University of Texas Southwestern Medical CenterUnited States
Jae Mo Park is an Associate Professor at UT Southwestern Medical Center, affiliated with the Graduate School of Biomedical Sciences and the Department of Biomedical Engineering. He also holds appointments in Electrical Engineering at UT Dallas, where he teaches courses such as Digital Image Processing and Signals and Systems. Education : Electrical Engineering degrees (undergraduate and graduate) from Stanford University; doctoral research on MRI/MRSI for glioma brain tumor metabolism. His lab develops hyperpolarized MRI/MRS methods to assess in vivo metabolic fluxes, focusing on mitochondrial function in diseases like cancer, diabetes, and traumatic brain injury. Research integrates MR pulse sequence design, physiology, and clinical translation. Recent publications highlight hyperpolarized 13C/15N probes for metabolic imaging of the liver, brain, and heart, with applications in fatty liver disease, TBI, and exercise physiology. Awards and grants are not explicitly mentioned, but his work includes collaborations across institutions. Dr. Park teaches courses in neuroimaging, molecular imaging, and metabolic imaging at UT Southwestern and advanced image processing at UT Dallas. The Park Lab investigates metabolic disease characteristics and clinical translation of imaging techniques, with open positions for graduate researchers.
Kevin M. Johnson is an Associate Professor at the University of Wisconsin–Madison with primary affiliation in the Department of Biomedical Engineering and joint appointments in Medical Physics and Radiology within the School of Medicine and Public Health. His laboratory focuses on advancing MRI technologies for quantitative disease assessment. Education: BS in Biomedical Engineering, University of Wisconsin–Madison MS in Medical Physics, University of Wisconsin–Madison PhD in Medical Physics, University of Wisconsin–Madison Research Focus: Dr. Johnson's work centers on overcoming MRI limitations through accelerated acquisition techniques, motion artifact reduction, and novel quantification methods. Key areas include 4D flow imaging, non-Cartesian reconstruction, vascular remodeling analysis, and patient-friendly MRI protocols. His research enables new applications in neurological disorders (Alzheimer's, MS), cardiovascular diseases, and oncological imaging. Publication Trends: Over 50 peer-reviewed articles demonstrate consistent innovation in MRI pulse sequence design, flow quantification, and reconstruction algorithms. Recent work (2015-2016) shows strong emphasis on 4D flow applications in neurovascular diseases, hepatic hemodynamics, and ultra-short echo time techniques for pulmonary and oncological imaging. Advising & Collaboration: As primary advisor in Medical Physics, Dr. Johnson mentors graduate students and collaborates widely across radiology, neurology, and engineering departments. Specific student and grant details are not provided in source materials.
Marco Pizzolato is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Visual Computing with a focus on Magnetic Resonance Imaging (MRI), particularly diffusion MRI and biophysical modeling. He is also affiliated with the inter-departmental Microstructure & Plasticity (MAP) research group and has held visiting positions at the University of Verona, EPFL, and DRCMR. His educational background includes a PhD in Signal and Image Processing from INRIA Sophia Antipolis, a Master’s in Bioengineering from the University of Padua, and a Bachelor’s in Biomedical Engineering from the same institution. He previously served as an Assistant Professor at DTU and was a postdoctoral researcher under the Marie Curie COFUND Eurotech programme. Dr. Pizzolato's research centers on image and signal denoising, inverse problems, optimization, diffusion MRI, tractography, and Monte Carlo simulations. He actively contributes to the development of microstructural models for brain imaging, with applications in neurodegenerative diseases and brain connectivity. His work aligns with UN Sustainable Development Goals, particularly in advancing education and health through imaging technology. The recent publications reflect a strong trend in advancing diffusion MRI techniques, including ACID imaging, microscopic propagator modeling, myelin integrity mapping, and multi-scale white matter organization. These works emphasize biophysical accuracy, model validation, and integration across imaging modalities and species. Magna Cum Laude , ISMRM 2020 Magna Cum Laude , ISMRM 2022 First Place , Macaque Validation Challenge at ISBI 2018 First Place (Overall and HCP) , IronTrack Challenge 2019 (MICCAI) MICCAI Student Travel Award 2015 He has supervised PhD students such as Thøgersen, T. L. and Corral Bolaños, M. in projects related to microstructure MR imaging and myelin mapping. He has also been involved in significant grants and collaborative projects, including the Multimodal Microstructure-Informed Connectivity (MMINCARAV) initiative between Inria and EPFL, and the Sinergia consortium for Brain Communication Pathways . He co-organized multiple international events, including the MICCAI CDMRI workshops and challenges (2019–2021), and the ESMRMB Leaps in Microstructure Imaging workshop (2024). Dr. Pizzolato is an active member of the scientific community, serving as an editor for MICCAI workshop proceedings, a reviewer for major journals and conferences, and an invited speaker at ISMRM 2025. He leads and participates in several ongoing research projects at DTU focused on quantitative imaging, myelin mapping, and MRI-based connectivity, demonstrating sustained research leadership and external funding success.
Dr. Kimberly Chan is an Assistant Professor and FIRST scholar at UT Southwestern Medical Center, jointly affiliated with the Advanced Imaging Research Center and the Department of Biomedical Engineering. Her work bridges biomedical engineering and clinical neuroscience through the development of advanced magnetic resonance spectroscopy (MRS) techniques. Research Interests: Dr. Chan's research centers on the development and application of novel MRS methods to study brain chemistry in neurological and psychiatric disorders. Her work focuses on spectral editing, metabolite quantification (e.g., GABA, glutathione), motion correction, and multi-site reproducibility. She aims to improve diagnosis, disease monitoring, and treatment planning through non-invasive neurochemical profiling. Publication Trends: Her recent publications demonstrate a strong focus on methodological innovation in MRS, including Hadamard encoding (HERMES, HERCULES), multi-metabolite detection, motion correction, and standardization across scanners and sites. These techniques are applied to conditions such as Huntington’s disease, Lafora disease, spinal cord injury, and cancer, showing translational impact. Scientific Awards: First Scholar Advising and Grants: As principal investigator of the CHAN Lab (Chemical Advanced Neuroimaging Lab), she mentors graduate students and postdoctoral fellows. She is actively recruiting and likely holds independent research funding, supported by her FIRST scholar status. While specific grants and advisees are not listed, her leadership role indicates active research supervision and extramural funding. Labs and Teams: Dr. Chan leads the CHAN Lab, a multidisciplinary team of biomedical engineers and imaging scientists focused on advancing MRS for clinical applications. The lab collaborates widely, as evidenced by multi-center studies like Big GABA, involving over 25 research sites globally.
Theresia Ziegs is a Research Associate at the Tübingen Center for Digital Education (TüCeDE) and serves as the link to the AI Makerspace , an extracurricular learning center for innovative technologies. She focuses on developing AI-supported methods for adaptive teaching within the MINT-ProNeD project and coordinates courses on robotics and AI for students, alongside designing teacher training programs in these fields. Education : Dr. rer. nat. in Neuroscience (2017–2023), Max Planck Institute for Biological Cybernetics, Tübingen M.Sc. in 2016, University of Rostock B.Sc. in Physics (2011–2014), University of Rostock Her research centers on neuroscience , particularly using 1H/13C FID-MRSI at ultra-high magnetic fields (9.4T) to study brain metabolism, including glutamate and glucose dynamics . Her work emphasizes methodological optimization for improved reproducibility and spatial resolution in metabolic imaging. Her publications highlight expertise in metabolic mapping , signal processing , and machine learning-enhanced imaging techniques , with applications in human brain metabolism and neuroimaging . She has received the ISMRM Magna Cum Laude Merit Award (2022) for her contributions. Scientific Awards : ISMRM Magna Cum Laude Merit Award (2022) Theresia actively collaborates on AI integration in education and teacher training , bridging advanced neuroimaging research with pedagogical innovation at the University of Tübingen.
Dr. Chang-Hoon Choi is a researcher at the Institute of Neurosciences and Medicine (INM) , Forschungszentrum Jülich GmbH, Germany, with a focus on Medical Imaging Physics (INM-4) . His work bridges Magnetic Resonance Imaging (MRI) , PET-MRI hybrid systems , and RF coil instrumentation for neuroscience applications. Research Highlights: Development of double-tuned coils for 1H/X-nuclei imaging, ultra-high field MRI systems , and MR-PET hybrid technologies . Technical Expertise: Specializes in RF antenna arrays , signal optimization , and multinuclear MRI/MRS for brain studies. Key Article Trends : Over 15 recent publications emphasize coil design innovations (e.g., butterfly, dipole, and birdcage coils), hybrid MR-PET/SPECT systems , and neurochemical dynamics via tDCS-MRS integration and phosphorus/sodium imaging . Methodological advances include free water elimination in diffusion MRI , quantum filtering , and shielding techniques for UHF-MR systems . Applications : His work targets stroke , epilepsy , brain tumors , and neuroplasticity studies using preclinical animal models (rat, chick embryo) and translational hardware (e.g., 9.4T systems).
Jon-Fredrik Nielsen is a Research Professor in the Departments of Radiology and Biomedical Engineering at the University of Michigan. His research focuses on advanced MRI technologies, including pulse sequence design, functional MRI, and quantitative imaging. He leads projects funded by NIH grants such as R21AG061839, R01EB023618, and R21EB019653, emphasizing innovations in MRI hardware and software. Research Interests: Steady-state MRI and RF pulse design Functional MRI and biomarker development Blood flow imaging and computational modeling Publications reflect contributions to open-source MRI frameworks (Pulseq/TOPPE), artifact correction, and novel imaging protocols. He holds patents related to MRI imaging techniques (e.g., US 9,791,530). Grants: NIH R21AG061839 (PI), NIH R01EB023618, NIH R21EB019653, and University of Michigan MCubed grants. Projects include improving fMRI reliability and developing vendor-agnostic MRI sequences. Labs/Teams: Active in the fMRI engineering group, contributing to software tools like TOPPE and Pulseq-Graphical Programming Interface.
