Lyanne M.I. Budé is a doctoral candidate in the Integrated Circuits group within the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). Her research focuses on MRI hardware development, particularly coaxial cable antennas for ultra-high and ultra-low field applications. Education : BSc in Biomedical Engineering (2020), dual MSc in Biomedical Engineering and Electrical Engineering (2023). Research Interests : Antenna design, magnetic resonance imaging (MRI) hardware, electromagnetic field applications in medicine, and biomedical engineering. Her work includes coaxial dipole and monopole antennas for ultrahigh field MRI, gradient coil arrays for ultra-low field MRI, and loop resonators for B1 field homogeneity. She received the Best MSc Thesis Award at TU/e in 2024 and has 5 Scopus citations. Scientific Awards Best MSc Thesis Award, TU/e Academic Awards 2024 Lyanne collaborates with A.J.E. Raaijmakers, I. Zivkovic, and others, contributing to peer-reviewed journals and conferences. Her research addresses challenges in MRI hardware design, including specific absorption rate optimization and transmit-receive array elements.
Paul Chang is a Control/MR Engineer and PhD student at the Max Planck Institute of Biological Cybernetics, working in the High-field Magnetic-Resonance Group since 2013. His doctoral research focuses on real-time feedback B0 shim systems for ultra-high field MRI to improve magnetic field homogeneity and image quality. His educational background includes: MSc in Control Systems from Imperial College London (2011-2012) with thesis on chemical sensing software development BSc (Eng) in Mechatronics from the University of Cape Town (2007-2010) with additional majors in Mathematics and Economics Chang's research integrates control theory, digital electronics, and biomedical engineering to solve MRI challenges. He specializes in fractional/integer PID controllers, real-time field monitoring systems, and embedded controller implementation using FPGA technology. His work addresses critical limitations in ultra-high field MRI related to B0 field inhomogeneity caused by physiological artifacts and hardware limitations. His publication record demonstrates consistent interdisciplinary innovation across biomedical sensing and control systems. Key themes include the development of Parylene C-based pH sensors for neural applications, MRI-guided neurosurgical planning tools, and advanced control algorithms for hydraulic and MRI systems. This reflects a strong pattern of translating theoretical control concepts into practical biomedical solutions with emphasis on real-time system implementation. Chang contributes to the High-field Magnetic-Resonance Group's core mission through hardware development (field cameras, shim amplifiers), software implementation (asymmetric multiprocessor systems on Zynq 7020 boards), and algorithm design for dynamic shimming. His technical expertise spans FPGA programming, Siemens gradient system interfacing, and spherical harmonic function computation for magnetic field correction.
Prof. Jérôme Schmid is a Full Professor (Professeur HES ordinaire) at the Haute école de santé - Genève, part of the Faculty of Health. His expertise spans medical image processing, artificial intelligence (AI), and their clinical applications. He leads innovative projects addressing challenges in diagnostics, surgery planning, and medical education. Key roles include Principal Investigator in grants funded by Swiss National Science Foundation, Swiss Innovation Agency, and others. Research Focus: Combines AI with medical imaging for applications such as Parkinson’s disease detection via SPECT, breast lesion analysis using ultrafast MRI, and AI-driven radiography training tools. Projects emphasize interdisciplinary collaboration with hospitals and industry partners. Projects: DeepDAT (2022–2025): AI for Parkinson’s diagnosis via SPECT imaging. SUBREAM (2022–2025): Rapid breast MRI protocols with AI integration. AIRx (2019–2020): AI-based radiography simulation for student training. MyHip (2012–2014): Patient-specific hip arthroplasty planning. Publications: Focus on AI-driven diagnostics, imaging techniques, and medical education. Recent works address drowning detection via post-mortem CT, multimodal AI fusion for breast cancer, and serious games in radiology training. Grants & Partnerships: Swiss National Science Foundation: FAI analysis via multi-modal imaging. Innosuisse: Low-cost X-ray detectors for developing countries (GlobalDiagnostiX). Swiss Cancer Research Foundation: Breast MRI advancements (SUBREAM). Labs/Teams: Collaborates with the Geneva University Hospitals, EPFL, and industry (e.g., Medacta International SA) on hardware and clinical AI solutions.
İshak Paçal is an Associate Professor at the Department of Computer Hardware, Faculty of Engineering, İğdır University. His research focuses on deep learning applications in medical imaging, including cancer detection, polyp identification, and neurological disorder analysis. He holds a doctorate from Erciyes University (2017-2022), a master's from the University of Nottingham (2015-2016), and a bachelor's from Harran University (2009-2013). Education: Ph.D., Computer Engineering, Erciyes University M.Sc., Electronic Communication and Computer Engineering, University of Nottingham B.Sc., Computer Engineering, Harran University Research Interests: Deep learning in medical diagnostics Applications in MRI, CT, and endoscopic imaging Neural networks for tumor and lesion classification Computer vision for agriculture and infrastructure Recent Contributions: Developed U-Net-based frameworks for brain stroke segmentation Pioneered hybrid ConvNeXt models for skin cancer detection Advanced transformer architectures for lung cancer diagnosis Grants & Projects: Leading a Tübitak-supported project on automated cancer detection Conducted MRI-based cerebral vascular occlusion research Labs/Teams: Medical AI Lab (focusing on endoscopic and radiological image analysis) Computer Vision Applications Group
Bo Zhou is a Research Professor in Radiology at Northwestern University's Feinberg School of Medicine, specializing in medical imaging and artificial intelligence. He holds a PhD from Yale University (2024) and focuses on advancing deep learning techniques for low-dose PET/SPECT imaging, including denoising, attenuation correction, and federated learning applications. His work bridges cutting-edge computational methods with clinical radiology to improve diagnostic accuracy and reduce radiation exposure. Education: PhD in Radiology, Yale University (2024) Affiliations: Member of RSNA, IEEE, American Heart Association, and SPIE Medical Imaging His research interests emphasize developing AI-driven solutions for nuclear medicine challenges, such as optimizing cardiac imaging protocols and enhancing image quality through diffusion models and federated learning. Notable contributions include noise-aware PET denoising frameworks and population-prior-aided networks for low-count imaging. Recent publications highlight advancements in PET reconstruction, federated learning for medical imaging, and motion-correction techniques. He has been recognized with the IEEE Bruce Hasegawa Young Investigator Award (2024) for his contributions to medical imaging science.
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.
Hongyu An is a Professor of Radiology and Professor of Neurology at Washington University in St. Louis, affiliated with the Roy and Diana Vagelos Division of Biology & Biomedical Sciences (DBBS), the Institute of Clinical and Translational Sciences (ICTS), and the Siteman Cancer Center. They lead research in the Division of Radiological Sciences with a focus on developing novel imaging methods to improve clinical diagnosis and patient management. Dr. An's research centers on MR imaging and combined PET/MR imaging with expertise in MRI physics, sequence design, image reconstruction, and deep learning applications. Their work spans MR oxygen metabolic imaging, perfusion imaging, susceptibility-weighted imaging, diffusion MR imaging, and quantitative measurements. The research has significant applications in cerebral small vessel disease and sickle cell disease, with innovations in attenuation correction techniques and motion-compensated MRI. With 154 total research outputs and 5,646 citations, Dr. An has maintained a robust publication record from 1999 to 2025, with increasing output in recent years (12 publications in 2023, 12 in 2024, and at least 7 in 2025). Their work shows strong trends in applying deep learning to medical imaging challenges, particularly in reconstruction algorithms, attenuation correction, and disease-specific applications. Research fingerprint shows 100% concentration in Magnetic Resonance Imaging Significant connections to Oxygen Extraction Fraction (75%), PET-MRI (66%), and Sickle Cell Disease (51%) Work has been mentioned in 21 news outlets and shared across multiple social media platforms Dr. An is available to mentor PhD/MSTP students and has developed several novel techniques including deep learning-based MR-derived pseudo-CT for PET/MR attenuation correction and motion-compensated dynamic contrast-enhanced MRI in the liver, demonstrating practical applications that reduce radiation exposure and improve patient comfort.
Peder Larson is a Professor in Residence at the University of California, San Francisco (UCSF) Department of Radiology and Biomedical Imaging, where he serves as Principal Investigator for The Larson Advanced Imaging Technologies Research Group. Based at the UCSF Mission Bay campus in Byers Hall as part of the Quantitative Biosciences Institute, his research group takes an engineering-driven approach to develop advanced medical imaging methods, primarily focusing on MRI with some work in CT and PET. Dr. Larson's research interests span radio frequency pulse design, pulse sequence development, novel imaging strategies, and optimized reconstruction methods for MRI, with particular emphasis on applications in Hyperpolarized carbon-13 agents and semi-solid tissue imaging with ultrashort echo time (UTE) methods. His group works on metabolic imaging with hyperpolarized MRI for cancer imaging, pediatric lung MRI methods, myelin MRI with ultrashort echo time techniques, quantitative imaging on PET/MRI systems, and AI-based analysis of prostate and kidney cancer imaging data. The team frequently draws on backgrounds in engineering, physics, biology, and chemistry to analyze complex imaging data and provide novel information about tissue structure and function. His research publications and educational presentations from 2017-2019 demonstrate a strong focus on hyperpolarized MRI technology, covering everything from fundamental physics to specific clinical applications in cancer, cardiac imaging, and neurology. His work shows a progression from theoretical foundations to practical implementations across multiple organ systems. As an educator, Dr. Larson has developed significant educational resources including the 'Introduction to Principles of MRI' eBook, edited 'Hyperpolarized Carbon-13 Magnetic Resonance Imaging and Spectroscopy,' and created numerous educational materials, courses, and software tools for MRI education. He teaches UCSF Biomedical Imaging 201: Principles of Magnetic Resonance Imaging and Bioengineering 297: Hyperpolarized MR Seminar. Dr. Larson joined UCSF as a post-doctoral scholar in 2007 under Dan Vigneron, PhD, and became faculty in 2011. He completed his undergraduate and graduate studies at Stanford University with Dwight Nishimura, conducting doctoral research on 'MRI of Semi-solid Tissues.' He emphasizes the importance of understanding patient experiences by having his team volunteer for medical and research imaging themselves, stating 'It is important to understand what your patient and research subject experience is going to be.'
Arvind Pathak serves as Professor of Radiology and Radiological Science, Biomedical Engineering, Oncology, and Electrical Engineering at Johns Hopkins University School of Medicine. He holds cross-appointments at the Sidney Kimmel Comprehensive Cancer Center, Institute for NanoBioTechnology, Institute for Computational Medicine, and Translational Tissue Engineering Center, directing the Image-based Systems Biology Laboratory dedicated to transforming lives through imaging innovation. His academic foundation includes a BS in Electronics Engineering from India's University of Poona, followed by a PhD from the Medical College of Wisconsin and Marquette University's joint Functional Imaging program as a Whitaker Foundation Fellow. He completed postdoctoral training in Molecular Imaging at Johns Hopkins University School of Medicine. Dr. Pathak's research pioneers functional and molecular imaging, image-based biomarkers for precision medicine, and image-informed tissue engineering. His interdisciplinary approach develops hardware, software, and wetware tools to decode disease mechanisms in cancer and neurology, with emphasis on vascular systems biology and tumor microenvironment characterization. The Pathak Lab operates at the engineering-medicine-design intersection to enable biomarker discovery and therapeutic impact. Analysis of his recent publications reveals a cohesive focus on vascular phenotyping in cancer metastasis, multimodal imaging pipeline development, and neurovascular applications. His work bridges cancer biology, stroke research, and tissue engineering through advanced imaging techniques that visualize previously invisible biological processes. Key scientific honors include: Bill Negendank Young Investigator Award (ISMRM) Susan Komen Career Catalyst Award ISMRM Outstanding Teacher Award 125 Hopkins Hero recognition Whitaker Foundation Fellowship He has mentored over 100 award-winning students and junior faculty while leading diversity initiatives. Current lab activities include developing the VascuViz pipeline and miniature microscopes for freely behaving animal studies, with active recruitment of postdocs and graduate students specializing in MRI/CT/optical imaging. The Pathak Laboratory maintains active collaborations across JHU's cancer center and tissue engineering institutes, driving translational projects that convert imaging insights into clinical biomarkers for precision oncology and regenerative medicine.
Rosa Tamara Branca is a Professor at the University of North Carolina at Chapel Hill, leading the Branca Lab. Her research focuses on advancing nuclear spin dynamics and magnetic resonance imaging (MRI) techniques to enhance diagnostic capabilities. She specializes in hyperpolarization methods, low-field MRI systems, and the development of innovative imaging tools for clinical applications. The lab is located in Marsico Hall within the Biomedical Research Imaging Center. Her work integrates physics, engineering, and medicine to improve MRI sensitivity and specificity, particularly through reducing reliance on bulky superconducting magnets. Key areas include xenon-129 MRI, brown adipose tissue imaging, and contrast agent development. She actively seeks students interested in spin physics and biomedical engineering. Publications highlight contributions to ultra-low field NMR, hyperpolarized gas applications, and thermometry. Research emphasizes translating lab innovations into clinical tools for metabolic disorder diagnosis and imaging precision. The lab’s projects often involve interdisciplinary collaborations and open-source hardware development for cost-effective medical solutions.
Li-Ming Hsu is a Research Assistant Professor in the Department of Radiology at the UNC School of Medicine. His work focuses on neuroimaging techniques, particularly functional MRI (fMRI), to study brain networks and their roles in behavior, disease, and addiction. He specializes in translating rodent models to human applications, developing machine learning tools for neuroimaging analysis, and investigating the neurobiological mechanisms underlying brain functions. His research emphasizes understanding addiction mechanisms through rodent models, such as nicotine and cocaine studies, while advancing imaging methodologies like SORDINO and U-Net-based segmentation. He has pioneered techniques like optogenetic fMRI and chemogenetic stimulation to map therapeutic brain circuits. His work bridges fundamental neuroscience with clinical applications, aiming to improve diagnostic tools and therapies for neurological disorders like Alzheimer’s disease and depression. Key Areas: Neuroimaging, functional MRI, addiction models, network neuroscience, machine learning Awards: ISMRM Magna Cum Laude Merit Awards (2020, 2016) Methodologies: Optogenetics, deep learning, rodent-human translation, network redundancy analysis Publications reveal a focus on brain network dynamics in health and disease, with contributions to Alzheimer’s early detection, addiction circuitry, and fMRI methodological improvements. His work emphasizes translational potential, seeking to advance both basic science and clinical outcomes.
Alexander JE Raaijmakers is an Assistant Professor with a joint appointment at Eindhoven University of Technology (Department of Biomedical Engineering, Medical Image Analysis group) and University Medical Center Utrecht (7 Tesla Research group, Radiology Department). His research focuses on RF engineering for ultrahigh field MRI, including antenna design, body imaging, and RF safety protocols. He leads multiple projects involving 5 PhD students and 1 postdoc. Education: MSc in Applied Physics, University of Groningen (2004) PhD in Radiotherapy Physics, University Medical Center Utrecht (2008) Research Focus: His work bridges electromagnetic physics with clinical MRI applications, developing hardware/software solutions for ultrahigh field systems. Key innovations include dipole antenna arrays for MRI and safety frameworks for medical implants. Recent work integrates deep learning for image correction. Awards: Casimir Grant (2008-2010) for RF coil development at Philips Medical Systems Leadership: Directs the 7T Research Group at UMC Utrecht and collaborates on MRI education at TU/e. His team focuses on clinical translation for cardiac, prostate, and abdominal imaging.
Michel Ménard is a Teacher-Researcher at the University of La Rochelle, affiliated with the Mathematics and Computer Science departments. His research focuses on image and signal processing, particularly in cardiovascular imaging, dynamic texture analysis, and UWB radar applications for through-wall imaging. Key projects: ANR DIAMS, FISC consortium, A.Gaugue project Applications: Cardiovascular imaging, environmental monitoring, mobile application programming Research Interests Ménard's work centers on modeling information ambiguity, imprecision, and uncertainty in image analysis, pattern recognition, and information fusion. He has developed generalized fuzzy coalescence methods, non-parametric Bayesian approaches for trajectory analysis, and variational formulations for image filtering inspired by quantum physics. His team focuses on: Dynamic texture modeling via spatio-temporal decomposition Low-level image processing with information theory Through-wall imaging systems using UWB radar Information fusion techniques with minimal a priori assumptions Applications in coastal environment monitoring and biomedical imaging Publications Ménard's publications reflect his expertise in advanced image processing techniques applied to diverse domains. Notable contributions include: Theoretical works on total variation and sublinear functionals Algorithm developments for multistatic radar systems Applications in 3D bee tracking and cardiovascular flow analysis Extensions of Chambolle's algorithm to color images Decomposition methods for dynamic textures Integration of quantum physics concepts in image filtering Collaborations He collaborates with: Laboratoires: L3i, MIA, CLDG/BQR, IRPHE CNRS, ETIS, LASIE Institutions: University Hospitals of Poitiers and Angers, ONERA, LEAT, Tronico Researchers: Abdallah El-Hamidi, Alain Gaugue, Damien Coisne, Gilles Aubert Teaching Ménard teaches across eight departments/programs including: Electronics and Industrial Computing Automation Network Security and Cryptography Video Game Programming Smartphone Programming Digital Media Distribution He has developed new educational initiatives in mobile application programming since 2010.
Prof. Dr. Dilber Polat is a full-time Professor at Kırşehir Ahi Evran University's Faculty of Education, Department of Mathematics and Science Education. She has held progressive academic positions including Assistant Professor (2007-2018), Associate Professor (2018-2024), and Professor (2024-present). She currently serves as Deputy Director of the Graduate School of Natural Sciences and holds multiple administrative roles including departmental chairmanship. Education includes: PhD in Biology Education from Gazi University (2006) Master's in Biology Education from Gazi University (2000) BSc in Biology from Atatürk University (1996) Her research focuses on science education pedagogy, particularly conceptual misconceptions in biology, technology integration (robotics, simulations), and environmental education. She extensively studies teacher training methodologies, STEM applications, and innovative assessment techniques including portfolio evaluation. Publication analysis reveals strong emphasis on: science teacher development, educational technology implementation, environmental awareness studies, and medical education research. Recent works show increased focus on coding pedagogy and climate education. Research projects include: Disaster-based outdoor learning experiences (TÜBİTAK/TÜBA, 2023) Robotics education initiatives (2018-2019) Science education for disadvantaged youth (2010-2012) She has supervised 16 graduate students (2 PhD, 14 Masters) with research spanning science motivation, educational technology, and environmental education.
Prof. Dr.-Ing. Muthuraman Muthuraman holds the Associate Professorship for Computer Science in Medical Technology at the University of Augsburg and maintains a secondary affiliation as Head of the Neural Engineering with Signal Analytics and AI (NESA-AI) group at the Julius-Maximilian University of Würzburg Department of Neurology. Previously, he served as Assistant Professor at Johannes Gutenberg University Mainz (2016-2024) and Senior Postdoc at Christian-Albrechts University Kiel (2013-2016). Research Focus : Mathematical time series analysis of oscillatory signals, biomedical statistics, multimodal neuroimaging (EEG/MEG/fMRI/EMG), structural and functional network analysis from MRI/DTI/PET, machine/deep learning applications, and proteomic-genomic network modeling including RNA/mRNA/Spatial transcriptomics in neurodegenerative and psychiatric disorders. Publication Trends : His recent work emphasizes neuroinflammatory biomarkers in MS, machine learning applications in multimodal data, deep brain stimulation effects on immune dysregulation, and cross-frequency coupling analysis in movement disorders. Scientific Recognition : Awarded as Best Paper of the Year in Brain Topography (2016) for cortical connectivity studies. Labs & Collaborations : Leads the Informatik für Medizintechnik (IMT) group in Augsburg and NESA-AI unit in Würzburg, collaborating extensively with German Competence Network for MS (KKNMS) and international consortia.
