School of Medicine
Showing 11-20 of 89 Results
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Pere Canals
Postdoctoral Scholar, Radiology
Current Research and Scholarly InterestsI have deep interest in studying how to leverage state-of-the-art AI advancements in the field of computer vision to develop applications with a practical use in day-to-day workflows in stroke treatment and other neurovascular diseases. For example, I want to study how we can use foundation models in 3D imaging to unlock novel applications with a real impact on stroke care. My main expertise has resided in understanding how complex vascular anatomies impact endovascular treatment in stroke.
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Carlos Castillo Passi
Postdoctoral Scholar, Radiological Sciences Laboratory
BioCarlos Castillo-Passi began his academic journey at Pontificia Universidad Catolica de Chile (PUC), where he earned both a degree and an MSc in Electrical Engineering in 2018. He then pursued a PhD in Biological and Medical Engineering through a joint program between PUC and King’s College London (KCL), completing it with maximum distinction in 2024. His research focused on the design of low-field cardiac MRI sequences using open-source MRI simulations. In 2023, his work on open-source MRI simulations was highlighted by the editor of Magnetic Resonance in Medicine (MRM). Furthermore, his application of this work to low-field cardiac MRI earned him the Early Career Award in Basic Science from the Society for Cardiovascular Magnetic Resonance (SCMR) in 2024. In addition to his research, Carlos is an active member of JuliaHealth, contributing to the development of high-performance, reproducible tools for health and medicine. In 2025, he joined Stanford University as a postdoctoral researcher, where he continues his work in cardiac MRI and open-source technologies.
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Rafail Christodoulou
Postdoctoral Scholar, Radiology
BioRafail Christodoulou, is an analytically minded medical doctor with a profound interest in neuroradiology and neurointerventional radiology. He graduated with an excellent average mark from the University of Patras, Greece, where he deeply engaged with the fields of radiology and interventional neuroradiology. His robust clinical experience includes completing electives and observerships at premier medical institutions in the United States, reinforcing his commitment to delivering patient-centered care. Rafail's approach is characterized by compassion and an ability to communicate effectively with diverse patient groups. Beyond his clinical pursuits, Rafail is actively involved in research, focusing on the application of artificial intelligence in diagnosing and managing brain tumors and dementia. His research aims to enhance diagnostic accuracy and patient outcomes, reflecting his dedication to advancing medical science. Additionally, Rafail maintains a balanced lifestyle with interests in basketball, martial arts, strength training, and swimming, underscoring his commitment to personal and professional well being.
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Ian Coates
Postdoctoral Scholar, Radiology
Trainer, Stanford Nano Shared Facilities Service CenterBioI am a chemical engineer advancing photopolymerization chemistry, fluid mechanics, and materials science to enable fabrication strategies once thought impossible. Pioneered injection Continuous Liquid Interface Production (iCLIP), using active resin chemistry and fluid–optical coupling to achieve order-of-magnitude gains in 3D printing speed and resolution, and translated chemical control of reactive interfaces into free-form microfluidic microneedle systems for intradermal delivery of small molecules, biologics, and mRNA. Current research applies water-soluble biocompatible sacrificial resins and projection-based fabrication workflows to design and print high-resolution, perfusable microvascular architectures for integration into 3D tissue patches.
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Tyler Edward Cork
Postdoctoral Scholar, Radiological Sciences Laboratory
Current Research and Scholarly InterestsCurrently, I am involved in two main projects. The first is developing 3D printing techniques to improve the accuracy of ex vivo geometrical and microstructural cardiac modeling from in vivo cardiac MR acquisitions. The second is applying machine learning applications to MRI data as a way to improve overall image quality and reduce acquisition time.
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Meysam Dadgar
Postdoctoral Scholar, Molecular Imaging Program at Stanford
BioMeysam Dadgar is a Postdoctoral Research Fellow at the Molecular Imaging Program at Stanford University, School of Medicine. He obtained his Ph.D. in Biophysics from Jagiellonian University in Kraków, Poland, as part of the J-PET collaboration, and previously held a Postdoctoral Fellowship at Ghent University, Belgium.
Dr. Dadgar’s research focuses on the development and optimization of next-generation positron emission tomography (PET) systems for cancer detection and precision medicine. His expertise spans PET instrumentation, Monte Carlo and GATE simulations, advanced image reconstruction, and AI-based image enhancement. He has made significant contributions to the design and evaluation of novel PET geometries, including dual-panel and total-body PET, as well as positronium imaging approaches that extend beyond conventional PET capabilities.
In addition to his PET-focused research, Dr. Dadgar gained unique experience at CERN, where he worked on advanced detector development, including trigger electronics, PET coincidence measurements, and composite material production under controlled conditions. These experiences provided him with a strong foundation in detector design, fabrication, and calibration that complements his biomedical imaging research.
He has authored more than 20 peer-reviewed publications, including papers in Science Advances, Nature Communications, IEEE Transactions on Radiation and Plasma Medical Sciences, and EJNMMI Physics. He has been recognized with multiple international fellowships and awards, including IEEE NSS/MIC Trainee Grants and national research grants in Europe.
At Stanford, Dr. Dadgar’s work integrates state-of-the-art medical imaging technologies, particle physics methods, and AI-driven modeling to improve sensitivity, resolution, and diagnostic accuracy in PET, with the ultimate goal of advancing early cancer detection and patient care.
