School of Medicine
Showing 601-650 of 687 Results
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Henk van Voorst
Postdoctoral Scholar, Radiology
BioDr. van Voorst is a postdoctoral scholar in Radiology studying the interfaces of artificial intelligence and neuroradiological imaging in stroke. Originally educated as an MD, Dr. van Voorst gained additional degrees in Finance and Data Science. As a PhD student, Dr. van Voorst focused on cost-effectiveness modeling and developed machine learning and deep learning algorithms with applications in acute ischemic stroke imaging. In his current research, Dr. van Voorst develops artificial intelligence algorithms to automatically extract information from arteries and veins in radiological stroke imaging.
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Minal Vasanawala
Clinical Associate Professor (Affiliated), Rad/Nuclear Medicine
Staff, Radiology - Diagnostic RadiologyCurrent Research and Scholarly InterestsPET/MRI, PETCT, and SPECT/CT applications in neuroimaging
Cardiac SPECT/CT and PET/CTA -
Shreyas Vasanawala, MD/PhD
William R. Brody Professor of Pediatric Radiology and Child Health
Current Research and Scholarly InterestsOur group is focused on developing new fast and quantitative MRI techniques.
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Manuela Vasquez
Administrative Associate, Radiology
BioAs an Administrative Associate, I am dedicated to keeping scientists organized, prioritized and less stressed. I practice a streamlined and intuitive approach to assistance.
I am open-arms to opportunities that will broaden my horizon. A team player, willing to lead or follow, eager to learn and is self-motivated. Committed to excellence in service.
My philosophy centers around what role I can play in helping others find solutions. Enthusiastic and personable, my passion is to contribute, support, and make a difference. -
Jaqueline Velazquez
Senior Administrative Associate, Radiology
Current Role at StanfordSupporting RSL Faculty:
Dr. Ennis, PhD
Dr. Setsompop, PhD
Dr. McNab, PhD -
Alexander Michael Vezeridis, MD, PhD
Assistant Professor of Radiology (Interventional Radiology)
BioAlexander Vezeridis MD, PhD is an Assistant Professor of Radiology at Stanford University School of Medicine, and a physician-scientist specializing in Interventional Radiology. His clinical expertise includes interventional oncology, biliary disease and endoscopy, venous disease, portal hypertension, urologic interventions, women’s and men’s health interventions, and general vascular/interventional radiology.
Dr. Vezeridis is an active researcher with expertise in translational techniques in engineering to make image-guided interventions safer and more effective for patients.
Dr. Vezeridis obtained his undergraduate, MD, and PhD degrees from Boston University. He completed a two year post-doctoral training at UC San Diego in ultrasound molecular imaging under the auspices of the Cancer Researchers in Nanotechnology (CRIN) R25T, followed by residency and fellowship at UC San Diego.
Dr. Vezeridis is highly committed to training the next generation, including students, residents, fellows, and engineering graduate students through co-directing Bio301B.
Dr. Vezeridis has a strong interest in medical device development and commercialization, and completed the Stanford Biodesign Faculty Fellowship. -
Davis Vigneault
Clinical Scholar, Radiology
BioDr. Vigneault is a fellow in cardiovascular imaging at Stanford, where he also completed his residency (including a year as chief resident) in diagnostic radiology. Previously, he received his medical degree from Tufts University School of Medicine and his DPhil in Biomedical Engineering from the University of Oxford through the NIH-Oxford Scholars and Medical Scientist Training Programs. For his graduate degree, Dr. Vigneault worked on novel algorithms for measuring regional cardiac function from cardiac CT and MR, publishing in Radiology, Medical Image Analysis, and the Journal of Cardiovascular Magnetic Resonance, among others. In addition to cardiovascular imaging and deep learning, Dr. Vigneault has a strong interest in open science, having been a frequent contributor of software to large open-source libraries such as ITK and related packages.
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Shannon Walters
Executive Technical Director, Radiology - Diagnostic Radiology
Current Role at StanfordI consider myself an innovation enabler and workflow optimization enthusiast. At Stanford 3D and Quantitative Imaging Lab, I work closely with healthcare providers, researchers, and educators to enable effective health visualization. Recent innovations are of particular interest to me; such as 3D Printing, immersive volumetric visualization, clinical implementation of validated AI algorithms, and the general concept of reporting concise changes over time.
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Adam Wang
Assistant Professor of Radiology (Radiological Sciences Laboratory) and, by courtesy, of Electrical Engineering
BioMy research group develops technologies for advanced x-ray and CT imaging, including artificial intelligence for CT acquisition, reconstruction, and image processing; spectral imaging, including photon counting CT (PCCT) and dual-layer flat-panel detectors; novel system and detector designs; and their applications in diagnostic imaging and image-guided procedures. I am also the Director of the Photon Counting CT Lab, Zeego Lab, and Tabletop X-Ray Lab.
I completed my PhD in Electrical Engineering at Stanford, developing strategies for maximizing the information content of dual energy CT and photon counting detectors. I then pursued a postdoctoral fellowship at Johns Hopkins in the I-STAR Lab, developing reconstruction and registration methods for x-ray based image-guided surgery. I was then a Senior Scientist at Varian Medical Systems, developing x-ray/CT methods for image-guided radiation therapy, before returning to Stanford in 2018, where I now lead a comprehensive research program in advanced x-ray and CT imaging systems and methods, with funding from NIH, DOD, DOE, and industry partners. -
Chong Wang
Postdoctoral Scholar, Radiology
BioI am currently a Postdoctoral Scholar in the Department of Radiology at Stanford University School of Medicine, affiliated with the Center for Artificial Intelligence in Medicine and Imaging (AIMI). My research mainly focuses on AI and foundation models in healthcare, with an emphasis on developing trustworthy, robust, and efficient AI solutions for medical imaging. I earned my Ph.D degree, honored with the Doctoral Research Medal, in Computer Science from the Australian Institute for Machine Learning (AIML), The University of Adelaide.
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Jie Wang
Postdoctoral Scholar, Radiology
BioDr. Jie Wang is deeply passionate about magnetic nanotechnology, including magnetic resonance imaging (MRI), magnetic particle imaging (MPI), magnetic nanoparticles (MNPs), magnetic nanofluid hyperthermia (MNFH), magnetic biosensors, etc., for biomedical applications. His dissertation focuses on MRI-guided magnetic hyperthermia for cancer theranostics. Currently, his research interests include developing enzyme-activable nanoparticles for brain cancer theranostics and employing multi-modal imaging modalities to investigate the interaction between nanoparticles and biosystems (nano-bio interaction) within tumor microenvironment.
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Sen Wang
Instructor, Radiology
BioDr. Wang is a research scientist in the Wang group. He previously completed his postdoctoral fellowship in the Wang group and his BS and PhD in the Department of Engineering Physics at Tsinghua University. Sen's research interests focus on technologies and methods for image processing, reconstruction, and recognition, especially in the medical field. His PhD work investigated reconstruction algorithms and applications of x-ray spectral imaging, including photon counting detector modeling and correction, as well as quantitative imaging and computer vision with deep learning on x-ray images and other medical images.
At Stanford, Dr. Wang works on advanced CT detector designs and imaging algorithms. -
Shan X. Wang
Leland T. Edwards Professor in the School of Engineering and Professor of Electrical Engineering and, by courtesy, of Radiology (Molecular Imaging Program at Stanford)
Current Research and Scholarly InterestsShan Wang was named the Leland T. Edwards Professor in the School of Engineering in 2018. He directs the Center for Magnetic Nanotechnology and is a leading expert in Edge AI, biosensors, information storage and spintronics. His research and inventions span across a variety of areas including Edge AI, magnetic biochips, in vitro diagnostics, cancer biomarkers, magnetic nanoparticles, magnetic sensors, magnetoresistive random access memory, and magnetic integrated inductors.
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Ronald D. Watkins
Senior Research Engineer, Rad/Radiological Sciences Laboratory
Current Role at StanfordMy current position is Senior Research Associate in the Radiological Sciences Laboratory and the Molecular Imaging Laboratory in the Department of Radiology at Stanford School of Medicine. I provide hardware, systems, and general technical support for a large group of Faculty members and many students and post docs in the development of advanced medical imaging, metabolic imaging and image-guided interventions. My training background is primarily in RF and electrical engineering. I have spent more than 25 years in the commercial diagnostic imaging industry. Most of the projects I am currently working on involve instrumentation for metabolic imaging using magnetic deuterium imaging. I am also involved in the development of Hybrid systems that combine Positron Emission Tomography and Magnetic Resonance imaging. Other projects I am involved in use focused ultrasound for neuro-stimulation or drug delivery via blood brain barrier. I also provide RF coil design and support for small animal imaging and various research studies. I have ongoing collaborations with many other medical research institutes and Universities around the world. I currently have 45 issued US patents, and 100 pear reviewed publications.
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Kenneth Weber, DC, PhD
Assistant Professor (Research) of Anesthesiology, Perioperative and Pain Medicine (Adult Pain) and, by courtesy, of Neurosurgery (Adult Neurosurgery) and of Radiology (Neuroimaging and Neurointervention)
BioDr. Weber directs the Neuromuscular Insight Lab. Dr. Weber's research seeks to develop quantitative markers of sensory and motor function, including pain, using machine-learning and advanced brain, spinal cord, and musculoskeletal magnetic resonance imaging. Dr. Weber aims to use these techniques to better understand the neuropathology of neurological and musculoskeletal conditions and discover more effective treatments and preventative strategies.
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Philipp Wesp
Postdoctoral Scholar, Radiology
BioI am a postdoctoral researcher investigating interpretable machine learning (ML) and large language model (LLM) applications in clinical radiology. My current research focuses on two complementary areas: understanding what human-interpretable concepts self-supervised vision foundation models learn through mechanistic interpretability techniques like sparse autoencoders, and developing LLM-based systems, including agentic workflows and retrieval augmented generation (RAG) architectures, that leverage unstructured hospital data to improve radiological workflows. I earned my PhD from LMU Munich, where I focused on clinically motivated machine learning applications in medical imaging in the Department of Radiology.
My work is partially funded by a Walter Benjamin Fellowship from the DFG (German Research Foundation). -
McKenzie White
Postdoctoral Scholar, Radiology
BioI work at the intersection of machine learning, medical imaging, and biomechanics. I'm committed to developing tools that bridge gaps between computational methods, musculoskeletal research, and clinical care - enabling more precise analyses, efficient workflows, and improved surgical decision-making.
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Rob W.
3D Biomedical Engineer, Radiology - Diagnostic Radiology
Current Role at StanfordDesigning custom surgical models and patient-specific implants for SHC and LPCH!
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Wesley Williams
Postdoctoral Scholar, Radiology
Current Research and Scholarly InterestsFirstly, a goal of mine is to fashion a novel scatter-based parameter for PET reconstruction algorithms to improve image resolution via determining a more detailed scatter/true ratio estimate via binning the photons that have scattered once, twice, and perhaps, many more times.
Secondly, AI drug discovery application towards radiotracers may quicken experimentation by determining the formulations worth trying. Moreover, it may be able to characterize efficacy (biodistribution) (self-update). -
Emily Kay Wiseman
Casual - Non-Exempt, Radiology - Diagnostic Radiology
Current Role at Stanford3DQ Lab Technologist
