People

Kawin Setsompop is a Professor of Radiology and, by courtesy, of Electrical Engineering. His research focuses on the development of novel MRI acquisition methods, with the goal of creating imaging technologies that can be used to help better understand brain structure and function for applications in Healthcare and Health sciences. He received his Master’s degree in Engineering Science from Oxford University and his PhD in Electrical Engineering and Computer Science from MIT. Prior to joining Stanford, he was a postdoctoral fellow and subsequently a faculty at the A.A. Martinos center for biomedical imaging, MGH, as well as part of the Harvard and MIT faculty. His group has pioneered several widely-used MRI acquisition technologies, a number of which have been successfully translated into FDA-approved clinical products on Siemens, GE, Phillips, United Imaging and Bruker MRI scanners worldwide. These technologies are being used daily to study the brain in both clinical and neuroscientific fields.

Nan Wang is an Instructor in the Radiological Sciences Laboratory in Dr. Setsompop's lab. She received her PhD degree in Bioengineering Department from UCLA in 2020 and worked as a postdoctoral scientist at Cedars-Sinai Medical Center before joining Dr. Setsompop's Lab. Nan has been particularly interested in the development of fast acquisition/reconstruction techniques to improve the performance of MRI. She developed the multitasking dynamic contrast enhanced MRI technique that is able to quantitatively differentiate diseased tissues from normal in multiple organs with reduced dose, high spatiotemporal resolution, and motion-resolved acquisition. She will continue to work on the development of fast multi-parametric MRI techniques.

Ariel Hannum is a PhD student at Stanford University, in the department of Bioengineering. She is interested in MRI image acquisition and reconstruction for cardiac applications. Her current research focuses on characterizing motion-induced errors in multishot diffusion MRI and developing correction strategies. She is co-advised by Professor Daniel Ennis from the department of Radiology/RSL.

Mahmut Yurt is a PhD student in the Department of Electrical Engineering at Stanford University. Before joining Stanford, he received his MSc (2021) and BSc (2019) degrees from Bilkent University, in Turkey, both in Electrical Engineering. His current research interests focus on developing reconstruction, synthesis, and denoising techniques for accelerated magnetic resonance fingerprinting, as well as sequence design and sampling mask optimization for clinical contrast-weighted imaging. 

Itamar Terem is a PhD student, in the department of Electrical Engineering at Stanford University, and an NSF Graduate Research Fellow. His current research focuses on the development of computational and acquisition techniques to explore the brain tissue mechanical response and clearance mechanician to blood pulsation and cerebrospinal fluid (CSF) motion.

Mengze Gao is a Ph.D. student in Biomedical Physics at Stanford University. Before joining Stanford, he received his B.E. degree from Tsinghua University in 2022. His current research focuses on developing robust reconstruction and distortion correction techniques for fast MRI.

Daniel Abraham is a PhD student in Electrical Engineering at Stanford University. Before starting his PhD, Daniel received his B.S. in Electrical Engineering and Computer Sciences at UC Berkeley. He is currently interested in developing new reconstruction techniques for accelerated MRI.

Yonatan Urman is a Ph.D. student at the Department of Electrical Engineering at Stanford University. Before joining Stanford, he earned his MSc degree in Electrical Engineering from Tel-Aviv University and dual BSc degrees in Electrical Engineering and Physics from the Technion, Israel Institute of Technology. He is also the recipient of the Stanford Graduate Fellowship in Science & Engineering (SGF). His research interests revolve around fast and efficient MRI reconstruction and acquisition techniques, with the aim of making MRI faster, more robust, and widely available.

Zachary Shah is a Ph.D. student at the Department of Electrical Engineering at Stanford University. He earned his M.S. degree in Electrical Engineering from Stanford in 2024 and previously completed his B.S. in Biomedical Engineering at Georgia Institute of Technology in 2022. His research focuses on signal processing and machine learning applied towards various tasks in MRI, from deep learning-based reconstruction for accelerated imaging to system imperfection modeling and calibration.

Yimeng Lin is a Ph.D. student in Electrical Engineering at Stanford University. Before starting her Ph.D., Yimeng received her B.S. degree in Biomedical Engineering from Tsinghua University. Her current research focuses on optimizing MRI acquisition and reconstruction by leveraging redundant information to achieve fast imaging with improved and robust image quality.

Aiza is a PhD student in Biomedical Physics admitted autumn 2022. She currently works in Setsompop lab on prospective motion correction for brain MR Fingerprinting. Before joining Stanford, Aiza received B.Sc. from Moscow Institute of Physics and Technology (2019) and M.Sc. from Technical University of Munich (2022).

Jaehyeok Bae is a PhD student in the Department of Electrical Engineering at Stanford University. He received his B.S. degree in Electrical Engineering from Seoul National University in 2023. His research interests include optimizing data acquisition and image reconstruction using deep learning-based accelerated MRI techniques.

Rachel Hollett is a master’s student in the Department of Electrical Engineering at Stanford University. She completed her B.S. degree in Electrical Engineering at Stanford University in 2025. Her research focuses on optimizing image acquisition and reconstruction for accelerated MRI.

Xiaozhi Cao is a Research Engineer for the Department of Radiology/RSL at Stanford University. Previously, he was a postdoctoral fellow in the lab of Dr. Setsompop, his research focused on the development of novel MR acquisition and reconstruction methods, especially for fast quantitative imaging and diffusion imaging. After 5-years research on fast multi-parameters quantitative brain imaging, he received his PhD degree in Biomedical engineering from Zhejiang University at 2019 and then joined Dr. Kawin Setsompop's group. His current work involves sampling trajectory optimization, novel sequence design, fast quantitative imaging and motion correction technique.

Uten Yarach is currently a Lecturer and Researcher at Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Thailand. Prior to this, he had been doing a PhD with Prof. Oliver Speck at BMMR, Magdeburg, Germany (2012-2017), focusing on residual artifact corrections for prospective motion correction for MRI data. After that, he was a postdoctoral fellow at Mayo Clinic until 2020, working on a model-based reconstruction for EPI data under supervision of Prof. Joshua Trzasko. In 2021, He has joined Prof. Kawin Setsompop’s group as a Research Collaborator, has focused on image reconstruction with integrating B0 and Eddy current induced distortion corrections for diffusion MRI data.

Itthi Chatnuntawech is a researcher at the National Nanotechnology Center, National Science and Technology Development Agency, Pathum Thani, Thailand, where he has led the Nanoinformatics and Artificial Intelligence Research Team since 2021. His research interests include machine learning, signal processing, and mathematical optimization. He received the B.S. degree in electrical and computer engineering from Carnegie Mellon University, Pittsburgh, PA, USA, in 2011, with a double major in biomedical engineering, and the S.M. and Ph.D. degrees in electrical engineering and computer science from Massachusetts Institute of Technology, Cambridge, MA, USA, in 2013 and 2016, respectively. During his Ph.D. study, he developed compressed-sensing-based reconstruction methods for accelerated multi-contrast MRI, magnetic resonance spectroscopic imaging (MRSI), and quantitative susceptibility mapping (QSM).

AA3 at Stanford University at The Stanford Radiological Sciences Laboratory (RSL), a research division of the Department of Radiology centered on the areas of Magnetic Resonance Imaging, X-Ray/Computed Tomography, and Ultrasound; supporting the labs of Drs. Daniel Ennis, PhD; Dr. Kawin Setsompop, PhD; Dr. Jennifer McNab, PhD.