School of Medicine
Showing 1-4 of 4 Results
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Jeya Maria Jose Valanarasu
Postdoctoral Scholar, Radiology
BioDr. Jeya Maria Jose Valanarasu is a postdoctoral scholar working with the Stanford Machine Learning Group and the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center). He leads the AI for Healthcare bootcamp with Dr. Andrew Ng, Dr. Curt Langlotz, and Dr. Nigam Shah which provides Stanford students an opportunity to engage in advanced research at the intersection of AI and healthcare.
He obtained his Ph.D. and M.S from Johns Hopkins University where he worked on various problems in Computer Vision, Machine Learning, and Healthcare. His research aims to overcome the challenges that arise when translating machine learning models to practical applications for healthcare and engineering sectors. His works have spanned over topics like designing effective deep architectures, model adaptability to changing environments, role of data and annotations, multi-modal learning and taming large models for computer vision and healthcare tasks. He has published over 25 peer-reviewed journal/conference articles at top venues and filed 3 U.S. patents. He has been awarded Amazon Research Fellowship 2022, Best Student Paper Awards at ICRA 2022, CVIP 2019, MICCAI Young Scientist Impact Award Finalist 2022, and the NIH MICCAI Award 2022. He has also served as a reviewer for multiple journals and conferences. -
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.
