Stanford University
Showing 161-180 of 249 Results
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Justin Xu
Graduate, Medicine, Radiology
BioJustin is currently studying for a PhD in Biomedical Data Science at the University of Oxford. His research aims to leverage artificial intelligence (AI) and machine learning (ML) to decipher clinical data and enhance healthcare. Specifically, his current doctoral work focuses on developing, deploying, and evaluating AI/ML tools to help hospitals manage patient demand. Justin is co-advised by Professor David W. Eyre, Professor A. Sarah Walker, and Professor David A. Clifton.
In January 2024, Justin began his visit to Stanford University as a Canadian Fulbrighter. He joined the Centre for Artificial Intelligence in Medicine & Imaging (AIMI) to develop multimodal generative AI in radiology under Dr. Curtis P. Langlotz.
Prior to beginning his doctoral studies, Justin worked with Dr. Alistair E. W. Johnson at the Hospital for Sick Children in Canada. During this time, he worked with the MIMIC-IV dataset and deployed a clinical terminology annotation dashboard to support multi-site analyses of electronic health records with natural language processing. Additionally, he contributed to the task querying features of "EventStreamGPT", a pre-processing and modelling library designed for generative pre-trained transformers and medical record time series. Justin was trained as a biomedical engineer and holds a BASc in Engineering Science from the University of Toronto. -
Kuang Xu
Associate Professor of Operations, Information and Technology at the Graduate School of Business and, by courtesy, of Electrical Engineering
BioKuang Xu is an Associate Professor of Operations, Information and Technology at Stanford Graduate School of Business, and Associate Professor by courtesy with the Electrical Engineering Department, Stanford University. Born in Suzhou, China, he received the B.S. degree in Electrical Engineering (2009) from the University of Illinois at Urbana-Champaign, and the Ph.D. degree in Electrical Engineering and Computer Science (2014) from the Massachusetts Institute of Technology.
His research primarily focuses on understanding fundamental properties and design principles of large-scale stochastic systems using tools from probability theory and optimization, with applications in queueing networks, healthcare, privacy and machine learning. He received First Place in the INFORMS George E. Nicholson Student Paper Competition (2011), the Best Paper Award, as well as the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS (2013), and the ACM SIGMETRICS Rising Star Research Award (2020). He currently serves as an Associate Editor for Operations Research and Management Science. -
Kun Xu
Postdoctoral Scholar, Mechanical Engineering
Current Research and Scholarly InterestsMaterials characterization by using advanced electron microscopy
