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Yiqun Chen is an Assistant Professor in the Department of Biostatistics at Johns Hopkins University, with a joint appointment at the Malone Center for Engineering in Healthcare. Dr. Chen's research primarily focuses on quantifying, calibrating, and communicating uncertainty in modern data analysis, particularly in applications involving biomedical health data. He is interested in developing reliable and equitable machine learning methods. Alongside his methodological work, he collaborates on projects in public health, human-computer interaction, software engineering, and biology. Dr. Chen has been recognized with several awards, including the New Investigator Scholarship at the 2020 Conference on Retroviruses and Opportunistic Infections, the Paper Award at the 2021 Meeting of the Western North American and Japanese Regions of the International Biometric Society, and the Student Research Award at the 2022 New England Statistics Symposium. Before joining Johns Hopkins, he was a data science postdoctoral fellow at Stanford University, where he worked with James Zou on applying generative AI techniques to biomedical data. He holds a PhD in Biostatistics from the University of Washington, where he developed methods to test data-driven hypotheses, and has worked with leading companies such as Amazon, Waymo LLC, and Meta. Dr. Chen completed his undergraduate degrees in Statistics, Computer Science, and Chemical Biology at the University of California, Berkeley.
Department of Pathology - PhD in Pathobiology. GRE is not required.