Assistant Professor of Biostatistics and Computer Science

Research interests:

• Statistical machine learning
• AI for science
• AI for health
• Computational genomics
• Uncertainty quantification

Yiqun Chen is an assistant professor of biostatistics at the Bloomberg School of Public Health with a joint appointment in the Whiting School of Engineering’s Department of Computer Science. Affiliated with the Malone Center for Engineering in Healthcare, Chen focuses on quantifying, calibrating, and communicating the uncertainty in modern data analysis, with applications to biomedical and health data. He is also interested in exploring how to make machine learning methods more reliable and equitable. In addition to his methodological interests, he works on collaborative projects with scholars in public health, human-computer interaction, software engineering, and biology.

He received a New Investigator Scholarship award at the 2020 Conference on Retroviruses and Opportunistic Infections, a Best Paper Award at the 2021 Meeting of the Western North American and Japanese Regions of the International Biometric Society and the Institute of Mathematical Statistics, a Student Research Award at the 2022 New England Statistics Symposium, a University of Washington Outstanding Teaching Assistant Award, and a Thomas R. Fleming Excellence in Biostatistics Award.

Before joining Hopkins, Chen was a data science postdoctoral fellow at Stanford University working with James Zou to apply generative AI techniques to biomedical data. Prior to this, he received his PhD in biostatistics from the University of Washington, where he was advised by Daniela Witten and developed methods to test data-driven hypotheses. He has also worked at Amazon CloudSearch, Waymo LLC, and Meta, and completed his undergraduate degrees in statistics, computer science, and chemical biology at the University of California, Berkeley.