Xinkai Zhou (周新凯)

I am a postdoctoral fellow in the Department of Biostatistics at Johns Hopkins University, working with Ciprian Crainiceanu. Before joining Hopkins, I obtained my Ph.D. in Biostatistics from UCLA, working with Hua Zhou.

My current research focuses on developing flexible and scalable functional data analysis methods for modeling modern health data collected via wearable and implantable technologies. My work is motivated by problems arising from both clinical and public health research, including high-resolution hemodynamic data from cardiac surgeries and large-scale physical activity data collected by the US CDC and UK Biobank using accelerometers. For more information about specific projects, please visit the Research page.

During Ph.D., I developed statistical methods and reproducible software to enable uncertainty quantification for big data and complex models. Key projects include 1) scaling up inference for mixed models to ultra-large datasets (> 200 million observations); 2) enabling statistical inference for complex machine learning tasks such as constrained and regularized regression, matrix completion, and sparse low-rank matrix regression by integrating concepts from optimization and Bayesian inference.

Honors and Awards

• Biostatistics Outstanding Student Award, UCLA, June, 2023
• Delta Omega Honorary Society in Public Health, June, 2023
• ASA Student Paper Award, Sections on Computing and Graphics, August, 2022
• Eugene and Sallyann Fama Fellowship, UCLA, June, 2020
• Graduate Summer Research Fellowship, UCLA, 2019 and 2020