Xiongtao Dai is an Assistant Professor of Biostatistics. His research interest lies in flexible, nonparametric analysis of complex data objects such as curves, trajectories, and images. He has worked on a wide range of applications including developmental neuroscience, kinetics, plant phenomics and genomics, and remote sensing.
Xiongtao Dai’s research interest lies in the analysis of complex data objects such as functional, longitudinal, and geometrical data. These data types, collectively referred to as object data, are generated increasingly often and call for new flexible analysis. A major theme in his research is to address the curve nature of functional and longitudinal data, and the geometrical constraint in non-Euclidean data. He has worked on statistical applications in developmental neuroscience, kinetics, plant phenomics and genomics, and remote sensing.
He received his Ph.D. in Statistics degree from UC Davis in 2018. Before joining Berkeley Public Health in 2022, he was an Assistant Professor of Statistics at Iowa State University.
- PhD, Statistics, University of California, Davis, 2018
- BS, Statistics, University of Hong Kong, 2013
- Nonparametric statistics
- Longitudinal data analysis
- Functional data analysis
- Geometrical data analysis
- Data depth
- Causal inference
- PBHLTH 290 Health Issues Seminars: Biostatistical Computing