I am currently a professor in the Department of Statistics at the University of Pittsburgh. My statistical methodological work focuses on functional data analysis and network modeling. Recently, my research has centered on modeling the variance and covariance structures of complex functional data and network data, nonparametric prediction for functional data, relational-data and survival outcomes, as well as using statistical and computational methods to extract meaningful insights from electronic health records (EHRs). My applied work focuses on statistical methods in mental health studies, including translational neuroscience in schizophrenia studies and biomarker identification and prediction in suicidal studies.
Courses
- Supervised Statistical Consulting
- Applied Statistical Methods
- Applied Categorical Data Analysis
- PhD, Statistics, University of California, Davis, CA, 2012
- BS, Probability and Statistics, Peking University, Beijing, China
Education & Training
Recent Publications
- K. Chen and H.G. Müller (2012). Conditional quantile analysis when covariates are functions, with application to growth data (PDF). J. Royal Statistical Society, Series B, 74, 67-89.
- K. Chen and H.G. Müller (2012). Modeling repeated functional observations (PDF). J. American Statistical Association, 107, 1599-1609.
- K. Chen, and J. Lei (2015), Localized functional principal component analysis (PDF). J. American Statistical Association , 110 , 1266-1275.
- K. Chen , P. Delicado and H.G. Müller (2016), Modeling functional-valued stochastic processes, with applications to fertility dynamics. J. Royal Statistical Society, Series B ,79(1),177-196.
- K. Chen , and J. Lei (2017), Network cross-validation for determining number of communities in network data (PDF). J. American Statistical Association,113(521), 241-251.
Research Interests
- Functional Data Analysis
- Network Data Analysis
- Non-parametric Prediction
- Suicidal Prediction
