Probability & Statistics

 

Full-Time Faculty Research Interests
Francesco Cellarosi Limit theorems for exponential sums, random processes of number-theoretical origin
Wenyu Jiang Methodology in biostatistics: design and analysis of clinical trials, biomarker study, survival analysis, statistical prediction and genomic data analysis
Boris Levit Non-parametric estimation, semi-parametric models, optimal design in non-parametric regression
Brian Ling Latent variable models, survival and event history analysis, nonparametric estimation, semiparametric models and shape-constrained statistical inference ​
Chunfang Devon Lin computer experiments, uncertainty quantification, experimental design, efficient data collections for real applications in science and engineering, big data and statistical learning
Brad Rodgers Analytic number theory, random matrix theory ​
Yanglei Song Sequential decision making, change point detection, distribution approximation
Glen Takahara Queueing and communication networks, Bayesian mixture models, statistical learning, orientation data
Serdar Yüksel Stochastic control theory, stochastic dynamical systems, networked control, information theory, source coding and quantization

 

Affiliated Faculty Status Research Interests
Bingshu E. Chen Cross-Appointed Recurrent events and multivariate survival analysis, statistical methods in cancer clinical trials, epidemiology and bio-markers
James McLellan Cross-Appointed Functional data analysis, statistical methods in chemical engineering, inference in nonlinear regression models
Paul Y. Peng Cross-Appointed Cure models, multivariate survival models, statistical computing and Bayesian methods, statistical and epidemiological methods for population/observational data analysis, statistical consulting
Hwashin Shin Adjunct Environmental public health risk models, Epidemiology, Biostatistics, Orientation data analysis, Experimental optimal design
Dongsheng Tu Cross-Appointed Survival analysis, statistical methods in clinical trials, resampling methods, large sample theory