Core Statistics Group
This group develops modern statistical methods for complex data, with applications in biomedicine, data mining, and high-dimensional analysis. Their work focuses on flexible modeling, accurate inference, and adapting to changing data patterns.
Themes:
- high dimensional statistics
- nonparametric statistics
- change point analysis
Members
- Ning Hao - High dimensional data; Machine learning; Change point detection
- Yue Selena Niu - Nonparametric statistics; Semiparametric modeling; Statistical genetics
- Hao Helen Zhang - Nonparametric smoothing; Model selection; Data Mining; Statistical applications in biosciences and biomedicine
Select Publications
Zou H, Zhang HH. On the Adaptive Elastic-Net with a Diverging Number of Parameters. Ann Stat. 2009;37(4):1733-1751. doi: 10.1214/08-AOS625. PMID: 20445770; PMCID: PMC2864037.
Jianqing Fan, Shaojun Guo, Ning Hao, Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression, Journal of the Royal Statistical Society Series B: Statistical Methodology, Volume 74, Issue 1, January 2012, Pages 37–65, https://doi.org/10.1111/j.1467-9868.2011.01005.x
Fan, J. and Niu, Y. Selection and validation of normalization methods for c-DNA microarrays using within-array replications. Bioinformatics, 23, 2391-2398.
Bayesian Statistics
Bayesian statistics provides a flexible framework for learning from data by combining prior knowledge with observed evidence. It is especially useful for complex models, uncertainty quantification, and real-time decision-making.
Themes:
- biostatistics
- ecological applications
- psychometrics
Members
- Edward Bedrick - Analysis of observational data; Bayesian methods; Generalized linear and mixed models
- Dean Billheimer - Measurement and normalization, Quantitative proteomics, Statistical methods for compositional data.
- Lifeng Lin - meta-analysis, network meta-analysis of multiple-treatment comparisons, publication bias, and Bayesian methods. He is also interested in the applications of statistical methods to real-world problems
- Henry Scharf - spatiotemporal statistics; Bayesian statistics; Ecological applications
- Xueying Tang - high dimensional Bayesian inference, latent variable models, small area estimation, psychometrics
Select Publications
Machine Learning Group
Machine learning focuses on developing algorithms that learn from data to make predictions, uncover patterns, and drive decision-making across domains like healthcare, finance, and technology.
Themes:
- reinforcement learning
- learning theory
- probabilistic graphical models
Members
- Kwang-Sung Jun - Reinforcement learning, active learning, Bayesian optimization
- Chicheng Zhang - Machine Learning, learning theory
- Jason Pacheco - Statistical machine learning, probabilistic graphical models, approximate inference algorithms, and information-theoretic decision making
Artificial Intelligence Group
Artificial intelligence aims to create systems that can reason, learn, and act intelligently. Research in this area spans from developing decision-making algorithms to building models that mimic human perception and behavior.
Members
- Clayton Morrison - Machine Learning, Causal Inference, Activity Recognition and Understanding, Automated Planning, Knowledge Representation, Computational Cognitive Science
- Kobus Barnard - Machine learning; Mathematical modeling of geometric form; Multi-modal data; Statistical applications in computer vision.