Xinzhou Ge, Postdoctoral Fellow
Department of Statistics, UCLA

Location: Young Hall CS50

Abstract:

Large-scale feature screening is ubiquitous in high-throughput biological data analysis: identifying the features (e.g., genes, mRNA transcripts, and proteins) that differ between conditions from numerous features measured simultaneously. The false discovery rate (FDR) is the most widely-used criterion to ensure the reliability of screened features. The most famous Benjamini-Hochberg procedure for FDR control requires valid high-resolution p-values, which are, however, often hardly achievable because of the reliance on reasonable distributional assumptions or large sample sizes. Motivated by the Barber-Candes procedure, Clipper is a general statistical framework for large-scale feature screening with theoretical FDR control and without p-value requirement. Extensive numerical studies have verified that Clipper is a versatile and effective tool for correcting the FDR inflation crisis in multiple bioinformatics applications.

Bio:

Xinzhou obtained his Ph.D. degree in 2021 from Department of Statistics at UCLA, where he worked with Prof. Jingyi Jessica Li. He received his Bachelor of Statistics from School of Mathematical Science, Peking University in 2016. After graduating from UCLA, Xinzhou continued working with Prof. Jingyi Jessica Li as a postdoc.

Robert Mee, Professor of Business Analytics
Haslam College of Business, University of Tennessee

Location: Young Hall CS50

Abstract:

Often the primary objective of order-of-addition experiments is to identify the sequence with the best mean response. Thus, we provide a multiple comparison procedure for identifying all sequences that are not significantly inferior to the best. Simulation is used to determine the multiple-comparison-with-the-best critical values. While the methods apply to any parametric model, certain cases require only a single critical value. We tabulate the required critical values for several popular order-of-addition models, when estimation is based on an optimal design. We use examples from the literature to illustrate how model choice impacts the set of sequences that are determined to be not significantly less than the best. The open question of a similar inference for order-of-addition kriging models will be raised.

Bio:

Dr. Robert Mee is the William and Sara Clark Professor of Business Analytics in the Haslam College of Business at the University of Tennessee. Dr. Mee received his B.S. In Management Science from Georgia Institute of Technology, and his M.S. and Ph.D. in Statistics from Iowa State University. Dr. Mee is an elected fellow of the American Statistical Association and has authored 60 refereed journal articles. He served on Technometrics’ Management Committee for 12 years and as an Associate Editor for 7 years. Currently he serves on the Journal of Quality Technology’s Editorial Board. Dr. Mee’s research interests include design and analysis of experiments and choice-based conjoint analysis. He is the author of A Comprehensive Guide to Factorial Two-Level Experimentation, a monograph published by Springer.

Ying Hung, Professor
Department of Statistics, Rutgers University

Location: Young Hall CS50

Abstract:

Surrogate modeling based on Gaussian processes (GP) has received increasing attention in the analysis of complex problems in science and engineering. Despite extensive studies on GP modeling, the developments for functional inputs are scarce. Motivated by an inverse scattering problem, a new class of kernel functions is introduced for GP with functional inputs. The asymptotic convergence properties of the proposed GP models are derived. In the application to inverse scattering problem, the functional input which is associated with the support of the scattering region of interest is identified, given a measured far-field pattern.

Bio:

Dr. Hung is a Professor in Department of Statistics at Rutgers. Dr. Hung graduated from Georgia Institute of Technology in 2008. She received NSF CAREER award and IMS Tweedie Award in 2014. Her research areas include experimental design, modeling for computer experiments, and uncertainty quantification.

Roshan Joseph, Professor
Industrial and Systems Engineering, Georgia Institute of Technology

Location: Young Hall CS50

Abstract: TBA

Frauke Kreuter, Professor
Maryland Population Research Center, University of Maryland

Location: Young Hall CS50

Abstract: TBA