I am currently a Professor in the Department of Biostatistics in the Division of Quantitative Sciences at The University of Texas MD Anderson Cancer Center, and serving as Deputy Chair of the department. I received my PhD from the Department of Statistics at Texas A&M University in August, 2000, under the direction of Raymond J. Carroll and Naisyin Wang. I became interested in Statistics through my undergraduate mentor, Marlin Eby, while getting my B.A. in Mathematics Education at Messiah College in Grantham, PA. My primary research interests include statistical methods for analyzing functional and image data, and also methods for analyzing various types of complex, high dimensional data encountered in bioinformatics, including mass spectrometry and 2-d gel proteomics data, microarrays, and array cGH data. In my bioinformatics work, I frequently collaborate with colleagues in the Department of Bioinformatics and Computational Biology. I also collaborate with numerous researchers at MD Anderson on various research projects involving gastro-intestinal related cancers.
Functional Data Analysis
Robust Classification of Functional and Quantitative Image Data using Functional Mixed Models (with Hongxiao Zhu and Philip J. Brown), UT MD Anderson Cancer Center Department of Biostatistics Working Paper Series (2011)
In this paper, we introduce classification of complex high dimensional functional data in the functional...
Robust, Adaptive Functional Regression in Functional Mixed Model Framework (with Hongxiao Zhu and Phil Brown), Journal of the American Statistical Asssociation (2011)
Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead...
Statistical Methods for Proteomic Biomarker Discovery Based on Feature Extraction or Functional Modeling Approaches, Statistics and Its Interface (2011)
In recent years, developments in molecular biotechnology have led to the increased promise of detecting...
Automated Analysis of Quantitative Image Data Using Isomorphic Functional Mixed Models with Application to Proteomics Data (with Veerabhadran Baladandayuthapani, Richard C. Herrick, Pietro Sanna, and Howard B. Gutstein), Annals of Applied Statistics (2010)
Image data are increasingly encountered and are of growing importance in many areas of science....
Bayesian Random SegmentationModels to Identify Shared Copy Number Aberrations for Array CGH Data (with Veerabhadran Baladandayuthapani, Yuan Ji, Rajesh Talluri, and Luis E. Nieto-Barajas), Journal of the American Statistical Association (2010)
Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis...
Proteomics
Robust Classification of Functional and Quantitative Image Data using Functional Mixed Models (with Hongxiao Zhu and Philip J. Brown), UT MD Anderson Cancer Center Department of Biostatistics Working Paper Series (2011)
In this paper, we introduce classification of complex high dimensional functional data in the functional...
Robust, Adaptive Functional Regression in Functional Mixed Model Framework (with Hongxiao Zhu and Phil Brown), Journal of the American Statistical Asssociation (2011)
Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead...
Statistical Methods for Proteomic Biomarker Discovery Based on Feature Extraction or Functional Modeling Approaches, Statistics and Its Interface (2011)
In recent years, developments in molecular biotechnology have led to the increased promise of detecting...
Evaluating the performance of new approaches to spot quantification and differential expression in 2-dimensional gel electrophoresis studies (with Brittan N. Clark, Wei Wei, and Howard B. Gutstein), Journal of Proteome Research (2010)
2-DE is an important method for proteomics. Accurate spot detection and quantification on the resulting...
Automated Analysis of Quantitative Image Data Using Isomorphic Functional Mixed Models with Application to Proteomics Data (with Veerabhadran Baladandayuthapani, Richard C. Herrick, Pietro Sanna, and Howard B. Gutstein), Annals of Applied Statistics (2010)
Image data are increasingly encountered and are of growing importance in many areas of science....
Genomics
Robust Classification of Functional and Quantitative Image Data using Functional Mixed Models (with Hongxiao Zhu and Philip J. Brown), UT MD Anderson Cancer Center Department of Biostatistics Working Paper Series (2011)
In this paper, we introduce classification of complex high dimensional functional data in the functional...
Bayesian Random SegmentationModels to Identify Shared Copy Number Aberrations for Array CGH Data (with Veerabhadran Baladandayuthapani, Yuan Ji, Rajesh Talluri, and Luis E. Nieto-Barajas), Journal of the American Statistical Association (2010)
Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis...
Members’ Discoveries: Fatal flaws in cancer research, IMS Bulletin (2010)
A recent article published in The Annals of Applied Statistics (AOAS) by two MD Anderson...
Statistical Contributions to Proteomic Research (with Keith A. Baggerly, Howard B. Gutstein, and Kevin R. Coombes), Methods in Molecular Biology (2010)
Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases....
Alternative Probeset Definitions for Combining Microarray Data Across Studies Using Different Versions of Affymetrix Oligonucleotide Arrays (with Chunlei Wu, Kevin R. Coombes, Keith A. Baggerly, Jing Wang, and Li Zhang), Meta-Analysis in Genetics (2006)
Many published microarray studies have small to moderate sample sizes, and thus have low statistical...
Statistical Methods: Bootstrap
The BLUPs Are Not “Best” When It Comes To Bootstrapping, Statistics and Probability Letters (2002)
In the setting of mixed models, some researchers may construct a semiparametric bootstrap by sampling...
Statistical Theory and Methods
Automated Analysis of Quantitative Image Data Using Isomorphic Functional Mixed Models with Application to Proteomics Data (with Veerabhadran Baladandayuthapani, Richard C. Herrick, Pietro Sanna, and Howard B. Gutstein), Annals of Applied Statistics (2010)
Image data are increasingly encountered and are of growing importance in many areas of science....
Wavelet-based functional linear mixed models: an application to measurement error–corrected distributed lag models (with Elizabeth J. Malloy, Sara D. Adar, Helen Suh, Diane R. Gold, and Brent A. Coull), Biostatistics (2010)
Frequently, exposure data are measured over time on a grid of discrete values that collectively...
Statistical Models
Robust, Adaptive Functional Regression in Functional Mixed Model Framework (with Hongxiao Zhu and Phil Brown), Journal of the American Statistical Asssociation (2011)
Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead...
Statistical Methods for Proteomic Biomarker Discovery Based on Feature Extraction or Functional Modeling Approaches, Statistics and Its Interface (2011)
In recent years, developments in molecular biotechnology have led to the increased promise of detecting...
Automated Analysis of Quantitative Image Data Using Isomorphic Functional Mixed Models with Application to Proteomics Data (with Veerabhadran Baladandayuthapani, Richard C. Herrick, Pietro Sanna, and Howard B. Gutstein), Annals of Applied Statistics (2010)
Image data are increasingly encountered and are of growing importance in many areas of science....
Image Analysis
Robust Classification of Functional and Quantitative Image Data using Functional Mixed Models (with Hongxiao Zhu and Philip J. Brown), UT MD Anderson Cancer Center Department of Biostatistics Working Paper Series (2011)
In this paper, we introduce classification of complex high dimensional functional data in the functional...
Robust, Adaptive Functional Regression in Functional Mixed Model Framework (with Hongxiao Zhu and Phil Brown), Journal of the American Statistical Asssociation (2011)
Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead...
Statistical Methods for Proteomic Biomarker Discovery Based on Feature Extraction or Functional Modeling Approaches, Statistics and Its Interface (2011)
In recent years, developments in molecular biotechnology have led to the increased promise of detecting...
Automated Analysis of Quantitative Image Data Using Isomorphic Functional Mixed Models with Application to Proteomics Data (with Veerabhadran Baladandayuthapani, Richard C. Herrick, Pietro Sanna, and Howard B. Gutstein), Annals of Applied Statistics (2010)
Image data are increasingly encountered and are of growing importance in many areas of science....