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Across scientific disciplines, massive amounts of data are being generated more rapidly and more easily. The real challenge is to design effective data collection strategies and to systematically find useful information in the data. Statistics is the scientific discipline that provides the tools to face this challenge in this age of data deluge. In simple terms, the field of statistics has been and remains "the science of collecting and interpreting data" (Diggle and Chetwynd, 2011). It is thus relevant to almost every kind of scientific investigation. For instance, statistical inference is an indispensible tool that allows scientists to evaluate the compatibility of their models (hypotheses) with their observations (data). Without this bridge or connection between model and data, there would be little systematic scientific progress. Statistician-scientists, both practitioners and methodologists, contribute to the scientific research advancements and discoveries ranging from understanding the causes of diseases to approaches to their treatment.
      Brief Bio: I joined the University of California at Irvine (UCI) in April 2013 as Director of the Biostatistics, Epidemiology and Research Design (BERD) Unit, within the Institute for Clinical and Translational Science (ICTS), and as Professor in the Division of General Internal Medicine, Department of Medicine. Prior to joining UCI, I was Professor in the Department of Public Health Sciences, Consortium Statistics Leader for the NeuroTherapeutics Research Institute, Director of the Data Coordinating Center for the Early Autism Risk Longitudinal Investigation (EARLI) Network, and Director of the Statistics Core of the Center for Children’s Environmental Health at UC Davis.
      My research involves several areas of basic, clinical and translational science, ranging from methods for analyzing genomics data to targeted treatment trials for individuals with Fragile X spectrum of involvements (fragile X syndrome, fragile X premutation, fragile X-associated tremor ataxia syndrome etc.). In the last several years I have also been involved in studies to further understand the outcome risk trajectories in patients on dialysis, such as cardiovascular events and infection-related hospitalization. From these collaborations on biomedical research studies, I work to develop innovative statistical methods/tools to further clinical and translational science research. Below is a list of some of my research areas.


Primary Research Areas

  • Applied statistics: applications in molecular biology, medicine, and epidemiology
  • Cases series methods and analysis, case only methods (epidemiology/biostatistics methods)
  • Categorical data analysis: Poisson modeling, zero-inflated and hurdle models
  • Covariate adjusted regression and correlation modeling (cross-sectional and longitudinal designs)
  • False discovery rate, multiple testing
  • Fragile X syndrome, fragile X premutation: molecular and clinical features, mechanism
  • Infection, cardiovascular outcome in dialysis patients, USRDS database, dialysis facility and hospitalization characteristics
  • Longitudinal analysis, sparse data, functional data
  • Lung cancer: survival, noninvasive staging, disparities, SEER database, NIS database
  • Measurement error modeling
  • Partial least squares: dimension reduction, classification/prediction
  • Statistical bioinformatics; high-dimensional data: microarray, genomics
  • Time-Varying Effects Modeling; Varying coefficient modeling