Yangxin Huang, Ph.D.

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Yangxin Huang, Ph.D.

Associate Professor

Contact Info

Office: 2129, MDC 56
Voice Mail: (813)974-8209
Fax: (813) 974-4719
Email: yhuang@health.usf.edu
 Education and History

Came to USF

2005

Education

B.S. Wuhan University of Technology, 1982
M.S. Huazhong University of Science and Technology, 1987
Ph.D. Liverpool John Moores University, 2000

Discipline

Biostatistics

Specialization

Biostatistics
Bayesian Modeling and MCMC
Mixed Effects Models for Repeated Measurements
Joint Modeling for Longitudinal and Survival Data
Longitudinal Data Analysis
HIV/AIDS Clincial Research
Health Data Analysis

Research Interests


Bayesian methodology and Markov Chain Monte Carlo
Parametric and nonparametric nonlinear mixed effects models
Joint models for longitudinal and survival data
Mixture of multiple-level models for longitudinal data
Missing and measurement error data modeling
HIV/AIDS dynamic modeling and prediction
Modeling ordinary differential equation (ODE) dynamic system for health research
Biostatistics to public health and medicine
Clinical research of infectious diseases and AIDS
AIDS clinical trial design and analysis
Community-based data analysis

Other Information

Curriculum Vitae

 Biography

Dr. Huang's current research interests are (1) Development of various statistical models and associated statistical methods to analyze longitudinal, repeated measurements, missing, censoring and survival data from epidemiological, medical and health fields. (2) Joint models with skew distributions for longitudinal and survival data: Normality (symmetric) of the model random errors is a routine assumption for the mixed-effects models in many longitudinal studies, but it may be unrealistic obscuring important features of subject variations. Propose a class of models with considering model errors to be a skew distribution for joint behavior of longitudinal dynamic response process, an associated covariate process with measurement errors in conjunction with survival process. Bayesian parametric and nonparametric NLME modeling approaches are proposed to simultaneously estimate model parameters for statistical inference. (3) HIV dynamic modeling: Propose mathematical/statistical models-based a system of ordinary differential equations (ODE) for drug exposure (pharmacokinetics and adherence), drug susceptibility (resistance), drug efficacy and responses of antiretroviral therapies in clinical trials and health data; develop statistical inference methods including Bayesian sampling techniques (MCMC) to estimate parameters in ODE dynamic models. Dr. Huang has also conducted on (1) various biostatistics methods and applications for various medical and health data; (2) interval estimation methods of median effective dose (ED50) for binary response data and their robustness in the presence of model misspecification; (3) optimal designs for the choice of numbers of doses in bioassay and their robustness under model misspecification.

Dr. Huang is a member of the American Statistical Association, International Chinese Statistical Association and the Royal Statistical Society. Dr. Huang serves as the Associate Editor of Journal “Computational Statistics and Data Analysis” and the Guest Editor of “Journal of Probability and Statistics”.