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Dr. Hua Zhou is a Professor in the field of Public Health, with long-term interests in numerical optimization problems, particularly those arising from the statistical analysis of high-dimensional data. He has developed highly scalable optimization algorithms for maximum likelihood estimation in multivariate discrete distributions and has worked on the calculation of importance sampling weights for large data sets. His research also includes geometric signomial programming and model-based movie rating methods. Dr. Zhou proposed a new deterministic annealing method for global optimization, employing a quasi-Newton scheme to accelerate high-dimensional optimization algorithms and utilizing massive parallel computing with graphical processing units (GPUs). He has investigated new path algorithms for regularization problems in statistics and machine learning, successfully generalizing angle regression in convex programming. Among his recent developments are scalable estimation algorithms for multivariate response generalized linear models and variance components models, along with fast matrix computation tools for distance majorization in convex programming. His research aspirations focus on developing statistical computational tools for the analysis of large-scale genomic data, including penalization methods for association screening in genome-wide association studies (GWAS) and nonlinear dimension reduction approaches for genotype aggregation and mapping. Dr. Zhou is currently engaged in genome-wide QTL association mapping, genotype imputation, and transcriptomics data analysis based on RNA-seq technology, alongside statistical methods for analyzing microbiome data. He is also the developer of Mendel, a comprehensive genetics analysis software that is freely available on the UCLA Human Genetics Software Page.
Department of Economics admits primarily for the PhD program.