Yulan Liang
University of Maryland, USA
Biography
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Methodologies: Biostatistics and Data Science<br />
Big Omics Data Science, Bayesian Inference and Meta Analysis; Longitudinal models and Transitional models; Hierarchical models and Multi-level models; Predictive models; Statistical Machine Learning and Bayesian Learning; Multivariate Analysis; Categorical Data Analysis; Data Mining; Neural Networks; Time Series Analysis; Pattern Recognition; High dimensional and high content mathematical modeling<br />
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Applications: Bioinformatics, Statistical Genetics/Genomics, Proteomics and Other Omics<br />
System and computational biology, Population Genetics and Genomics; Association Study for Haplotype, SNP Data; Genetic Epidemiology; High-throughput Genomic and Proteomic and Other Omics Data on Common Diseases (Diabetes; Hypertension; Obesity, Periodontal and Cardiovascular Disease, Neurological Disorders Including Multiple Sclerosis and Parkinson Disease; Cancer; HIV Pathogenesis, Pain, Aging, Sjögren’s Syndrome, Rheumatoid Arthritis). Other Biostatistical Areas: Public health, health services, and healthcare data; Clinical trials; Epidemiology; Functional Neuroimaging and Medical Image data<br />
Software experience: SAS, MATLAB, WINBUGS, SPSS, S+, C++, R, Bioconductor, and various statistical genomics/genetics software.<br />
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