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Azam Mandana Yazdani
Division of Biostatistics and Epidemiology
The University of Texas, Houston, Texas, United States
Dr. Yazdani’s research is identifying and analyzing statistical causal networks by integrating multi-omics data to identify biomarkers influencing risk factor levels and disease endpoints. She has been trained in statistical theory and causal inference at top Universities, Jena University in Germany, and Cambridge University in England. Dr. Yazdani introduced an algorithm, Granularity Directed Acyclic Graph (GDAG), which first extracts information across the genome using principal components and then employs them to identify a robust statistical causal network over phenotypes. This work was recognized at the 2015 Atlantic Causal Inference Conference at the University of Pennsylvania and won “The Thomas R. Ten Have Award”.
Multi-omics integration in statistical causal setting, Statistical Causal Inference, Bayesian Network, Metabolomics, System Biology