High-throughput sequencing of microbial communities allows researchers to characterize associations between the host microbiome and health status, detect pathogens, and identify the interplay of an organism’s microbiome with the built environment. Recent highlights include work on the specificity of the human skin microbiome, the diversity in the ocean microbiome, and a catalogue of the global virome. Effective data analysis tools and appropriate statistical models for this type of data are vital to derive and communicate actionable insights from these experiments. In this talk I will present our group's efforts at addressing this need including statistical methods and interactive, exploratory data visualization tools. Along the way, I will reflect on how this experience has coincided with the establishment of Biomedical Data Science as a discipline and how that has shaped my group's research and training focus.
- from https://www.umiacs.umd.edu/people/hcorrada
Héctor Corrada Bravo is an associate professor of computer science. He holds appointments in the Center for Bioinformatics and Computational Biology and UMIACS.
His research focuses on statistical and machine learning methods for high-throughput genomic data analysis. This includes pre-processing of measurements from high-throughput assays, disease risk models that integrate high-throughput genomic and other data, and cancer epigenetics and biomarker discovery. Corrada Bravo's research interests also include the development of new methods and tools from multiple areas in the computational and statistical sciences: basic bioinformatics/biostatistics, statistical and machine learning, data management, and numerical optimization.
He received his doctorate in computer science from the University of Wisconsin in 2008. Corrada Bravo held a post-doctoral fellowship in biostatistics in the Johns Hopkins Bloomberg School of Public Health from 2008 to 2010. He joined the University of Maryland in 2010.