Corrada Bravo's 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. He also works on 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.
Corrada Bravo joined the department in July of 2010 after a postdoctoral fellowship at Johns Hopkins Bloomberg School of Public Health. He earned a PhD in Computer Science from the University of Wisconsin at Madison, and he also holds degrees in music from The Peabody Institute and the Indiana School of Music.c