Rafael Irizarry received his Bachelor’s in Mathematics in 1993 from the University of Puerto Rico and went on to receive a Ph.D. in Statistics in 1998 from the University of California, Berkeley. He joined the faculty of the Department of Biostatistics in the Johns Hopkins Bloomberg School of Public Health in 1998 and was promoted to Professor in 2007. He is now Professor of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute and a Professor of Biostatistics at Harvard School of Public Health. In 2009, the Committee of Presidents of Statistical Societies (COPSS) named Professor Irizarry the Presidents’ Award winner. The Presidents’ Award is arguably the profession’s most prestigious award honoring early career contributions. In 2017, the members of Bioinformatics.org chose Irizarry the laureate of the Benjamin Franklin Award in the Life Sciences. He also received the 2009 Mortimer Spiegelman Award which honors an outstanding public health statistician under age 40. He also won the 2001 American Statistical Association Noether Young Scholar Award for researcher, younger than 35 years of age, who has significant research accomplishments in nonparametrics statistics. Irizarry was also named a fellow of the American Statistical Association in 2009. He served as the chair of the Genomics, Computational Biology and Technology Study Section (GCAT) NIH study section from 2013-2015.
"Since 1999, my work has focused on Genomics and Computational Biology problems; my expertise being applied statistics and genomics, for which I develop statistical tools that help researchers better interpret their data. My lab disseminates these tools through open source that is available for free online. This software has tens of thousands of users and the scientific publications in which these methods are highly cited. In particular, we have worked on the analysis and signal processing of microarray, next-generation sequencing, and genomic data. We are currently interested in leveraging his knowledge in translational work, e.g. developing diagnostic tools and discovering biomarkers. I am also the developer or co-developer of several widely used methodologies and open source software packages relevant for high-throughput data pre-processing and am part of the current leadership of the Bioconductor project. My major contributions to science relate to statistical methods and software that permit biological discovery from the complex datasets generated with high-throughput technologies. These contributions include Signal Processing for Gene Expression Data, Open Source Software for Genomics, Statistical Methodology for High-throughput Data, Epigenetics, and Leveraging Genomics Public Databases. I am deeply committed to training our next generation of biostatisticians, in particular those interested in biomedical data-related research."