SCIENCE

Revolution Analytics has named Lee Edlefsen chief scientist. In this newly created role, Edlefsen will oversee Revolution's technology initiatives, and continue his efforts to create innovative enterprise solutions around the R language and the growing R enterprise community. This executive appointment follows two quarters of aggressive growth, during which time Revolution Analytics delivered two major technologies to bolster R for business use, as well as doubled the size of its customer base.

Revolution Analytics CEO Norman Nie said of the appointment, "For 30 years, Lee has been a technical leader around the R statistical language and other cutting-edge analytical languages. As a key technologist and one of the original engineers of Revolution's flagship product, Revolution R Enterprise, Lee has been invaluable to this company's growth and expansion. We are thrilled to have Lee at Revolution and we know he will play a key role in pushing R to its technical limits as he continues to shape our products and the overall vision for the company."

Prior to joining Revolution Analytics, Edlefsen co-founded a number of startups including ExaMetrix, a consulting and software development firm specializing in analysis and visualization of large, complex data sets and TriMetrix, a technical graphics and data analysis company acquired by MathSoft, which itself was later acquired by TIBCO. At MathSoft, Edlefsen held several leadership positions, including vice president of development.

"R is an incredible platform for analytics, with an amazing community behind it," said Edlefsen. "With Revolution's help, I believe its use is going to see explosive growth in the commercial world as it already has in academics. I am particularly excited to be working with Revolution's impressive team to enhance and expand huge data set and distributed computing capabilities in R."

Edlefsen holds a Ph.D. from Harvard University and has led a long career developing data analysis and visualization software. He has a long-standing interest in high performance computing, especially in the analysis and visualization of large and complex data sets. He currently leads a project at Revolution Analytics to add increasingly faster distributed big data capabilities to R.