Big Data and Academia: the launch of Rennselaer IDEA

“Big Data” is all over the news these days, and has gained both positive and negative connotations. The origins of the term “Big Data” are somewhat mysterious. In a paper examining the phenomenon, Francis Diebold, an economist at the University of Pennsylvania, concludes that the term probably began to be used informally within the private sector in the mid-1990s. He traces the first “significant academic references” to two papers, one published in 1998 and the other in 2000. An article in The New York Times goes back further, concluding that the first use of the term was probably in 1989, in Harper’s magazine. The first use of the term in something approximating its modern context and meaning probably occurred later, however; The New York Times author credits John Mashey, the chief scientist at Silicon Graphics in the 1990s, calling him the “father of the term Big Data.”  The new quarterly update of the Oxford English Dictionary defines big data as follows:

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Whatever the origins of the term, there can be no doubt that the notion of Big Data has long since grown beyond its obscure origins. Within academia, Big Data is well on its way to emerging as a well-developed (if inter-disciplinary) field of study. The business and popular media regularly run prominent stories on the phenomenon. Recent years have seen cover stories and dedicated sections within the media with titles like “The Data Deluge” and “The Petabyte Age.” As a result, a field that until recently was confined to the realm of academic specialists and researchers has now decidedly entered the mainstream. Within academia, Diebold argues that “Big Data is now a key theme in all the sciences—arguably the key scientific theme of our times.”  In the popular consciousness, the term has joined a short list of computer terms (“Moore’s Law,” “to Google,” “friending”) that are part of everyday vocabulary.

Not surprisingly the last two years we have also seen the creation of several new academic centers that focus on big data. For instance, both the Center for Urban Science and Progress (CUSP) at New York University and the Urban Center for Computation and Data (UrbanCCD)  were launched last year and focus on how big and open data can tackle urban challenges.

The new kid on the block of multi-disciplinary big data centers was launched yesterday at Rensselaer.   According to the press release the new Rensselaer Institute for Data Exploration and Applications (IDEA) will bring “together and [fortify] the wealth of data science, high performance computing, predictive analytics, data visualization, and cognitive computing research at Rensselaer, the nation’s oldest technological research university”.

Professor James Hendler, head of the Rensselaer Department of Computer Science,  and GovLab Network member, will serve as the director of the Rensselaer IDEA. In contrast with CUSP and UrbanCCD, IDEA will focus on societal problems that go beyond urban challenges. Jim Hendler: “From improving health care, to environmental stewardship, to creating new educational technologies, researchers at Rensselaer are known internationally for using data science to attack some of the world’s most pressing problems…The Rensselaer IDEA will create a collaborative space where our faculty and students can explore the intersections of different leading-edge data research, and then use what they find to jump-start new programs, products, and companies. A key focus of the IDEA is data-driven innovation, which builds on the Rensselaer legacy of pushing forward the frontiers of basic science and changing the world with outstanding inventions and applications.”

“The new institute connects faculty members and students with four critical Rensselaer research platforms: the CCNI supercomputing center, the IBM Watson cognitive computing system, the Curtis R. Priem Experimental Media and Performing Arts Center, and the Center for Biotechnology and Interdisciplinary Studies. With these resources, IDEA researchers will innovate new theories, technologies, and applications in many areas, with a particular emphasis in seven key pillar arenas:

  • Health-care analytics
  • Business intelligence
  • Built and natural environments
  • Virtual and augmented reality systems
  • Cybersecurity applications
  • Basic research in physical and engineering sciences
  • Public policy”

Slides from Jim Hendler describing the concept behind IDEA can be found here.

 

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