Data science has become one of the defining disciplines of the 21st century. Recent years have seen unprecedented growth in size, speed, and accessibility of data. In 1993, the total movement of data on the Internet amounted to 100 terabytes per year. Today, the rate is over a billion terabytes per year. About 90 percent of all of the data created in human history have been generated in the past two years.
More than 500 years ago, Gutenberg’s printing press produced a new way to disseminate information that seems trivial in comparison with the Internet of today. Nonetheless, that innovation is often cited as the critical catalyst for the Renaissance and the scientific revolution that followed. Today, the digital revolution is creating unforeseen new opportunities, especially for those whose decision making most expertly taps the vast information resources that are now at our disposal.
Although we have the tools to access huge amounts of information, we now need to develop and wield new and better ones to lead us to optimized decisions in almost any human endeavor. In doing so, we can better understand and, ideally, solve some of the greatest challenges facing the world today in medicine and health, energy and the environment, economics and politics, and many other areas.
The Institute for Data Science
The University of Rochester intends to be among the world’s leaders in data science. Data science is the centerpiece of the University's five-year strategic plan, committing
$50 million—in addition to more than $50 million it has invested in recent years—to greatly expand its work in this burgeoning field.
The commitment includes the creation of an Institute for Data Science and the construction of a state-of-the-art building to house it. The University will also recruit as many as 20 new faculty members in many departments in which data science plays a critical role: biostatistics, brain and cognitive sciences, computer science, physics, political science, psychiatry, and others.
New Campus Landmark
The Institute for Data Science will become the anchor of the new Science and Engineering Quadrangle on the River Campus. Construction of the building will facilitate centralization of formerly dispersed areas and be the catalyst for reconfiguration and renovation of research and lab spaces.
The building will also extend the collegiate character of the Eastman Quad to a new area on campus. It creates a distinctive space where someone can walk in any direction and engage with faculty members in medicine, the humanities, education, and business. This hub for data science–related programs further enriches the collaborative environment for which Rochester is well known.
Initial Areas of Focus
The institute will build on the University’s strengths, which are currently dispersed across many departments and divisions. While there are collaborations among research groups, there has not been an umbrella organization to bring them together.
The institute will enable the coalescence of multiple centers, including those that exist now as well as those that are planned as part of the University’s commitment to data science. It will also put researchers together “under one roof” to collaborate and harnesses the potential of data science. Learn more here.
Data science research will focus initially on these three areas.
Predictive health analytics: Using data to predict individual health outcomes on the basis of treatments, genomics, and lifestyle and behavioral factors may lead to some of the biggest advances in health care. The University is already a leader in tracking and developing methods to control the spread of infectious diseases and is home to a world center for the collection and analysis of cardiac data.
Cognitive systems and artificial intelligence: Home of internationally recognized research in cognitive science and artificial intelligence, Rochester is uniquely positioned to advance our understanding of how the brain makes sense of the world. Modeling and replicating human perception is one of the most ambitious and exciting domains in data science.
Analytics on demand: Analyzing large-scale data requires the appropriate tools—a challenge that some of the institute’s faculty members will address. The ultimate goal is to relieve the end user from the need to understand details of a platform in order to have the computer system determine the optimal use of resources.
Learn more about the University’s:
“It would be difficult to overestimate the advances in science, medicine, and technology that will be possible when instead of having one Einstein per generation, there are thousands or millions of equivalently endowed artificial intelligent agents.”
—Henry Kautz, chair of the Department of Computer Science and director of the Institute for Data Science