The University of Illinois, Champaign-Urbana has been selected by NSF to lead one of the five new … [+]
The National Science Foundation (NSF) announced last week that it would invest $75 million to establish five new Harnessing the Data Revolution Institutes. The institutes will bring together researchers and engineers in data science to develop and apply new technologies in several areas of science spanning biology, physics, neuroscience, geophysics, and climate change.
Each of the five institutes will be centered at a lead university that will coordinate investigations at multiple collaborating institutions.
The Harnessing the Data Revolution institutes represent one of the 10 “Big Ideas” set forth by NSF in 2016 as it identified key areas for future investment at the frontiers of science and engineering. They are the latest example of the agency putting significant money behind this research agenda.
“These new institutes will lead innovation in data science,” said Manish Parashar, office director for NSF’s Office of Advanced Cyberinfrastructure. “They position our nation at the cutting edge of global science and engineering by bringing together diverse perspectives to support convergent research.”
Here are summaries of each of the five awards, which are envisioned to have a four or five-year duration and are funded at $13- $15.5 million each.
The NSF Institute for a New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning will be led by The Ohio State University. The institute will establish the new field of Imageomics, in which biologists use machine learning algorithms to analyze vast amounts of images of living organisms gathered from national centers, field stations, museums and individual laboratories.
The project will involve collaborations between Ohio State researchers and co-investigators from Tulane University, Virginia Tech, Duke University and Rensselaer Polytechnic Institute. Other collaborators will be drawn from more than 30 universities and organizations around the world.
Imageomics is expected to transform the biomedical, agricultural, and basic biological sciences as it provides scientist with a better understanding of how genetically controlled traits interact with the environment. Those discoveries should in turn bolster the nation’s bioeconomy and expand public understanding of the rules and evolution of life on Earth.
Led by the University of Washington, the NSF Institute for Accelerated AI Algorithms for Data-Driven Discovery will be designed to advance knowledge that’s essential to new applications of artificial intelligence in three fields of science: physics, astrophysics and neuroscience.
The institute will develop artificial intelligence (AI) solutions to process large datasets in real time. The ultimate goal is to facilitate real-time applications of AI in any scientific field, helping scientists make sense of what some have called a “data deluge.”
Students involved in this research will interact closely with industry partners, creating new career opportunities and strengthening synergies between academia and industry. The institute will include researchers at the University of Washington; the University of Illinois at Urbana-Champaign; Duke University; the Massachusetts Institute of Technology; the University of Minnesota, Twin Cities; the California Institute of Technology; Purdue University; the University of California, San Diego; and the University of Wisconsin–Madison.
The NSF Institute for Harnessing Data and Model Revolution in the Polar Regions will be directed by the University of Maryland, Baltimore County (UMBC). It will serve as a research hub where experts in fields such as data science, Arctic and Antarctic science and cyberinfrastructure work together to study climate change and the rapidly changing Arctic. In addition to UMBC faculty, the institute will feature investigators from a number of other universities, as well as government and industry researchers.
The work of the institute is intended to give us a better understanding of how climate change is causing the loss of polar ice sheets, leading to impacts like rising sea levels and coastal flooding. Because of the fundamental nature of the data science problems this institute will explore, it’s hoped that it can identify solutions that can be translated to other disciplines, including remote sensing, medicine, and autonomous driving.
The NSF Institute for Data Driven Dynamical Design will be led by the Colorado School of Mines. It will focus on applying machine intelligence to materials science with an emphasis on predicting processes such as ion and molecular transport, catalytic pathways, and transformation of metamaterials.
The institute’s data science discoveries will have implications for several scientific fields including molecular biology, atmospheric science, geophysics, robotics and cosmology. It also should lead to discoveries with implications for cyberinfrastructure development.
Another aim of this institute is to build a stronger educational pathway in big data and STEM careers through outreach activities to high school coding schools, undergraduate training in data-rich research, and a post-baccalaureate bridge program that introduces students to data sciences and motivates them to pursue advanced degrees. Data scientists, engineers, physicists, chemists and material scientists from 11 other institutions across the U.S. will be involved in the project.
Led by the University of Illinois Urbana- Champaign, the NSF Institute for Geospatial Understanding through an Integrative Discovery Environment will be a multidisciplinary effort using geospatial data to learn more about the interconnections between socioeconomic-environmental systems. The institute will develop tools that increase the ability to estimate and predict risk and anticipate impacts from disasters or climate change.
Institute partners from academic, governmental, and industrial institutions will focus on real-world problems such as water security, biodiversity and food security. About 40 researchers from several institutions will be involved, representing fields such as computer science, atmospheric science, ecology, economics, environmental engineering, geographical sciences, hydrology, industrial engineering, sociology, and statistics.
You can find more information about Harnessing the Data Revolution here..