NSF grant to fund multidisciplinary data science research
An Ohio State University team of researchers is among the first to receive funding from a new National Science Foundation (NSF) initiative to accelerate data science and discovery through multidisciplinary collaboration.
The three-year, $1.5 million grant will support research led by Computer Science and Engineering Professor Tamal Dey, who is also a professor of mathematics. Dey and his team will leverage geometry and topology to address challenges inherent with increasingly complex data. Through algorithms based on geometric and topological approaches, the researchers hope to discover, model and reveal structure, shape and underlying dynamics of big data.
Their findings could benefit a wide range of fields including medicine, machine learning, geographic information systems, mechanical engineering designs and even political science. The research products will be implemented and disseminated through software packages and tutorials, allowing widespread application in both academia and industry.
“One specific focus of this project is to compute repetitive structures and their anomalies in data such as the ones generated by a healthy or a defective heart,” said Dey.
Beyond the research implications, the team will develop curricula for cross-disciplinary, undergraduate and graduate education. Ohio State’s Translational Data Analytics Institute, Mathematical Biosciences Institute and Data Analytics undergraduate major provide immediate extension and partnership opportunities. The grant also will fund STEM outreach, research seminars, training workshops and summer schools.
Ohio State’s award represents one of 12 Transdisciplinary Research in Principles of Data Science (TRIPODS) projects that earned a collective $17.7 million in NSF funding. These TRIPODS Phase I awards will enable data-driven discovery through major investments in state-of-the-art mathematical and statistical tools, better data mining and machine learning approaches, enhanced visualization capabilities and more.
“Data is accelerating the pace of scientific discovery and innovation,” said Jim Kurose, NSF assistant director for Computer and Information Science and Engineering (CISE). “These new TRIPODS projects will help build the theoretical foundations of data science that will enable continued data-driven discovery and breakthroughs across all fields of science and engineering.”
A future TRIPODS Phase II will select awardees through a second competitive proposal process from among the Phase I institutes, as well as any new collaborative partners Phase I awardees bring on board.
