Ten Big Ideas from the National Science Foundation

By Amy Nussbaum posted 08-29-2016 09:45


The National Science Foundation is shaping their strategy for both the incoming administration and the next several years, by building on ten Big Ideas for Future NSF Investments, first discussed by NSF Director France Cordova in May of this year.  These ideas will likely involve researchers from multiple disciplines as well as collaborators from industry, academia, and other foundations—here are five that may specifically interest the statistical community.  

Two initiatives dealing with special content are the “Understanding the Rules of Life: Predicting Phenotype” and “Harnessing Data for 21st Century Science and Engineering” initiatives. The hope behind the first initiative is that researchers will develop the ability to predict physical characteristics of biological organisms. Although much is known about genetics, this knowledge does not necessarily translate into the ability to predict phenotypes since so many other factors can influence observable traits. Phenotype prediction represents a huge challenge in the recent Precision Medicine Initiative, which promises to tailor treatments to specific individuals based on various factors such as the patient’s genome). The NSF specifically mentions data integration, analysis, modeling, and informatics techniques as potential research areas. The second initiative is a nationwide effort to create a format for research data as well as train the next generation to effectively work with big data and make data-driven decisions.  It will support basic research in math, statistics and computer science in order to make discoveries based on massive amounts of data from a wide variety of sources, as well as create pathways to careers in data science. Many different stakeholders in academia, government, and industry have been using big data to solve a wide variety of problems, but the methods they use come with their own set of challenges—some of which can and should be addressed by statisticians. Again, statisticians will be essential to both initiatives, and should keep an eye on developments in the field as well as research opportunities.

There are several other ideas that are more concerned with the infrastructure of the NSF. In FY16, the NSF launched the Inclusion across the Nation of Communities of Learners of Underrepresented Discoverers in Engineering and Science program, otherwise known as INCLUDES.  The purpose of INCLUDES is to broaden participation in STEM in a way that fully reflects the diversity of the U.S. Specifically, the goal is to engage more representatives of traditionally under-represented groups  including women, different races, persons with disabilities, people from rural areas and people of low socioeconomic status. Another proposed initiative is “NSF 2050: The Integrative Foundational Fund”. The goal of this initiative is to rethink the structure of the community to allow bolder long term research ideas. Rather than planning long term research within specific directorates, research agendas can be developed in such a way that it cuts across the directorates and areas that might usually fall through the cracks can be more fully explored.  Finally, “Growing Convergent Research at NSF” serves to solves today’s grand challenges by combining approaches from different disciplines. NSF already has a history of fostering multidisciplinary projects, but shifting investments to research projects based on societal problems can help create a truly convergent system.

As the NSF continues to develop their research agenda, both statisticians and researchers from other disciplines should remain alert for opportunities to engage the statistical community, as several of the initiatives have benefited from statistical contributions already. Not only is multidisciplinary research a priority in and of itself, but statisticians can truly make the science better.