Two faculty teams at the University of North Carolina at Charlotte have received awards in phase 1 of the National Science Foundation's Convergence Accelerator program, a major new research investment by the agency, designed to "accelerate use-inspired convergence research in areas of national importance."
According to NSF, "Convergence research is a means of solving vexing research problems, in particular, complex problems focusing on societal needs. It entails integrating knowledge, methods, and expertise from different disciplines and forming novel frameworks to catalyze scientific discovery and innovation." Convergence research typically involves participants who span different research fields and projects that aim for "deep integration" across the disciplines, driven by the need to solve a specific and compelling problem.
Convergence research involves creating new fields of inquiry and new cohorts of researchers. "As experts from different disciplines pursue common research challenges, their knowledge, theories, methods, data, research communities and languages become increasingly intermingled or integrated. New frameworks, paradigms or even disciplines can form sustained interactions across multiple communities," NSF explains.
The NSF Convergence Accelerator is a pilot program aimed at accelerating use-inspired convergence research in areas of national importance, and "initiating convergence team-building capacity around exploratory, potentially high-risk proposals." 43 Phase 1 awards for short-term projects (up to 9 months long) have been funded for up to $1 million each.
"The Convergence Accelerator is a unique new experiment for NSF," said Douglas Maughan, NSF's Convergence Accelerator office head. "For decades, traditionally the private sector has not been able to justify investment in basic research due to a lack of obvious commercial applications. With the Convergence Accelerator, we're challenging that notion, engaging with partners who can use their experience to help us support research that can change and enhance American society."
"We are pleased that two of our research teams have been successful in competing for the pilot for this important new NSF program," said Rick Tankersley, UNC Charlotte Vice Chancellor for Research and Economic Development. "We have been focusing for a while now on research that crosses traditional boundaries and forges new disciplines, and this shows how effectively our faculty have embraced the challenges involved."
"Building the Federal Data and Advanced Statistics Hub" is one of the two UNC Charlotte Convergence Accelerator awards, and is in the program track NSF calls "Open Knowledge Networks" - projects selected because they "enable new modes of data-driven discovery" by "fully harnessing the data revolution" and making the data accessible and useful. This project, led by UNC Charlotte political scientist Jason Windett, aims to produce a digital resource, the "Federal Data and Advanced Statistics Hub" (F-DASH, for short), which will be "a single, comprehensive source of data and analysis on governing institutions and public policies in the American states."
F-DASH, Windett explains, will have two basic capabilities that will make it useful to academics and other public researchers interested in analyzing diverse state government policies: "First, it will integrate an unprecedented amount of data on politics, policy, and economic and social outcomes in the states. Second, F-DASH will develop the analytic tools that will allow users to easily explore, visualize, and analyze this data."
To achieve this, Windett notes a very high level of collaboration will be required with partner organizations and researchers in a variety of different disciplines. "The project will emphasize the value of convergent research by drawing from a wide range of disciplines in its development, including applied statistics, computer science, geography, philosophy, political science, public policy, and sociology," he said. "It also draws on technical expertise and channels for promoting the work through collaborations with several partner organizations, such as Open States, the Society for Public Health Educators, and the State Politics and Policy Section of the American Political Science Association."
Windett's research team includes Samira Shaikh, Stephanie Moller, Gordon Hull, Matt Parker and Rick Hudson from UNC Charlotte; Isaac Cho from North Carolina A&T State University; Nathaniel Birkhead and Audrey Joslin from Kansas State University; Jeffrey Harden from the University of Notre Dame; Mary Kroeger from the University of Rochester; and Justin Kirkland from the University of Virginia.
F-DASH should have "a broad potential impact and a wide societal benefit for the public, educators, and researchers," he said. The collaborative project "will facilitate new engagement by citizens, new classroom materials for teachers, innovations by practitioners, and breakthroughs by researchers."
"Public policy at the state level has a direct impact on citizens' daily lives, including their health, education, and employment. This first phase F-DASH development will provide immediate and accessible information on these very topics," Windett said.
The project is really aimed at solving a fundamental problem in American public policy research, a field that studies how representative government in translates citizen preferences into laws and impacts society. "The American states represent an ideal venue for understanding the effects of policy choices because states routinely experiment with different solutions to important societal problems," Windett explained, while noting a daunting obstacle to analysis: "The decentralized nature of the American federal system impedes efforts to compare social, economic, or other outcomes across the states. Indeed, researchers must often engage in 50 separate data collection processes, reflecting the unique challenges of data acquisition in each state."
"Consequently, researchers or government agencies commonly collect only the minimum of what they need to address a particular question. Moreover, they often do not share these data, and those who do make data publicly available have no obvious way to connect their data to data collected by others," he said. "The F-DASH will provide a centralized place for all data collection and analysis related to institutions, policies, and their implications in the American states."
Adding to the centralized databases, the finished project should allow researchers to experiment with the use of a variety of data science tools that aid analysis. "F-DASH will promote innovation in complex database design and provide an extensive testbed for researchers interested in text mining, large-scale relational databases, static and dynamic network analysis, and many other topics," Windett said.
The second grant awarded to UNC Charlotte in the Convergence Accelerator program is a project entitled "Smart Platform of Personalized Learning, Assessment and Prediction for Future Coordinated Training of Skilled Workers," selected as part of the program's "National Talent Ecosystem" award track. Led by UNC Charlotte computer scientist Aidong Lu, phase 1 of this project aims to develop a smart, AI-driven training platform, making use of advances in virtual reality/mixed reality, smart sensing, and kinesiology for training firefighters, with an eye to further applications in other fields requiring technical training, such as smart manufacturing and healthcare.
The team is composed of researchers Pu Wang, Weichao Wang, Abbey Thomas, Luke Donovan from UNC Charlotte, Xintao Wu from University of Arkansas, Aixi Zhou from NC A&T, and UNC Charlotte Ventureprise director Devin Collins.
Driven by the fact that advances in technology require skilled workers to receive training - and nearly continuous re-training - in technical fields where traditional training has often been difficult to make sufficiently available to meet workforce needs, Lu's platform will offer low-cost, easily arranged training - and even personal training, injury prevention, instruction in tech skills, continuous assessment and individualized instruction.
"Our goal is to develop the next generation platform of personalized training with data-centric approaches toward deep digitalization," the project proposal explains. "As techniques such as big data and AI are changing our world with a new industry horizon, digitalization has been viewed as an unavoidable process of training innovation. We also anticipate that our techniques will connect and coordinate all participants including students, workers, educators, administrators, and policy makers."
Developing a smart training platform for firefighters was chosen as the first phase of the project, as modern firefighting involves ever-increasing use of technology, requires significant resources for traditional training, and training involves hazardous conditions where injury prevention is an important issue.
"In this phase 1 project, we propose to focus on the training of firefighters," the proposal continues. "Similar to other occupations of skilled workers, the future of firefighters will involve both professional skills and STEM skills including working with various types of data and machines. Currently, firefighter is listed among the top 10 most dangerous jobs in America. The training of firefighters is important to both social and economic well-being of firefighters and millions of people through better protecting properties and environments."
This project will develop a smart training platform through embedding advanced techniques of big data, AI, smart sensing, mixed reality, kinesiology and fire engineering. The results could accelerate the change from traditional training programs to new types of training and certification related to the latest technology advances. As the proposal describes: "We plan to build immersive trainers (for virtual and mixed realities) that can be applied across education and training organizations, and develop a comprehensive suite of training functions including action recognition, injury prevention, personalized modeling, assessment and prediction, and immersive analytics. Our platform contains both innovations in sensing to create smart environments with non-intrusive techniques and methodology advances of AI, Kinesiology, MR and immersive analytics for modeling, evaluating, and predicting worker performances."
Lu stresses that the smart training platform is planned not simply as a substitute for traditional training, but as a significant improvement, where instructors can receive constant information on trainee progress and trainees receive constant personalized feedback and can adapt their training program to meet their individual needs. "The goal is to make learning deeper and training more effective," Lu said.
Lu's Convergence Accelerator project involves collaboration with a number of partners in education and training, including the University of Arkansas, North Carolina Agricultural and Technical State University and the Charlotte Fire Department.
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The NSF award announcement can be seen at https://www.nsf.gov/news/news_summ.jsp?cntn_id=299179&org=NSF&from=news. NSF award numbers are 1937003 (Windett) and 1937010 (Lu).