The Institute for Mathematical and Statistical Innovation (IMSI) will apply mathematics and statistics to urgent, complex scientific and societal problems, and spur transformational change in the mathematical sciences communities. The Institute aims to catalyze new mathematical and statistical approaches to further understanding of complex phenomena and issues of great societal interest. As part of its mission, IMSI will facilitate rapid and effective dissemination of these advances to both the mathematical sciences research community and broader audiences. IMSI will also train a new generation of mathematicians and statisticians equipped to advance multidisciplinary research through immersion in the challenges encountered within and beyond the mathematical sciences. All of the IMSI programs will foster diversity and inclusion in the mathematical sciences and engage in impactful outreach programs.
IMSI will address major challenges facing society by building conduits from the core disciplines of mathematics and statistics to a broad range of disciplines and applications that need mathematical and statistical insights to understand the dynamics of data- and computation-intensive phenomena. IMSI research affiliates will immerse themselves in major projects, organized around themes, driven by societal challenges such as climate forecasting, epidemiological modeling, economic crises, cancer-marker identification, and neural processing. Conceptual and technical challenges from interdisciplinary applications will create the impetus to advance mathematical and statistical approaches that are urgently needed to understand the principles governing such systems and to make accurate predictions along with quantifying the uncertainty of the predictions. Mathematical scientists at IMSI will be systematically exposed to and deeply engaged with these grand challenges. IMSI programs will bring together mathematical scientists and researchers working in these fields to promote collaborations on important problems and to enrich fundamental mathematics and statistics. One mechanism for achieving this will be through the development of national and international collaborations and partnerships with industry and national labs. The initial long programs will explore distributed decision making processes and the impact of uncertainty in the development of effective models in the social sciences. In addition, workshops will explore topics such as computational materials science, the complexity of machine learning tasks, dimension reduction methods in genomics, topological data analysis, optimized decision making in health care, and the development of verification, validation, and uncertainty quantification methods across disciplines.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
