NSF Org:	DMS Division Of Mathematical Sciences
Recipient:	UNIVERSITY OF CHICAGO
Initial Amendment Date:	July 13, 2020
Latest Amendment Date:	August 21, 2024
Award Number:	1929348
Award Instrument:	Continuing Grant
Program Manager:	Yong Zeng yzeng@nsf.gov  (703)292-7299 DMS  Division Of Mathematical Sciences MPS  Directorate for Mathematical and Physical Sciences
Start Date:	August 1, 2020
End Date:	July 31, 2026 (Estimated)
Total Intended Award Amount:	$15,500,000.00
Total Awarded Amount to Date:	$15,500,000.00
Funds Obligated to Date:	FY 2020 = $2,000,000.00 FY 2021 = $3,000,000.00 FY 2022 = $7,000,000.00 FY 2024 = $3,500,000.00
History of Investigator:	Kevin Corlette (Principal Investigator) kevin@math.uchicago.edu Douglas Simpson (Co-Principal Investigator) Brooke Shipley (Co-Principal Investigator) Lee DeVille (Co-Principal Investigator) Elizabeth Tipton (Co-Principal Investigator) Bryna Kra (Former Co-Principal Investigator) Jeremy Tyson (Former Co-Principal Investigator)
Recipient Sponsored Research Office:	University of Chicago 5801 S ELLIS AVE CHICAGO IL  US  60637-5418 (773)702-8669
Sponsor Congressional District:	01
Primary Place of Performance:	University of Chicago 5734 S. University Ave Chicago IL  US  60637-1501
Primary Place of Performance Congressional District:	01
Unique Entity Identifier (UEI):	ZUE9HKT2CLC9
Parent UEI:	ZUE9HKT2CLC9
NSF Program(s):	MATHEMATICAL SCIENCES RES INST
Primary Program Source:	01002122DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT 01002223DB NSF RESEARCH & RELATED ACTIVIT 01002425DB NSF RESEARCH & RELATED ACTIVIT 01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s):	733300
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.049

ABSTRACT

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.

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