
NSF Org: |
DMS Division Of Mathematical Sciences |
Recipient: |
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Initial Amendment Date: | May 18, 2023 |
Latest Amendment Date: | June 25, 2024 |
Award Number: | 2246576 |
Award Instrument: | Continuing Grant |
Program Manager: |
Elizabeth Wilmer
ewilmer@nsf.gov (703)292-7021 DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
Start Date: | July 1, 2023 |
End Date: | June 30, 2028 (Estimated) |
Total Intended Award Amount: | $499,977.00 |
Total Awarded Amount to Date: | $190,644.00 |
Funds Obligated to Date: |
FY 2024 = $165,048.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
615 W 131ST ST NEW YORK NY US 10027-7922 (212)854-6851 |
Sponsor Congressional District: |
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Primary Place of Performance: |
2990 Broadway, RM 509, MC 4406 New York NY US 10027-6940 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | PROBABILITY |
Primary Program Source: |
01002728DB NSF RESEARCH & RELATED ACTIVIT 01002627DB NSF RESEARCH & RELATED ACTIVIT 01002526DB NSF RESEARCH & RELATED ACTIVIT 01002324DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): | |
Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.049 |
ABSTRACT
Probability, as a field, tries to address the question of how large complex random systems behave. An important class of probabilistic models deals with growth in random media. These can be used to model how a cancer grows in a particular organ, how a car moves through traffic on a highway, how neurons move through the brain, or how disease moves through a population. The purpose of this project is to understand important models for growth in random media, both in terms of developing statistical distributions associated with the models and in terms of understanding in what sort of systems these models are relevant. The project will leverage new tools to solve previously inaccessible problems. The project includes a range of broader impact activities, including the organization of scientific, education, diversity/equity/inclusion, and outreach programs; advising and mentoring junior researchers; and serving on editorial and scientific boards and committees.
Stochastic PDEs, random walks in random media, interacting particle systems, six vertex model, and Gibbs states are active areas of study within probability, equilibrium and non-equilibrium statistical physics, combinatorics, analysis and representation theory. This project touches on problems in and draws upon tools from each of these areas. In particular, this project will probe (1) the nature of invariant measures and mixing times for various growth models in contact with boundaries, (2) the behavior of multi-class particle systems and the propagation of perturbations in growth dynamics, and (3) the fluctuations of interfaces, in particular the likelihood of upper and lower deviation probabilities along with various applications. By marrying integrable structures (e.g. Yang-Baxter equation, symmetric functions, determinantal processes, matrix product ansastz) with probabilistic methods (e.g. couplings, Gibbsian properties, hydrodynamic / stochastic PDE limits) the PI will solve problems in both areas which were previously inaccessible from either approach alone.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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