| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | June 13, 2022 |
| Latest Amendment Date: | July 22, 2024 |
| Award Number: | 2210849 |
| 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: | July 1, 2022 |
| End Date: | June 30, 2026 (Estimated) |
| Total Intended Award Amount: | $200,000.00 |
| Total Awarded Amount to Date: | $200,000.00 |
| Funds Obligated to Date: |
FY 2023 = $87,049.00 FY 2024 = $89,660.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
3 RUTGERS PLZ NEW BRUNSWICK NJ US 08901-8559 (848)932-0150 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
110 Frelinghuysen Road Piscataway NJ US 08854-8019 |
| 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): | STATISTICS |
| Primary Program Source: |
01002425DB 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
Markov chain Monte Carlo methods have revolutionized statistics and data science in the past several decades. These methods are routinely used for simulation and numerical integration in nearly all scientific areas. In practice, however, parallel implementation is a long-standing bottleneck for Markov chain Monte Carlo methods. Existing Monte Carlo estimators generally suffer from bias, which precludes their direct use of modern parallel computing devices. This project aims to design a new framework to construct unbiased estimators based on Markov chain Monte Carlo outputs. Results of the project will advance the development of unbiased estimators and efficient algorithms that can scale up for massive datasets. The new method will empower practitioners in scientific fields such as chemistry, biology, and computer science that face high-dimensional simulation problems. This project will provide training opportunities to undergraduate and graduate students.
The technical goals of this project include two interconnected aspects. The first focus is on unbiased estimators for general simulation-based inference problems by combining the idea of the existing unbiased Markov chain Monte Carlo and Multilevel Monte Carlo methods. The second aspect focuses on designing fast algorithms for unbiased estimation. The efficiency of the developed method relies on the underlying Markov chain Monte Carlo algorithm and the design of a coupling strategy between the two Markov chains. This project will theoretically investigate the convergence speed of different existing algorithms and develop practical, implementable algorithms. The new method will be applied to solve problems arising from diverse areas such as operation research, optimization, and machine learning.
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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