NSF Org:	CCF Division of Computing and Communication Foundations
Recipient:	BOARD OF REGENTS OF THE UNIVERSITY OF NEBRASKA
Initial Amendment Date:	February 20, 2024
Latest Amendment Date:	February 20, 2024
Award Number:	2342244
Award Instrument:	Standard Grant
Program Manager:	Karl Wimmer kwimmer@nsf.gov  (703)292-2095 CCF  Division of Computing and Communication Foundations CSE  Directorate for Computer and Information Science and Engineering
Start Date:	March 1, 2024
End Date:	February 28, 2027 (Estimated)
Total Intended Award Amount:	$337,697.00
Total Awarded Amount to Date:	$337,697.00
Funds Obligated to Date:	FY 2024 = $337,697.00
History of Investigator:	Vinodchandran Variyam (Principal Investigator) vinod@cse.unl.edu
Recipient Sponsored Research Office:	University of Nebraska-Lincoln 2200 VINE ST # 830861 LINCOLN NE  US  68503-2427 (402)472-3171
Sponsor Congressional District:	01
Primary Place of Performance:	University of Nebraska-Lincoln 366 Avery Hall LINCOLN NE  US  68588-0115
Primary Place of Performance Congressional District:	01
Unique Entity Identifier (UEI):	HTQ6K6NJFHA6
Parent UEI:
NSF Program(s):	Algorithmic Foundations
Primary Program Source:	01002425DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	079Z, 7923, 9150
Program Element Code(s):	779600
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Replicability and reproducibility in science are critical concerns. The fundamental requirement that scientific results and experiments be reproducible and replicable is central to the development and evolution of science. In recent years, these concerns have been even more amplified as several scientific disciplines are turning to data-driven research that enables exponential progress from data democratization and affordable computing resources. The replicability/reproducibility issue has received attention from a wide spectrum of entities ranging from general media publications to scientific publication venues to professional and scientific bodies. This project focuses on addressing the pivotal need for replicability and reproducibility in scientific research that involves randomized computations.

The project will explore new directions in algorithmic replicability. The approach is threefold: (1) defining and formalizing new concepts of replicability across various computational contexts, (2) developing methodologies for the design of replicable algorithms, and (3) investigating the limitations and costs associated with replicability. This initiative seeks to contribute fundamentally to the field of replicable and reproducible computations. It focuses on the development of innovative algorithms and establishing lower bounds in replicability. The research is expected to have a significant impact on areas such as machine learning and large data analysis. Beyond these immediate fields, the principles and techniques devised under this project are anticipated to influence a broader spectrum of scientific and technological domains.

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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