Project Grant N000142612071

This $211,135 federal Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to expand and improve the use of stochastic simulation models for optimal decision-making under uncertainty. The award, with a performance period from July 2024 to June 2027, will enhance the SIMOPT testbed of simulation optimization problems and solvers, making it more powerful, widely applicable, and aligned...

This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program provides $575,000 from August 1, 2025 to July 31, 2028 to the Trustees of Columbia University for "Collaborative Research: III: Medium: Incentives and Interventions for Robust Networked Data Exchange." The project aims to develop new theoretical foundations, algorithmic methods, and educational initiatives to improve data quality through...

This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research on algorithms for optimization problems with uncertain or incomplete data inputs. The $484,600 grant awarded to the Regents of the University of Michigan will fund the design and analysis of parallelizable algorithms for stochastic optimization, as well as algorithms for stochastic optimization with unknown probability...

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, with a total funding of $599,999.00 from January 1, 2026 to December 31, 2028, focuses on analyzing optimization problems where the solution is guaranteed to be robust against changes and disruptions in the environment. The project is concerned with modeling uncertainty through the use of mathematical programming, examining models that consist of quantified...

This $275,000 Project Grant from the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program supports research on optimization algorithms and digital twins constrained by partial differential equations (PDEs) that incorporate data to make decisions resilient to uncertainty. The research will develop: (1) inexact adaptive semismooth Newton and trust-region methods to solve these optimization problems; (2) primal dual...

The National Science Foundation awarded a $599,995 project grant to the University of Massachusetts under the Computer and Information Science and Engineering program (CFDA 47.070) for the period of September 1, 2022 through August 31, 2025. The grant funds research to develop scalable in-database prescriptive analytics capabilities for dynamic environments. Specifically, the university researchers will extend prior work on constrained optimization problems to handle data uncertainty and...

The National Science Foundation (NSF) Division of Chemical, Bioengineering, Environmental, and Transport Systems awarded a $384,315 Project Grant to the Georgia TECH Research Corp (also known as the Office of Sponsored Programs) to develop novel decomposition algorithms for solving large-scale, nonconvex stochastic programs. The goal is to create improved computational methods for making important decisions under uncertainty, which often arises in areas like critical infrastructure...

This $209,988 Project Grant award from the National Science Foundation (NSF) Integrative Activities (CFDA 47.083) program supports research at Clemson University to establish theoretical and algorithmic foundations for ensuring differential privacy in decentralized optimization algorithms without losing provable optimality. The key research thrusts include: Investigating the tradeoff between convergence speed and differential privacy in decentralized optimization, Exploring differential...

This $349,427 CAREER grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research at The Leland Stanford Junior University (Stanford University) to develop machine learning (ML) techniques to improve the performance of discrete optimization algorithms. The project aims to address challenges in efficiently solving complex combinatorial optimization problems, such as those encountered in supply chain logistics and...

This National Science Foundation project grant of $250,000 will fund research at Rensselaer Polytechnic Institute from July 2022 to June 2025 under the Mathematical and Physical Sciences program (CFDA 47.049). The grant supports the development of accelerated distributed stochastic optimization methods and applications in machine learning. Specifically, the grantee will design fast-convergent and communication-efficient optimization algorithms with theoretical guarantees for solving...