Project Grant FA95502510341

This National Science Foundation (NSF) Engineering Program (CFDA 47.041) Project Grant award of $550,213 will fund research to develop novel machine learning-enabled cutting plane strategies for optimization problems in applications such as power systems, logistics, and healthcare. The primary objective is to create efficient learning-based cut and disjunction selection techniques that can improve the performance and capabilities of commercial optimization solvers. The project, titled...

This National Science Foundation (NSF) Engineering Program grant, awarded to Oregon State University (OSU), supports fundamental research on a novel metal cutting method called constrained cutting. This $630,687 Project Grant, with a period of performance from July 1, 2024, to June 30, 2027, aims to investigate how constraining the chip formation process can improve the efficiency of machining difficult-to-cut materials. The research focuses on analyzing the mechanics, dynamics, and surface...

This Project Grant award of $220,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research by Rensselaer Polytechnic Institute (RPI) to develop new stochastic optimization algorithms for solving nonconvex nonsmooth minimax problems. The project aims to create software packages that can improve the robustness of deep learning models, which are often vulnerable to adversarial attacks. The research will explore various...

This Project Grant award of $661,515 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports collaborative research to address fundamental challenges in discrete and continuous optimization. The key research thrusts include: Understanding the complexity of the widely used interior-point method for optimization, both in the worst case and on average. Developing continuous methods for solving discrete optimization problems, particularly...

This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Program (CFDA #47.070) provides $525,000 in funding to The Johns Hopkins University to conduct collaborative research on "Post-Modern Min-Max Optimization Theory". The project aims to develop specialized theoretical frameworks and efficient algorithms tailored explicitly to min-max optimization, which underpins technologies ranging from generative AI...

This three-year Project Grant from the National Science Foundation's Division of Computing and Communication Foundations under the Computer and Information Science and Engineering program will support the development of new optimization approaches for machine learning problems. The $600,000 award to the University of Wisconsin-Madison beginning October 1, 2022 will advance optimization algorithms and analysis techniques for convex-concave minimax problems incorporating sparsity or regularity....

This $199,996 project grant awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) aims to develop a principled approach to the systematic design of efficient iterative algorithms for a wide variety of data-driven applications. The project at Miami University will leverage tools from both optimization and control theory, including techniques like interpolation, Lyapunov stability, and robust control synthesis, to enable the deployment of specialized and efficient...

This Project Grant award of $148,127 was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The grant focuses on developing efficient and reliable methods for finding optimal balanced graph cuts, which have applications in materials science and social sciences. The research aims to address fundamental challenges in partitioning large data sets into prescribed subsets, with potential impacts across fields such as...

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, with CFDA Number 47.070, will support the development of new algorithmic techniques to achieve favorable tradeoffs between the robustness of machine learning (ML) algorithms and compression. The goal is to enable the deployment of robust ML algorithms on edge devices, such as phones, sensors, and IoT devices, which have computational and power limitations. The...

The National Science Foundation (NSF) Division of Mathematical Sciences has awarded a $546,334 Project Grant to The Leland Stanford Junior University, also known as Stanford University, to conduct research on "Stability in Geometric Variational Problems." This award, under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), will support efforts to advance the mathematical understanding of optimal configurations that minimize energy in various scientific and...