The National Science Foundation awarded a $500,000 Project Grant to the Texas A&M Engineering Experiment Station to advance optimization for threshold-agnostic fair artificial intelligence systems under the Computer and Information Science and Engineering program (CFDA 47.070). The three-year project aims to develop scalable stochastic optimization algorithms and novel threshold-agnostic fairness measures to directly optimize machine learning models for fairness without reliance on...
The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $500,000 Project Grant to Texas A&M Engineering Experiment Station, doing business as Tees, to support research towards a principled framework for resilient, data efficient and scalable reinforcement learning for control. The award period is from February 1, 2021 through January 31, 2026. The research is funded under the NSF Directorate for Engineering's Engineering program (CFDA 47.041), which...
Northeastern University was awarded a $250,000 Project Grant from the National Science Foundation Division of Electrical, Communications and Cyber Systems to support research titled "COLLABORATIVE RESEARCH: CONSENSUS AND DISTRIBUTED OPTIMIZATION IN NON-CONVEX ENVIRONMENTS WITH APPLICATIONS TO NETWORKED MACHINE LEARNING." Under the grant, Northeastern University will conduct research and education activities to advance the fundamental understanding of distributed non-convex optimization...
The National Science Foundation Division of Mathematical Sciences awarded Texas A&M Engineering Experiment Station a $180,000 Project Grant under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) from August 1, 2023 through July 31, 2026. The award will support research to develop a systematic approach for constructing deep Bayesian neural networks that are both computationally efficient and amenable to model designs. The research is expected to lead to...
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...
The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $224,375 Project Grant to the University of Texas at Austin from September 15, 2021 through August 31, 2024. The grant supports collaborative research to develop computationally efficient algorithms for large-scale bilevel optimization problems under the NSF Engineering program (CFDA 47.041). The Engineering program seeks to improve quality of life and economic strength by fostering innovation...
The Texas A&M Engineering Experiment Station was awarded a $325,000 Project Grant from the National Science Foundation Division of Computing and Communication Foundations under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The three-year grant, awarded October 1, 2021 and set to be completed by September 30, 2024, will support research into developing low-complexity algorithms for unsourced multiple access and compressed sensing in large...
This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) provides $415,754 to the Texas A&M Engineering Experiment Station (Tees) to advance computational methods for accurately characterizing the mechanical behavior of critical materials. The key products and services to be delivered include: Developing a global programming "epsilon-optimal" spatial branching technique using a novel class of efficient convex underestimators. An...
The University of Texas at Austin was awarded a $220,000 Project Grant from the National Science Foundation Division of Electrical, Communications and Cyber Systems. The award is part of the Engineering (47.041) federal grant program to support collaborative research titled "CCSS: LEARNING TO OPTIMIZE: FROM NEW ALGORITHMS TO NEW THEORY" from August 15, 2021 to July 31, 2024. Under this award, the University of Texas at Austin will conduct research to develop new algorithms and...
This National Science Foundation (NSF) Engineering Program (CFDA 47.041) Project Grant award of $517,612 to the University of Texas at Austin (UT Austin) will develop new foundations of scalable and resilient distributed reinforcement learning for real-time autonomous cooperation in open multi-agent systems. The overarching goal is to design new learning and control methods that enable agents to interact effectively in open systems, adapt in time-varying environments, and be resilient to...