This Project Grant award, with a total funding of $549,999, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program. The primary objective is to develop a time-sensitive large model training platform for dynamic data analytics, enabling real-time adaptability of large-scale deep learning models across various applications such as climate modeling, traffic management, and virtual infrastructure twins. The...
This $600,000 federal Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) aims to integrate federated learning with power systems to better predict electricity consumption and lower the cost of electricity generation. The project will develop machine learning methods, specifically recurrent neural networks, to forecast day-ahead electricity consumption using distributed data from smart meters while preserving consumer privacy. Key scientific...
This National Science Foundation (NSF) Project Grant award of $200,000 to Kansas State University, under the Mathematical and Physical Sciences program (CFDA 47.049), aims to develop and validate deep-learning-enabled distributed stochastic algorithms to solve large-scale, stochastic security-constrained unit commitment problems within power systems. The project will focus on designing a holistic, three-stage, deep neural network-based machine learning approach, developing solution strategies...
This $393,890 federal Project Grant, awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041), aims to advance the autonomy of power grids by developing innovative strategies to enhance decision-making speed, resilience, and sustainability awareness in distributed grid management models and algorithms. The key products and services to be delivered through this 5-year award include: 1) Novel machine learning-assisted optimizers to rapidly solve complex...
The National Science Foundation (NSF) awarded a $484,965 project grant under the Engineering (CFDA 47.041) program to New York University (NYU) to develop transformative concepts and methodologies to enhance situational awareness of electric power distribution systems. The project aims to address challenges in integrating distributed renewable energy generation by enabling real-time tracking of distribution system operating states. Key objectives include learning-based continuous-time system...
The National Science Foundation (NSF) awarded a $1,200,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the University of Texas at Austin. The grant, running from June 1, 2024 to May 31, 2027, aims to develop theoretical frameworks and practical algorithms for learning data-driven models and control strategies in networked cyber-physical systems, with a focus on power distribution systems. Key areas of work include designing...
This Project Grant award of $240,000.00 from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) supports collaborative research to develop novel topological and graph-based modeling techniques for integrating large-scale, distributed energy resources into power grid systems. The key objectives are to create a data-adaptive graph generation module, apply topological data analysis and higher-order network models, and design deep neural...
This Project Grant award of $160,673 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to combine algorithms and machine learning to improve decision-making under uncertainty. The project, led by New York University (NYU), will explore incorporating machine-learned predictions into algorithm design as well as developing learning models optimized for specific algorithmic objectives. This work aims to create a...
This $948,000 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support the development of new methods for data-efficient decision-focused learning to address uncertainty in various real-world decision-making problems. The research aims to create a general framework for pre-training key components and rapidly fine-tuning them for specific decision-making tasks, such as in public health,...
The National Science Foundation (NSF) awarded a $237,028 Project Grant to New York University (NYU) under the Computer and Information Science and Engineering program (CFDA 47.070) to develop statistical and algorithmic foundations for robust policy learning in uncertain environments. The goal is to create provably efficient techniques for learning optimization policies that can be deployed in practical settings where the training and operational environments differ, such as when using digital...