This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program aims to develop software frameworks that can efficiently serve and deploy machine learning models for a variety of AI-powered applications. The $600,000 award, spanning from October 2024 to September 2027, tasks the prime awardee, Georgia Tech Research Corporation, with creating agile mechanisms and policies to serve a family of AI models across...
This $199,040 Project Grant, awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to develop new classes of computational algorithms that combine the benefits of direct computer simulations and the speed of machine learning predictions. The project, titled "XTRIPODS: HYBRID SCIENCE-MACHINE LEARNING SOLVERS FOR NANOPHOTONICS AND METAMATERIALS," will embed scientific knowledge into the machine learning...
This $2,250,000 Project Grant award from the Office of Advanced Cyberinfrastructure within the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program seeks to develop a software platform and infrastructure for training machine learning (ML) models at scale to predict the properties of molecular and materials systems. The key objectives are to: Establish a technological paradigm and software infrastructure for developing ML models capable of predicting...
This $131,959 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research conducted by Rutgers, The State University to develop new deep learning training methods that can efficiently scale to utilize high-performance computing (HPC) systems. The key goals are to: 1) Explore techniques like second-order information approximation, computation-communication tradeoffs, and data compression to enhance the speed...
This $296,555 Project Grant from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support the development of reduced basis enhancements for neural networks and their application to quantum materials simulation. Specifically, the University of Massachusetts Dartmouth will combine traditional reduced basis methods with deep learning techniques to build an analysis-driven computational emulator for parameterized partial differential equations....
This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) supports research to develop novel mathematical models and efficient algorithms for deep learning on large-scale graph-structured data. The $249,999 award, spanning September 2024 to August 2027, aims to produce innovations in areas like graph convolutional networks, graph matching, and graph clustering. The research will involve graduate...
This National Science Foundation (NSF) Project Grant award, under the Mathematical and Physical Sciences program (CFDA 47.049), supports theoretical and computational research to enhance the accuracy and efficiency of first-principles quantum mechanical simulations for understanding the electronic structure of materials. The $220,991 award will fund the development of innovative machine learning-based approximations to improve the Density Functional Theory (DFT) methodology, which is widely used...
This $300,000 Project Grant awarded by the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the Massachusetts Institute of Technology (MIT) will support the development of advanced machine learning (ML) algorithms and data compression techniques to address the 200 TB/s data rate challenge for the LHCb Upgrade II experiment at CERN's Large Hadron Collider. Specifically, the project will extend the functionality of the HLS4ML software...
This $250,000 five-year 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 generative artificial intelligence (AI) frameworks to accelerate scientific discovery and modeling in key domains like climate and energy. The project aims to create AI systems that can efficiently learn from and analyze large-scale scientific data to identify patterns, simulate natural phenomena,...
The National Science Foundation (NSF) awarded a $116,552 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, Riverside (UC Riverside) to develop open-source cyberinfrastructure for publishing and reusing minimally compressed measurement data from large physics experiments. The project aims to build tools that enable measurements directly on uncompressed or minimally compressed data, addressing limitations in current...