This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $349,009 to Texas A&M University-Central Texas to develop a database architecture that integrates privacy policies, data management, and artificial intelligence/machine learning (AI/ML) workflows. The key objectives of this 3-year project are to: (1) create a database system that coordinates data privacy policies, AI/ML...
The National Science Foundation (NSF) awarded a $387,044 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Arizona State University to improve the fundamental limits of privacy-enhancing technologies (PETs). The research aims to develop new PET methods that optimize the balance between preserving individual privacy and enabling comprehensive data analysis for societal benefit in domains such as healthcare, education, and resource allocation. Key...
This $547,584 National Science Foundation project grant supports research at Arizona State University to redesign analytics databases for machine learning model serving. The goal is to develop methods bridging machine learning inference and relational algebra processing through a unified intermediate representation. This will allow native deep neural network model inferences directly from databases, eliminating cross-system latency in applications like supply chain prediction, fraud detection,...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) project grant, awarded to North Carolina State University, is focused on enhancing privacy in federated learning, which is an AI approach enabling knowledge sharing without compromising data privacy. The $220,258 grant, awarded on October 1, 2024, aims to address vulnerabilities in federated learning schemes that may leak sensitive information through improper privacy...
This $167,158 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a customizable, privacy-preserving database analytics system compatible with existing SQL databases. The key products to be delivered under this 4-year award include: Automated tools for analyzing a database schema and interactively developing a flexible privacy model to determine which data elements require differential...
The National Science Foundation awarded a $721,000 project grant to the University of Arizona under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to develop a new software platform called DEEPSECURE. DEEPSECURE will integrate essential functions and building blocks to support privacy-preserving and secure machine learning research. It will include a scalable and customizable modular framework with seamlessly integrated libraries, function blocks, and...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) provides $392,619 to Arizona State University (ASU) from October 1, 2024 to May 31, 2027. The award aims to foster a systematic breakthrough in developing energy-efficient, dynamic, and robust AI-in-memory computing systems. The key products and services to be delivered include: Designing, implementing, and validating a new hybrid in-memory computing system that...
This federal Project Grant award from the Department of Energy's Office of Science Financial Assistance Program (CFDA 81.049) provides $525,000.00 to Arizona State University (ASU) to conduct research on privacy-preserving federated learning for scientific applications. The project will run from Sep 1, 2024 to Aug 31, 2025. ASU, a Hispanic-serving research university, will leverage its expertise in areas like artificial intelligence, engineering, and computational technologies to develop...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $241,682 to the Regents of the University of Michigan to develop a consolidated privacy protection framework for machine learning systems. The 2-year project, running from October 2024 to November 2026, will comprehensively consider the trade-offs between computational privacy and critical machine learning properties such as utility, fairness, and...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award, with a total funding of $174,770, supports the development of an adaptive, federated, continuous learning system that uses a novel federated, semi-supervised learning framework. This framework aims to retrain deep neural network models on distributed, unlabeled, heterogeneous data from edge devices, while leveraging explainable AI techniques to expedite local training. The...