This Project Grant award from the National Science Foundation (NSF) under CFDA 47.070 - Computer and Information Science and Engineering is for $395,927 over the period of Sep 1, 2024 to Aug 31, 2027. The award aims to develop theoretical and algorithmic foundations for building a safe and robust human-AI ecosystem, where machine learning (ML) and artificial intelligence (AI) techniques are used in applications involving humans, such as recommendation systems, lending, and healthcare. The key...
This NSF Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) Project Grant, awarded to Arizona State University in the amount of $598,123 on July 15, 2024, will develop new algorithms to enable AI systems to autonomously learn hierarchical world models and high-level actions. The goal is to create AI systems, such as hospital robots and disaster-recovery support systems, that can plan reliably and efficiently to accomplish complex user-desired tasks, without requiring...
The National Science Foundation (NSF) Division of Information and Intelligent Systems awarded a $200,000 Project Grant to Temple University to develop transformative machine learning and data analytics technologies for enabling AI-based applications on resource-constrained edge computing devices. The project aims to address gaps between the complexity of data and the limited computing resources on edge devices, as well as the need for robust predictive models across heterogeneous edge...
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 $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,...
The National Science Foundation (NSF) awarded a $400,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Arizona State University, Division (doing business as Orspa), for the project "COLLABORATIVE RESEARCH: SHF: MEDIUM: TINY CHIPLETS FOR BIG AI: A RECONFIGURABLE-ON-PACKAGE SYSTEM". This 4-year project (7/1/2024 - 6/30/2028) aims to pioneer a computing system for massive AI workloads, including new architectural and design automation...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to advance the field of artificial intelligence (AI) by investigating the mathematical foundations and practical applications of deep learning models. The research project, conducted by the University of Wisconsin-Madison, focuses on understanding the properties of neural networks, analyzing the progressively refined data...
This $416,516 National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) project grant award to Arizona State University (ASU) is focused on developing an energy-efficient artificial intelligence (AI) processing-in-memory (PIM) system that leverages emerging spin-orbit torque magnetic random access memory (SOT-MRAM) technology. The project aims to advance the materials, devices, circuits, architectures, and AI algorithms for this SOT-MRAM-based AI-PIM...
This five-year, $199,995 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop a co-designed framework of hardware, software, and algorithms enabling extreme-scale machine learning systems for emerging artificial intelligence of things and internet of senses technologies. Specifically, the Saint Louis University team will pursue five research thrusts: developing hardware and compiler approaches for large-scale split learning...
This National Science Foundation Project Grant award of $303,891 supports research at Arizona State University to advance machine learning for human-in-the-loop cyber-physical systems. Under the Computer and Information Science and Engineering program, the award funds development of mixed-initiative solutions to enable accurate learning of human behaviors in uncontrolled environments through mobile and wearable devices. Key objectives include investigating combinatorial approaches to maximize...