The Trustees of Princeton University will use a $300,000 project grant from the National Science Foundation to conduct research exploring the design of Plotkin transform codes via machine learning techniques. Funded through the NSF's Computer and Information Science and Engineering program (CFDA 47.070), which supports investigator-initiated research and education in computing and information sciences, the three-year project will investigate generalizing the family of Plotkin transform codes,...
The National Science Foundation awarded a $266,772 Project Grant to the Regents of the University of Michigan under the Computer and Information Science and Engineering program (CFDA 47.070). The five-year award beginning September 1, 2023 will support research investigating the connections between coding theory, specifically list decoding of error-correcting codes, and pseudorandomness theory. The investigator aims to advance the theoretical understanding of fundamental notions like...
This $400,000 Project Grant was awarded on July 1, 2024 by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) to the Regents of the University of Michigan to conduct collaborative research on the theoretical foundations of compositional learning in large language models based on transformer architectures. The research aims to investigate three key areas: model expressivity, statistical learning theory, and optimization, with...
This $400,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop a principled and unified mathematical framework for deep learning on low-dimensional data structures. The project aims to bridge the gap between theory and practice of deep learning by designing "white-box" deep neural networks using unrolled optimization schemes to maximize information gain in...
This $196,176 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will support collaborative research to apply deep learning techniques to the design of new encoding and decoding methods for physical layer communication. The researchers at Princeton University aim to use deep learning tools to generate a new family of codes naturally built for finite block lengths, addressing a longstanding challenge in information theory. In parallel,...
This National Science Foundation Project Grant award of $599,970 provides funding from May 2023 through April 2026 to support research activities at the University of Illinois under the Computer and Information Science and Engineering program (CFDA 47.070). The award will fund research to develop new algorithmic tools and techniques for interpreting error-correcting codes that have been designed using deep learning methods. The research goals include gaining a better understanding of how and why...
The National Science Foundation (NSF) awarded a $242,668 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the Regents of the University of California at Riverside. The grant, which runs from January 1, 2025 to December 31, 2029, will support research exploring the interplay between pseudorandomness and coding theory. The project aims to improve recent code constructions based on expander graphs, which can approach optimal tradeoffs between code...
The National Science Foundation (NSF) awarded a $600,000 project grant under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the Regents of the University of Michigan to support research on reinforcement learning and transformer-inspired approaches for smart photonics inverse design. The project aims to enable non-experts to use artificial intelligence models to design sophisticated photonic structures for optical applications, while also advancing...
The University of Michigan was awarded a $330,000 Project Grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) to support research titled "ATD: ALGORITHMIC THREAT DETECTION AND MITIGATION WITH ROBUST MACHINE LEARNING" from September 1, 2021 through August 31, 2024. The grant funding will allow the University of Michigan to conduct research focused on developing robust machine learning...
This National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) Project Grant award to the Regents of the University of Michigan, through its Office of Research and Sponsored Projects, provides $250,000 in funding from September 1, 2024 to August 31, 2027. The award supports research to develop new theoretical tools and algorithm design techniques for the emerging area of operator learning - the use of statistical machine learning to find fast approximations for solving...