The University of Washington was awarded a three-year $500,000 Project Grant from the National Science Foundation Division of Information and Intelligent Systems under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will support research into the theoretical foundations of reinforcement learning, from modeling learning as a blank slate to using function approximation. The University will conduct investigator-initiated research advancing the...
The University of Washington was awarded a $549,843 National Science Foundation Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) for the project "CAREER: KNOWLEDGE-RICH NEURAL TEXT COMPREHENSION AND REASONING" from September 1, 2021 to August 31, 2026. This Project Grant will support the development of new natural language processing techniques focused on enabling computers to comprehend and reason about text at a deeper level through the...
The University of Washington was awarded a three-year, $599,995 Project Grant from the National Science Foundation Division of Computer and Network Systems under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research titled "COLLABORATIVE RESEARCH: CNS CORE: MEDIUM: RECONFIGURABLE KERNEL DATAPATHS WITH ADAPTIVE OPTIMIZATIONS" to be conducted between October 1, 2021 and September 30, 2024. Specifically, the University will investigate...
The University of Washington was awarded a two-year, $165,949 Project Grant from the National Science Foundation under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will support collaborative research on natural language (variety) processing from October 1, 2021 to September 30, 2023. The Computer and Information Science and Engineering program aims to advance computing and communication sciences and technologies through...
This Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $274,560 to the University of Washington for research titled "Machine Learning for Bayesian Inverse Problems." The award period is from September 1, 2022 through August 31, 2025. The project aims to develop foundational theory and novel computational techniques for applying machine learning methods to quantify...
The University of Washington was awarded a $316,356 project grant from the National Science Foundation Division of Computer and Network Systems under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will fund research from October 1, 2021 to September 30, 2025 to develop novel algorithms and tools that empower individuals who are blind to safely manage private visual content. The goal is to advance computing and information sciences research...
This $1.2 million project grant from the National Science Foundation's Computer and Information Science and Engineering program will support research and education activities at the University of Washington from October 2022 to September 2026. The project aims to develop a mathematical foundation for deep reinforcement learning by investigating guarantees achievable by neural network policies under different problem structures. Researchers will leverage tools from approximation theory, control...
The National Science Foundation awarded a $499,979 project grant to the George Washington University under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research towards developing high-performance machine learning techniques on graphs from October 1, 2021 to September 30, 2024. The Computer and Information Science and Engineering program aims to advance computing and informatics research and education. This award will further those goals by...
The National Science Foundation awarded a $350,000 Project Grant to the University of Washington under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to develop novel strategies for constructing optimal statistical estimators using machine learning tools. Over a three-year period ending August 2025, the investigators will study representations of the efficient influence function that can be computed numerically to derive new asymptotically efficient estimators. They...
This $120,000 federal Project Grant award from the National Science Foundation's Mathematical and Physical Sciences (CFDA 47.049) program supports research at the University of Washington to develop mathematical frameworks, algorithms, and computational methodologies for scientific machine learning using Gaussian processes. The key focus areas are: (1) using Gaussian processes to solve nonlinear, high-dimensional, and parametric partial differential equations; (2) Gaussian process-based...