Project Grant 2209654
- This $600,000 federal Project Grant award was issued by the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CFDA 47.070) program. The purpose of the grant is to lay the foundations for building tools that will enable data scientists to more effectively manage and process large datasets for machine learning applications. The key focus is extending relational database technology with the ability to...
- 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 awarded a $599,995 project grant to the University of Massachusetts under the Computer and Information Science and Engineering program (CFDA 47.070) for the period of September 1, 2022 through August 31, 2025. The grant funds research to develop scalable in-database prescriptive analytics capabilities for dynamic environments. Specifically, the university researchers will extend prior work on constrained optimization problems to handle data uncertainty and...
- This $1,016,318 Project Grant was awarded by the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports research and development conducted by Columbia University to design and build two open-source systems, named Marque and Zork, that enable more efficient in-database machine learning. Marque will be a database management system that supports embedding machine...
- This $275,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop and test new scheduling algorithms for improving the performance of modern database systems. The goal is to create models and policies that can efficiently allocate limited hardware resources, such as compute and memory, to handle a stream of parallelizable database queries with varying levels of parallelizability and service...
- This $236,099 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program is supporting research to develop robust optimization and machine learning algorithms capable of handling dynamic and uncertain data environments. The research aims to advance optimization techniques for fundamental supervised learning tasks, yielding computationally and data-efficient algorithms with provable error guarantees. This work will...
- This National Science Foundation Project Grant of $600,000 supports research to improve the performance of database management systems that use log structured merge tree storage technology. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the award will support development of novel data organization and flow patterns within log structured merge tree storage to boost read and write capabilities. Key activities include designing algorithms to store...
- 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) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award of $600,000 to the Massachusetts Institute of Technology (MIT) supports research into developing better algorithms for machine learning problems that involve sequential data with rich dependency structures. The project will explore learning methods for linear dynamical systems, graphical models, and hidden Markov models, with the goal of proving rigorous theoretical...
- This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) is focused on enhancing machine learning with graph-structured data. The research aims to address the challenge of data distribution shifts in AI models when applied to real-world scenarios, particularly in fields like particle physics and biochemistry. The key activities under this 3-year award include: Developing methods to estimate and...
This $258,780 project grant from the National Science Foundation's Computer and Information Science and Engineering program will fund the development of relational algorithms at the University of Pittsburgh from October 2022 through September 2025. Relational databases are ubiquitous for storing business data, but standard machine learning algorithms cannot directly analyze data across multiple database tables. This project aims to create efficient algorithms that can operate directly on relational data without expensive join operations. Goals include developing foundational algorithms for basic geometric problems underlying machine learning, algorithms for standard problems like regression and classification, and techniques for algorithm design and analysis on relational data. The researchers will determine existing tools applicable to relational algorithms and may need to invent new techniques. This award supports the NSF's mission to advance computing and information sciences through investigator-initiated research.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $8.0k | 3/8/23 | ||
| Not listed | $250.8k | 7/14/22 |