Project Grant 2547020
- The National Science Foundation (NSF) awarded a $221,317 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the Board of Regents of the University of Nebraska. The grant, titled "CAREER: Advancing Experimental Frameworks and Empirically-Based Guidelines for Designing and Assessing Statistical Data Graphics", aims to develop evidence-based guidelines and experimental methods for designing effective data visualizations such as charts and...
- This federal Project Grant award, valued at $399,999.00 and awarded on July 15, 2025, was provided by the Division of Information and Intelligent Systems (IIS), a civilian agency within the National Science Foundation (NSF). The grant supports a collaborative research project titled "Perception-Augmented Databases for Efficient and Robust Visual Analytics" under the NSF's Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The research aims to develop...
- This Project Grant award, valued at $540,000.00, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports a collaborative research project between researchers at Northeastern University to develop methods for designing "responsive uncertainty visualizations" that can effectively communicate the uncertainty in data to users with varying levels of expertise and decision-making needs. The...
- This $343,324 National Science Foundation project grant supports research at Northeastern University to develop empirically validated perceptual tasks for data visualization. The goal is to refine and evaluate a model of the lower-level perceptual processes underlying higher-level data analysis and visualization tasks. Researchers will conduct qualitative studies to document how people decompose tasks and then empirically evaluate proposed perceptual subtasks like filtering images, judging...
- The National Science Foundation awarded a $550,000 project grant to Arizona State University under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) for the period of October 1, 2022 through September 30, 2025. The grant will support the development of novel techniques and open source software tools to enable the creation of privacy-preserving data visualizations. A visualization grammar will be established to specify visualization designs that consider...
- This Project Grant award, with a total funding amount of $599,997, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program. The award, effective October 1, 2024 through September 30, 2027, aims to develop advanced techniques and tools to improve the management and resolution of ambiguity within collaborative visual analytics. The key objectives are to: 1) Identify sources of ambiguity in collaborative...
- This federal Project Grant award, titled "CAREER: BRINGING STRUCTURE TO THE UNSTRUCTURED: ROBUST CAUSAL AND STATISTICAL MODELING OF HIGH-DIMENSIONAL UNSTRUCTURED DATA", is provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The $329,020 grant, awarded to the University of Michigan on June 15, 2025, focuses on developing new analytical tools to extract meaningful insights from complex, high-dimensional...
- The Project Grant award titled "CAREER: TOWARDS RELIABLE MACHINE LEARNING IN FEEDBACK SYSTEMS" is funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The award, totaling $101,476, will support a research program at Cornell University focused on developing innovative algorithms and theoretical frameworks to enable reliable decision-making in complex systems that utilize machine learning...
- This Project Grant award, titled "CAREER: FOUNDATIONS OF INTERACTIVE MACHINE LEARNING WITH RICH FEEDBACK", is funded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The $477,128 award to the University of Arizona will support research to design interactive machine learning algorithms with multiple feedback modalities, addressing challenges in data efficiency, safety, and reusability. The project aims to establish...
- This federal Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports collaborative research at Columbia University focused on developing incentive-driven algorithms and machine learning tools to improve the quality and representativeness of data used in high-stakes decision-making. The $575,000 award, with a project period from August 1, 2025 to July 31, 2028, aims to design data...
This federal Project Grant award, titled "CAREER: Towards Trustworthy Analytics," was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The $222,663 award, effective August 1, 2025, supports research to develop techniques for assessing the reliability of data visualizations and insights generated through interactive data analysis. The key objectives of this project are: (1) capturing analysts' beliefs, expectations, and intentions during visual data analysis; (2) developing algorithms to forecast the reliability of emerging visualizations; and (3) evaluating strategies for communicating the risk of false patterns. The resulting techniques will be validated and incorporated into tools for detecting RNA modifications from noisy sequencing data, in collaboration with bioinformatics researchers. This research aims to aid analysts in assessing the reliability of insights, while guarding against misleading visualizations, in order to increase confidence in data-driven decision-making and reduce the incidence of false discoveries. The project also includes the development of interactive educational materials for training students in reliable data-driven inference, to be disseminated to data science instructors.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $222.7k | 8/26/25 |