Project Grant N001782510036

This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program, totaling $247,903, supports research from November 2021 through September 2024 to develop techniques for improving the adaptability of machine learning-based security defenses. The goal is to enable these defenses to better handle dynamic changes in data caused by evolving attacks and changes in benign system usage, with reduced need for costly manual data labeling. The awardee,...

This National Science Foundation (NSF) Division of Civil, Mechanical, and Manufacturing Innovation Project Grant award of $531,915 to the Massachusetts Institute of Technology (MIT) aims to develop fundamental methodologies for reducing workforce inequality due to implicit bias in automated applicant screening technologies. The 5-year research project will apply counterfactual and causal analysis to construct variability sets for candidate evaluation data, yielding optimization problems to...

This $348,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at the Massachusetts Institute of Technology (MIT) to develop robust and ethical machine learning models for healthcare applications. The project aims to improve model robustness across diverse patient populations and care settings, with a focus on three key areas: ensuring representations and models can withstand...

This federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $236,099 in funding to the University of Wisconsin - Madison to conduct research on optimization and learning techniques for machine learning models with changing data distributions. The research aims to develop robust algorithms capable of handling dynamic and uncertain data scenarios, such as when training and testing data differ...

This $123,859 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will enable the University of California, Santa Barbara (UCSB) to develop a closed-loop machine learning (ML) pipeline that iteratively refines training data collection to improve the generalizability of ML models for network operations. The key objectives are to: (1) design a programmable data-collection platform to acquire flexible and...

This $175,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program will fund the development of robust machine learning methods to address data disparities. The University of Michigan will receive funding from March 2022 through February 2024 to create predictive and causal machine learning tools for medical decision making that are reliable despite inaccuracies from underrepresented patient subgroups. Specifically, the university will...

The National Science Foundation awarded $235,438 under the Computer and Information Science and Engineering federal grant program to develop interpretable and fair machine learning frameworks, algorithms, and methodologies. The two-year project grant aims to address concerns that machine learning algorithms can inadvertently amplify human biases by funding research to design scalable, data-driven methods with provable fairness guarantees. Specifically, the grant will support the development of...

This $600,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at the University of Maryland, College Park to develop transparent and interpretable bias mitigation approaches for place-based mobility-centric prediction models. The three-year award beginning September 1, 2022 will support technical contributions in three thrusts. The first will provide a novel prediction model to forecast reported place-based...

This Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $300,000 to Arizona State University to develop a machine learning framework for training models across hospitals on electronic health records without sharing patient data. The framework aims to address fairness and mitigate biases by training representation learning algorithms jointly across multiple...

This three-year, $500,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at the University of California, Davis to develop trustworthy machine learning systems through adversarial robust reinforcement learning. Specifically, the award supports investigating potential vulnerabilities in reinforcement learning models and algorithms, developing robust RL approaches that mitigate impacts from adversarial attacks, and...