Project Grant 2238821
- This $271,343 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research into developing robust machine learning and inference methods that can withstand data corruption and distribution shifts. The project aims to explore new techniques for structured learning, supervised learning, and reinforcement learning that are resilient to these challenges, with potential applications in healthcare,...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $250,000 to the Massachusetts Institute of Technology (MIT) to conduct research on developing corruption-robust online optimization and learning algorithms. The goal is to create resilient decision-making algorithms that can operate effectively in the face of security threats and adversarial attacks, supporting the resilience of critical...
- This $299,886 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop new big data algorithms that are robust to adversarial input. The award supports research to address emerging vulnerabilities in areas such as black-box streaming algorithms, white-box streaming algorithms, and adaptive data analysis with bounded space. This work will focus on improving the reliability, security, and trustworthiness of...
- This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research to develop new statistical and computational methods to enhance the reliability of data analysis in modern, large-scale datasets, particularly in the era of AI. The key areas of focus include: (1) analyzing the robustness of manifold and deep learning algorithms for high-dimensional, noisy, and nonlinear data; (2) developing statistical theory...
- This National Science Foundation (NSF) Project Grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), provides $300,000 in funding to Princeton University to develop novel computational methods for recovering signals from highly corrupted and distorted data. The key products to be delivered include: New algorithms and mathematical foundations to enable effective extraction and analysis of information from data collected by advanced imaging technologies, such as...
- This $299,998 federal Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program will support collaborative research at Carnegie Mellon University to develop new big data algorithms that are robust to adversarial input. The key focus areas include: 1) adversarial robustness in black-box and white-box streaming settings, and 2) adaptive data analysis with bounded space. The research team will also explore emerging attack...
- This $431,250 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research by Carnegie Mellon University to develop robust machine learning (ML) systems that can operate reliably in complex, real-world environments. The 5-year project (8/1/2025 - 7/31/2030) aims to bridge theoretical analysis and practical experimentation to address the brittleness of current ML models, which can fail unexpectedly under...
- This three-year $300,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to advance understanding of robustness in machine learning models. Specifically, the University of Maryland, College Park will research conditions under which adversarial attacks on deep networks can be detected and original data reconstructed. It will also study fundamental limits of robustness guarantees against poisoning attacks, especially with a...
- This National Science Foundation project grant of $199,968 supports collaborative research at Tufts University from September 2022 through August 2025 under the Mathematical and Physical Sciences program (CFDA 47.049). The research aims to advance theory and computation for structured sensing problems involving low-rank matrix recovery from deterministically structured measurements. Specifically, the project will study scalable non-convex optimization methods for generalized matrix completion...
- This two-year, $500,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop foundational theory and optimal methods for robust statistical inference in the presence of adversarial data falsification. The grantee, Oregon State University, will investigate the fundamental limits of performing hypothesis testing and estimation when an adversary can manipulate input data. They will also develop robust inference methods...
CAREER: ROBUST ALGORITHMS FOR CORRUPTED DATA -RECENT YEARS HAVE SEEN AN INCREASE IN DATA-DRIVEN APPROACHES TO SCIENTIFIC DISCOVERY AND TECHNOLOGICAL INNOVATION. DATASETS OF ENORMOUS SIZE AND VARIETY ARE ROUTINELY USED TO EXPLORE NEW PHENOMENA AND TRAIN ALGORITHMS. AS DATA VOLUME AND COMPLEXITY GROW, HOWEVER, DATA QUALITY OFTEN DECREASES. DATA CAN BE PLAGUED BY NOISE, OUTLIERS, MISSING VALUES, AND OTHER FORMS OF INFORMATION LOSS. THIS PROJECT WILL DESIGN ROBUST, EFFICIENT ALGORITHMS FOR ANALYZING SUCH CORRUPTED DATA. THESE METHODS WILL BE DEPLOYED ON PROBLEMS IN IMAGING, BIOLOGY, AND OTHER HIGH-IMPACT DOMAINS. THEY WILL BE IMPLEMENTED IN GENERAL-PURPOSE CODES, TO BE RELEASED FOR PUBLIC USE. IN ADDITION TO ITS RESEARCH PRODUCTS, THIS PROJECT WILL ALSO CREATE EDUCATIONAL RESOURCES TO TRAIN STUDENTS AT THE ADVANCED UNDERGRADUATE AND BEGINNING GRADUATE LEVEL IN THE THEORY AND PRACTICE OF DATA ANALYSIS. TWO COMPLEMENTARY METHODOLOGIES WILL BE CONSIDERED FOR PROCESSING CORRUPTED DATA. FOR THE FIRST CLASS OF METHODS, THE DATA WILL BE ASSUMED TO HAVE AN UNDERLYING LOW-RANK STRUCTURE THAT IS PERTURBED BY BOTH ADDITIVE NOISE AND LINEAR FILTERS. NEW RESULTS FROM RANDOM MATRIX THEORY WILL BE DEVELOPED FOR SIGNAL RECOVERY PROBLEMS IN THIS SETTING, INCLUDING THE ESTIMATION OF COVARIANCE AND DISTANCE MATRICES. THE SECOND CLASS OF METHODS WILL EXPLOIT GEOMETRIC STRUCTURES IN DATA. NOVEL METHODS FROM COMPUTATIONAL HARMONIC ANALYSIS WILL BE DEVISED TO BOTH LEARN THE GEOMETRY OF A DATASET BY UNCOVERING RELATIONAL INFORMATION BETWEEN DATA POINTS, AND TO USE THIS GEOMETRIC INFORMATION FOR CLUSTERING, REGRESSION, AND OTHER TASKS. BOTH APPROACHES HAVE THE POTENTIAL TO YIELD METHODS THAT ARE NOT ONLY STATISTICALLY ROBUST, BUT ALSO COMPUTATIONALLY EFFICIENT. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $94.9k | 8/28/25 | ||
| Not listed | $207.2k | 1/20/23 |