Project Grant 2312546
- This three-year National Science Foundation project grant of $300,000 will fund research to advance trustworthy machine learning through bi-level optimization. The grantee, the University of California, Santa Barbara, will develop new algorithms and computational methods to achieve robust and fair deep learning. Specifically, the project will create a bi-level optimization framework for robust learning, defenses against adversarial examples and distribution shifts, and a full-stack robustness...
- 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 provides $395,842 to Princeton University to develop an open-source framework called "NetFortify". The goal is to enhance the reliability of machine learning (ML) systems for critical networking functions such as managing network resources and troubleshooting problems. The project focuses on three key thrusts: (1) defining formal semantics for...
- 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...
- This three-year, $1.1 million project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop highly scalable and sample-efficient spectral methods for learning graph topologies from high-dimensional data samples. The grantee, the Trustees of the Stevens Institute of Technology, will investigate spectral graph densification frameworks to efficiently estimate attractive Gaussian Markov random fields. A unique feature of the learned...
- This $300,000 National Science Foundation project grant supports research into robust machine learning under sparse adversarial attacks through 2025. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the University of California, Santa Barbara will develop theoretical frameworks and defense methods to make machine learning models resilient against perturbations affecting few data points. Specifically, the researchers aim to establish fundamental limits of...
- 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 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...
- The National Science Foundation Division of Computer and Network Systems awarded a $175,000 Project Grant to the Illinois Institute of Technology under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to support research towards understanding the robustness of graph neural networks against graph perturbations. The two-year award beginning June 1, 2023 will fund the development of both restricted and stringent black-box graph perturbation attacks on graph...
- This three-year, $300,000 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), will support the development of new algorithms and computational methods for trustworthy machine learning via bi-level optimization. The grantee, Michigan State University, will advance the theoretical understanding and practical implementation of robust and fair deep learning....
COLLABORATIVE RESEARCH: CIF: MEDIUM: ROBUST LEARNING OVER GRAPHS -CONNECTED SENSORS, AUTONOMOUS SYSTEMS AND THE INTERNET ALL PRODUCE VAST AMOUNTS OF STRUCTURED DATA THAT MUST BE ANALYZED. GRAPHS CAN MODEL COMPLEX NETWORKED SYSTEMS, AND AS SUCH GRAPH-BASED MACHINE LEARNING SYSTEMS AIMING AT INFERENCES FROM STRUCTURED DATA, HAVE GAINED SIGNIFICANT TRACTION. DATA-DRIVEN SYSTEMS, ON THE OTHER HAND, MUST DEAL WITH NOISY, UNCERTAIN, AND OUTLYING MEASUREMENTS, AS WELL AS DATA PROVIDED BY UNTRUSTWORTHY OR EVEN MALICIOUS SOURCES. IN ADDITION, GRAPH-AWARE SYSTEMS MUST COPE WITH ERRORS IN THE ESTIMATED GRAPH STRUCTURE. ASPIRING TO ADDRESS THESE CHALLENGES, THIS PROJECT INTRODUCES A COMPREHENSIVE FRAMEWORK FOR ROBUST LEARNING OVER GRAPHS THAT CAN IDENTIFY AND MITIGATE OUTLIERS, ADVERSARIAL ATTACKS, AND ERRORS IN THE GRAPH STRUCTURE. THE DEVELOPMENT OF SCALABLE, ROBUST, AND TRUSTWORTHY LEARNING CAN ENABLE ACTIVE AND TIMELY INFERENCE AND DECISION-MAKING IN DOMAINS SUCH AS SOCIAL ANALYTICS, CROWDSOURCING, HEALTH INFORMATICS, AND INTERNET-OF-THINGS SECURITY. THE OVERARCHING GOAL OF THIS PROJECT IS TO FORTIFY LEARNING OVER GRAPHS AGAINST NOISE, ANOMALIES, ERRORS IN THE DATA, AND ADVERSARIES. THIS PROJECT CONSISTS OF THREE INTERTWINED THRUSTS: (T1) IDENTIFYING ANOMALIES IN NODAL PROCESSES OVER NETWORK GRAPHS; (T2) GRAPH-AWARE DEEP LEARNING UNDER PERTURBED GRAPH STRUCTURE, AND; (T3) INFORMATION AND DECISION FUSION TO QUANTIFY THE RELIABILITY OF INFORMATION SOURCES. T1 LEVERAGES RANDOM SAMPLING AND CONSENSUS, AS WELL AS GRAPH SIGNAL PROCESSING TOOLS TO PINPOINT OUTLYING NODES. DEALING WITH NOISY OR PERTURBED GRAPH TOPOLOGIES, T2 ENDOWS GRAPH CONVOLUTIONAL NETWORKS WITH DITHERING-INSPIRED MODULES THAT ARE ROBUST TO CHANGES IN GRAPH LINKS. FINALLY, T3 WILL DESIGN AND ANALYZE GRAPH-COGNIZANT UNSUPERVISED ENSEMBLE LEARNING AND CROWDSOURCING ALGORITHMS TO ASSESS THE RELIABILITY AND USEFULNESS OF VARIOUS INFORMATION SOURCES. RESULTS FROM THIS PROJECT WILL BE DISSEMINATED TO THE BROADER RESEARCH COMMUNITY THROUGH PUBLICATIONS, WORKSHOPS, AND CODE SHARING. 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 | $285.2k | 6/2/25 | ||
| Not listed | $557.9k | 5/19/23 |