Project Grant 2436156
- The National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program awarded a $420,000 Project Grant to New York University (NYU) for the project "Collaborative Research: SATC: CORE: SMALL: Evaluating the Security Landscape of Machine Learning Enabled Electronic Design Automation". The 3-year project, starting on October 1, 2025, will comprehensively examine the impact of input and training data perturbations and attacks on the quality, performance,...
- This Project Grant award of $246,516 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to enhance the security and robustness of machine learning (ML) systems in multi-tenant cloud FPGA (field programmable gate array) environments. The project aims to: (1) understand the vulnerabilities of ML cloud-FPGA systems and explore defensive approaches; (2) advance the security of ML cloud systems against hardware-based model...
- This Project Grant award, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to improve the security and resilience of machine learning (ML) software. The $262,828 award, with a performance period from March 2025 to April 2026, will develop methods for detecting and correcting non-functional vulnerabilities in ML libraries, such as denial-of-service attacks and side-channel attacks. This research will address...
- This $250,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program will fund research at Northeastern University to improve the security of machine learning models in multi-tenant cloud field programmable gate array (FPGA) environments. The three-year award aims to advance understanding of vulnerabilities in cloud-FPGAs shared by multiple tenants, where a malicious actor could potentially manipulate another tenant's machine learning...
- The National Science Foundation (NSF) awarded a $431,250 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Carnegie Mellon University (CMU) to develop principled defenses against vulnerabilities in modern machine learning (ML) systems. The goal is to make robustness a core design property rather than an afterthought, bridging rigorous analysis with practical experimentation. The project will proceed along three technical thrusts: 1) robust...
- This $127,000 Project Grant award from the National Science Foundation's STEM Education program (CFDA 47.076) will support the development of a comprehensive educational program at Rutgers, The State University to address security vulnerabilities in artificial intelligence (AI) and machine learning (ML) systems. The key products and services to be delivered under this 3-year award include: Creating a practice-in-the-loop learning experience for students to understand the security of ML models in...
- Arizona State University received a $300,000 project grant award from the National Science Foundation on May 1, 2021 to develop artificial intelligence tools and resources for cybersecurity education. The funding supports the EAGER: SATC-EDU project to create a machine learning-enabled security knowledge graph for integrating AI into cybersecurity curriculum from April 30, 2023. The grant falls under the National Science Foundation's STEM Education program (CFDA 47.076), which aims to strengthen...
- 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 federal Project Grant award, valued at $155,000.00, 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 led by the Regents of the University of Michigan to develop practical generative AI tools for enhancing the performance of security classifiers used to detect cybersecurity threats. The key focus is on addressing data challenges that often limit the...
- This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports an initiative at Brown University to integrate machine learning (ML) techniques into electronic design automation (EDA) optimization algorithms. The goal is to improve the performance and speed of EDA tools, which are critical for designing and manufacturing computer chips. The project aims to develop numerical embeddings of chip...
This Project Grant award, valued at $180,000.00 and provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research to evaluate the security landscape of machine learning (ML) and artificial intelligence (AI) technologies in electronic design automation (EDA) tools for the chip design industry. The key research aims include: (1) comprehensively examining the impact of input and training data perturbations and attacks on the quality, performance, and security of AI/ML-based EDA tools; and (2) thoroughly investigating mechanisms to defend against such attacks. This research will enable the trustworthy adoption of AI/ML methods in the chip design industry, leading to enhanced productivity, design quality, and trustworthiness. The project is being conducted by Arizona State University, which has a strong track record of federal research awards across diverse scientific and technological domains.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $180.0k | 8/7/25 |