Project Grant 2555093
- The National Science Foundation Division of Information and Intelligent Systems awarded Tulane University $376,008 on June 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to conduct collaborative research on fault localization techniques for deep learning models. The project addresses the gap between traditional software fault localization methods and their applicability to deep neural networks, which operate on fundamentally different computational...
- This Project Grant award of $180,803 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will support research into enabling the reuse, replacement, and independent evolution of deep neural network (DNN) modules. The research aims to address key challenges in the software engineering of DNN-based systems, such as explainability, scalability, and correctness. Specifically, the project will investigate approaches to systematically...
- The National Science Foundation Division of Computer and Network Systems awarded Brown University $400,000 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop methods and tools for verifying the trustworthiness of pre-trained artificial intelligence models before their incorporation into scientific workflows and operational systems. The project addresses three major security vulnerabilities in the machine learning model supply chain....
- The National Science Foundation Division of Computer and Network Systems awarded Purdue University $410,000 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop methods and tools for verifying the trustworthiness of pre-trained artificial intelligence models shared through open online repositories before their incorporation into scientific workflows and operational systems. The project addresses three security vulnerabilities in the...
- The National Science Foundation Division of Computing and Communication Foundations awarded The Administrators Of Tulane Educational Fund (Tulane University) $418,410 on July 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop interpretable artificial intelligence methods that explain AI decision-making in transparent, intuitive ways. The project develops a novel framework for interpretable AI that learns semantically meaningful data patterns...
- The National Science Foundation Division of Computer and Network Systems awarded $450,000 to New Jersey Institute of Technology on May 15, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to improve the security of large language model-assisted coding. The project addresses vulnerabilities in LLM-generated code, which can omit critical security checks or contain exploitable mistakes that evade scrutiny and reach production systems. The research introduces...
- The National Science Foundation Division of Computer and Network Systems awarded The Trustees of The Stevens Institute of Technology $364,538 on July 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop AI-assisted tools for automated vulnerability management in open-source software. The project runs through June 30, 2031, and is performed in Hoboken, New Jersey. Work focuses on three research thrusts: using program analysis and graph neural...
- The National Science Foundation Division of Computing and Communication Foundations awarded Tulane University $449,868 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop and evaluate community-centered civic AI systems that connect residents, community organizations, and local governments around public service information and civic data. The project will conduct participatory design activities with residents, civic organizations,...
- This Project Grant award of $599,978.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at Tulane University to develop innovative countermeasures against a specific type of memory disclosure attack known as JIT-ROP. The key objectives are to establish efficient memory permission control mechanisms using modern CPU features, and to integrate unreadable booby traps into JIT-compiled code to detect JIT-ROP...
- The National Science Foundation Division of Computer and Network Systems awarded $344,911 to Rochester Institute of Technology on April 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to defend machine learning models from adversarial threats through unified interpretability and attribution methods. The research develops a layered defense framework across four technical thrusts: scalable attribution techniques that combine gradient-based influence...
The National Science Foundation Division of Computer and Network Systems awarded Tulane University $599,936 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop compiler-based defenses against reverse engineering of deployed deep neural network binaries. The project develops techniques to protect AI models compiled into machine code on mobile devices, cloud servers, embedded systems, and other hardware by leveraging compiler optimization as a security primitive. Current defenses scramble programs in ways that degrade performance, enlarge executables, or alter behavior—making deployment on resource-constrained devices impractical. The research models how deep neural network decompilers recover model structure and data from compiled executables and designs compiler-based obfuscation through loop transformations, tiling, and memory layout changes to disrupt recovery. The project develops quantitative uncertainty measures, including adversarial search-space size, to estimate model recovery likelihood and builds SecTuner, a security-aware auto-tuning framework that treats decompilation resistance as an optimization goal alongside performance, using randomized loop tiling and multi-objective optimization to select compiler parameters. The project runs through September 30, 2029, with work performed in New Orleans, Louisiana. Beyond technical outputs, the project creates open-source tools, educational materials, and training opportunities to strengthen cybersecurity and AI workforce development in Louisiana.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $599.9k | 8/10/26 |