This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) will support research to establish a framework for designing and implementing safe learning-enabled systems. The $399,965 award to Cornell University, with a period of performance from October 1, 2024 to September 30, 2027, aims to develop methods for ensuring the safety of learning-enabled systems, even in complex operating environments,...
This $800,000 Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research to establish a computational foundation for safe Graph Neural Networks (GNNs). The 3-year project investigates the end-to-end safety of GNNs, which are a family of deep learning models for interrelated, graph-structured data. The research aims to develop new theories, algorithms, and evaluation methods to enable safer...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program will develop a closed-loop machine learning (ML) pipeline to improve the generalizability of ML models for network operations. The $123,859 project aims to (1) create a programmable data collection platform to acquire training data across diverse network environments, and (2) use explainable ML techniques to detect and address underspecification issues in network...
The National Science Foundation (NSF) has awarded a $266,589 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of California, Berkeley to develop safe learning-enabled systems that can navigate uncertain environments. The project aims to create a two-phase design process that combines an offline robust synthesis phase with an online safety monitoring and adaptation phase, enabling provable end-to-end safety guarantees for learning-enabled...
The National Science Foundation (NSF) awarded a $800,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Illinois Urbana-Champaign. The 3-year grant, effective September 1, 2024, focuses on enhancing the safety of large language models (LLMs) used in high-stakes applications. The project aims to develop quantifiable safety measures and algorithms to detect and mitigate unsafe behaviors in LLMs, such as providing false or...
This National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems award, CFDA 47.041 Engineering, will provide $193,000 from September 1, 2024 to August 31, 2027 to New York University (NYU) to develop new theories and methodologies for safe reinforcement learning in domains such as robotics, autonomous driving, and power systems. The key products and services to be delivered under this Project Grant include: 1) Formulating safety measures as general objectives...
The National Science Foundation (NSF) awarded a $793,065 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of Wisconsin System for the project "SLES: Foundations of Safety-Aware Learning in the Wild." The project aims to develop novel machine learning algorithms and theoretical guarantees that can reliably detect and handle out-of-distribution data encountered by AI models deployed in dynamic, unpredictable environments. This...
This Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) provides $128,737.00 to The Trustees of Princeton University to develop NETFORTIFY, an open-source framework for testing and strengthening the reliability of machine learning (ML)-powered networking functions. The key objectives are to: (1) define formal semantics for "contextual robustness" to ensure ML-based networking approaches meet required properties...
This Project Grant award of $285,694, provided by the National Science Foundation (NSF) under the Integrative Activities program (CFDA 47.083), aims to develop a safe online hierarchical learning framework for Open Radio Access Network (O-RAN) mobile networks. The key goals are to enable online resource allocation at near-real-time RICs using safe deep reinforcement learning, and online service orchestration at non-real-time RICs through robust Bayesian learning. Additionally, the project will...
The National Science Foundation Division of Information and Intelligent Systems awarded a $154,231 Project Grant to the University of Texas at Austin under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The two-year award will support research to develop a generative deep learning framework for approximating human decision-making processes on social networks when structural network data is unavailable. Specifically, the university researchers will...
This Project Grant award of $347,995, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, supports the design, development, and evaluation of a novel "Safety-Centric Network Learning Framework" (SNL) at the University of Connecticut.
The project aims to advance network learning research and offer solutions to address challenges across diverse domains like public health, cybersecurity, and social media. The key focus is on prioritizing safety as a fundamental aspect, by developing reliable network data and learning models, generating stable and consistent outputs, and providing easily understandable usage explanations. The comprehensive research program involves designing novel network learning algorithms, creating efficient representation learning models, devising data-to-model optimization theories, and developing generative learning methods to improve the reliability, stability, and explainability of network learning techniques. The project outcomes are expected to substantially impact network learning research and benefit society by tackling various challenges through this convergent research approach.