The National Science Foundation (NSF) awarded Arizona State University a 5-year, $196,726 CAREER Program grant under the Computer and Information Science and Engineering (CISE) grant program (CFDA 47.070) to support a research and education program focused on accelerating scientific discovery through physics-informed deep learning models. The project aims to develop physically-consistent dynamics models, deep learning-based symbolic regression algorithms, and multimodal deep learning...
Arizona State University was awarded a three-year $500,000 project grant from the National Science Foundation to develop neuro-symbolic learning and control tools for cyber-physical systems. The grant is funded through the NSF's Computer and Information Science and Engineering program, which supports research and education in all areas of computing, communications, and information science and engineering.
Under this award, Arizona State University researchers will integrate machine learning,...
The National Science Foundation (NSF) awarded a $213,679 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the University of California, Berkeley. This grant, titled "CAREER: HIGH-ASSURANCE DESIGN OF LEARNING-ENABLED CYBER-PHYSICAL SYSTEMS WITH DEEP CONTRACTS", aims to develop a novel methodology for the design and verification of learning-enabled cyber-physical systems (CPS). The project will pursue a compositional...
This $150,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support the development of foundational principles, algorithms, and tools for causal decision-making systems. Researchers at Columbia University will enrich traditional artificial intelligence formalism with causal modeling to enable more efficient, robust, and explainable decision-making by autonomous systems. Key deliverables include integrating...
The National Science Foundation (NSF) Office of Advanced Cyberinfrastructure awarded a $599,905 Project Grant to Arizona State University, Division Orspa, to develop CAUSALBENCH, a cyberinfrastructure for causal-learning benchmarking. The grant was awarded on July 1, 2023 under the NSF Computer and Information Science and Engineering (CISE) program (CFDA 47.070), which funds research and education in all areas of computing, communications, and information science.
The key products and...
This $750,000 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program to Arizona State University focuses on developing foundational technologies for safe Reinforcement Learning (RL)-enabled systems. The 4-year project aims to establish theories, algorithms, and experiments for distributional RL to enable policy safety, exploration safety, and environmental safety in RL-powered applications like 6G networking,...
The National Science Foundation (NSF) has awarded a $850,000 Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, Los Angeles (UCLA) to develop an efficient human-in-the-loop learning framework for human-centric cyber-physical systems (CPS). The 3-year project aims to create a novel approach to integrate human oversight and intervention into the training of CPS agents, such as assistive driving and exoskeleton systems,...
This $271,343 Project Grant award was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to Arizona State University to conduct research on developing robust machine learning and inference methods. The key objectives are to explore new approaches to make learning and inference techniques more resilient to distributional uncertainty and data corruption, particularly for applications in areas of national importance...
This National Science Foundation (NSF) grant under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $1,200,000 to The Washington University to develop new theoretical and algorithmic capabilities for ensuring the robust and safe deployment of AI-enabled cyber-physical systems (CPS). Specifically, the project aims to design certification, verification, and robust training algorithms for multi-agent reinforcement learning (MARL) control policies. This...
Arizona State University received a $569,413 National Science Foundation Project Grant under the Engineering program (CFDA 47.041) for the period of June 1, 2021 through May 31, 2026. The grant funds the "CAREER: WHEN REALITY FAILS EXPECTATIONS: CONTAINING REFLECTIVE DOMAIN MODELS FOR HUMAN-AWARE PLANNING AND LEARNING OF ROBOTIC TEAMMATES" project. This project aims to develop domain models and learning techniques to help robots better understand human expectations and adapt their...