Category II: BRIDGE: A National Center to Democratize Multi-disciplinary Data Science and AI for Research Labs and Communities

This project will establish the BRIDGE National Center to broaden access to modern data science and artificial intelligence for scientists across many research fields. Researchers in data-intensive domains, such as criminology, public health, and neurobiology, increasingly need to analyze large datasets and use advanced AI and machine learning methods. The Center will directly address these needs, serving as a vital resource for domain researchers who require additional programming expertise, computing infrastructure, or technical support. Specifically, BRIDGE will eliminate traditional barriers to entry by providing an open, supported, and shareable infrastructure that enables researchers to build, reuse, and collaborate on data-analysis workflows, datasets, and machine learning models. By lowering technical barriers and expanding access to cloud-based computing resources, the Center will empower a broader range of scientists to participate in data-driven discovery. Ultimately, the project serves the national interest by promoting the progress of science, strengthening research infrastructure, advancing education and workforce development, improving reproducibility, and enabling discoveries that benefit national health, prosperity, and welfare. The Center will build on "Apache Texera (Incubating)", an open-source, web-based workflow system that supports data science, real-time collaboration, workflow sharing, and elastic access to cloud computing resources. BRIDGE will operate and coordinate Texera-based platforms across multiple scientific communities while providing documentation, tutorials, training, office hours, and technical support. The project will continue to strengthen the usability, scalability, reproducibility, privacy, security, and efficiency of these platforms during the operations. Technical goals include developing more intuitive workflow construction and execution interfaces, domain-specific AI assistants and co-pilots with privacy and safety protections, mechanisms for protecting data and workflows, automated migration of script-based analyses into reusable workflows, and seamless use of cloud resources for large-scale and GPU-intensive computation. The resulting infrastructure will enable scientists to create, execute, share, and reproduce complex data science and AI pipelines more easily, thereby accelerating multidisciplinary research and expanding the impact of open, collaborative scientific computing. 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. NSF Award ID: 2609582 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Chen Li | Institution: University of California-Irvine, IRVINE, CA | Award Amount: $8,998,359 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2609582 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2609582.html