In this pilot project, core literacy and advanced skills at the intersection of Secure, Safe, Reliable (SSR) Computing, High Performance Computing (HPC), and artificial intelligence (AI) are integrated into educational curricula and training materials to prepare faculty, undergraduate, and graduate students at institutions with relatively low rates of advanced cyberinfrastructure (CI) adoption for large-scale secured data analytics. From self-driving vehicles to smart digital personal assistants and real-time multilingual translators, applications of AI have become omnipresent in our daily lives. There is an urgent need to ensure that current and future scientists who advance AI, as well as practitioners who use AI, understand the limitations of AI and how to develop robust and dependable AI. The long-term goals of this project are to contribute to a pipeline for a SSR AI-minded CI workforce and a self-sustaining advanced CI ecosystem.
In this project, inspired by authoritative sources such as Open AI and Partnership on AI, curricular modifications and materials are developed to educate computer science (CS) students in SSR techniques from the outset. Intensive, multi-faceted, modular, experiential learning units are designed to upgrade the skills of current and future CI users rapidly, so they can apply their new skills to their tasks. The loosely coupled modules can be integrated into existing classes, including elementary CS classes taken by non-CS STEM students. Students participate in research activities, which train next generation interdisciplinary scientists, including many from underrepresented groups. Universities at varied levels and varied locations as well as community colleges are included in the project. Using a collective impact plan, a group of multi-discipline, public-private-sector experts provide guidance and participate in train-the-trainer activities to multiply the effect. Lessons learned and best practices are codified into blueprints for reusability and widespread future adoption across STEM disciplines.
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.
