This project is an ExpandAI@SD Partnership between the San Diego State University (SDSU) and The Institute for Learning-enabled Optimization at Scale (TILOS). In this project, a minority-serving institution leads a new collaboration with an AI Institute focused on scaling up already-established research and education programs at SDSU and to pursue shared, complementary goals to develop safety-conscious scalable AI and to develop the next generation of AI education and workforce talent. The collaboration focuses on providing cutting-edge AI research and education to the diverse community of innovators and future leaders in the San Diego region through sustainable collaborations in the AI Institutes ecosystem that also leverage and expand AI initiatives at SDSU. The resulting research collaborations will engage faculty and students at SDSU with those in a wide range of TILOS partner institutions, including the University of California San Diego, the Massachusetts Institute of Technology, Yale University, the University of Pennsylvania, and the University of Texas at UT Austin. Through a range of research and education initiatives, the project will build community and new centers of excellence in AI between these institutions, involving outreach to new minority serving organizations and communities.
This mutually beneficial partnership in research, education/workforce development, and infrastructure will be centered on investigation of AI techniques to confront the fundamental research challenges in the optimization of autonomous systems, such as robotic systems and intelligent edge networking devices, especially in the presence of uncertainty. The research encompasses both theoretical foundations of AI in learning and optimization and their applications to autonomous systems, building upon and strengthening the research pillars already established under the TILOS AI Institute. Collaborative research in trustworthy AI decision making under uncertainty in the domain of autonomy will addressing reliability challenges for autonomy under uncertainty. In another thrust, AI-driven optimization for distributed autonomy on the edge will achieve scale and tackle the practical AI deployment challenges that exist at the edge of distributed computing systems, including distributed optimization over communication graphs, motion planning, reinforcement learning (RL), and stochastic games. These efforts will directly inform a comprehensive range of collaborative education and workforce development activities, ensuring accessibility and availability of AI, optimization, robotics, and networking education to students from diverse backgrounds. Project goals include the enhancement of underrepresented minority participation in AI education and research while fostering a diverse talent pipeline that encompasses paths to both industry and graduate programs through expansion of AI course offerings with tailored materials for diverse students, programs that enhance student engagement, new undergraduate summer internship programs and graduate research symposiums, and training programs for faculty. The project is partially funded by the Directorate for STEM Education (EDU), under the Louis Stokes Alliances for Minority Participation (LSAMP) program and the IUSE: Hispanic Serving Institution program.
