Creating a diverse and inclusive AI research community is the goal of new NSF awards
The U.S. National Science Foundation is excited to announce a new round of ExpandAI awards dedicated to fostering diversity and inclusion within the artificial intelligence research community. In collaboration with the departments of Homeland Security, Agriculture, Defense and the National Institute of Standards and Technology, NSF is awarding grants to seven innovative projects aimed at enhancing AI research and education at minority-serving institutions and historically Black colleges and universities. These initiatives are designed to empower underrepresented groups, promote inclusivity and advance the field of AI through a variety of focused projects.
"By fostering a diverse community of AI researchers and cultivating an inclusive workforce, we ensure that the voices of researchers across our country are represented in the development of this transformative technology," said NSF Director Sethuraman Panchanathan. "This is not just about advancing AI; it's about ensuring that AI innovation supports and benefits the entire nation, leaving no one behind. Through these ExpandAI awards, we are taking significant steps toward a future where AI serves the needs of all communities, not just a select few."
Award highlights:
Texas A&M International University (TAMIU) — "Physics-Based AI Institute for Engineering"
This project aims to build a robust AI research infrastructure and curriculum at TAMIU, with a special emphasis on supporting underrepresented Hispanic and female researchers. The initiative will focus on integrating AI into the engineering curriculum and introducing the new AI-Plus certificate. By enhancing AI education and strengthening AI infrastructure, this project seeks to improve retention and graduation rates among engineering students from disadvantaged backgrounds, preparing them for successful careers in engineering.
Bowie State University (BSU) — "Expanding Use-Inspired AI Research and Instruction Across Campus"
Targeting the underrepresentation of African Americans in the AI workforce, this project supports faculty and students at BSU and other historically Black colleges and universities in Maryland. It will develop AI infrastructure, provide training, establish a faculty learning community, offer research opportunities and create AI-related educational modules for various disciplines. By doing so, it aims to increase diversity in the AI field, empower underrepresented groups and advocate for ethical AI practices.
Savannah State University (SSU) — "AI Research Center for Preserving the Environment"
Focused on minority-serving institutions, this project will establish an AI research center at SSU dedicated to monitoring environmental contaminants using various AI and machine learning technologies, including Tiny Machine Learning (TinyML) systems. These innovative technologies offer practical, cost-effective methods for real-time monitoring and prediction without relying on cloud services, directly benefiting low-income and underserved communities. The project aims to enhance research capacity at SSU and leverage TinyML technologies for the patenting, commercialization and industrial applications of AI innovations, particularly in polluted regions impacting underserved communities.
This project, part of the ExpandAI Capacity Building Pilot at AAMU, focuses on enhancing AAMU's AI capabilities, particularly in swarm computing. It aims to integrate advanced AI research areas like cybersecurity, image analysis, Internet of Things and intelligent robotics into educational offerings, enriching research and learning environments. The initiative seeks to create a diverse, inclusive setting with targeted mentorship and opportunities for underrepresented students, aspiring to develop a leading academic program in AI-driven technologies. This strategic approach is designed to prepare students to lead and innovate in the rapidly evolving AI sector.
Pervasive systems, which integrate computational capabilities into objects and environments like wearables and built spaces, hold immense potential to revolutionize health and wellness through continuous monitoring, intervention and decision-making. However, deploying these technologies in real-world settings presents significant challenges, including high costs and the availability of research infrastructure. To address this, the project is partnering with the NSF AI Institute for Foundations of Machine Learning to leverage its research capabilities and expand ASU's expertise in areas such as robust machine learning, reinforcement learning and robotics. This collaboration aims to impact health management and consumer applications.
In collaboration with the NSF Institute for Learning-enabled Optimization at Scale, this project aims to expand the AI research, education and infrastructure capacity at SDSU, focusing on cultivating talent from diverse communities in the San Diego area for the AI workforce. The initiative will include research experiences at institutions like UC San Diego, the Massachusetts Institute of Technology, Yale University, the University of Pennsylvania and The University of Texas at Austin. The project will address fundamental challenges in the optimization of autonomous systems operating under uncertainty and advance safe AI applications in fields such as autonomous driving, edge computing and smart power networks, enhancing educational outreach programs like robotics data science camps.
Clark Atlanta University (CAU) — "NSF AI Hub at CAU"
This project aims to address the underrepresentation of African Americans in AI research by establishing the NSF AI Hub at CAU. The research team will partner with the NSF AI Institute for Advances in Optimization to establish the first graduate degree programs in AI at an HBCU, thereby creating pathways for students to enter the AI workforce. The initiative prepares students in machine learning, reasoning and mathematical optimization to solve complex, use-inspired problems in various fields. It aims to democratize AI education, reduce biases in AI systems and increase diversity in the AI workforce.