EAGER: PBI: Modeling Place-Based Innovation by Leveraging AI-Enabled Dynamic Graph Techniques

Place-based innovation (PBI) leverages a region''s existing research institutions, universities, and industries to establish clusters of innovation, address community needs, and spur regional economic development. The importance of this concept has been recognized for decades; however, despite recent progress in PBI research, critical gaps remain in understanding and quantifying key aspects of PBI. These challenges include the lack of insights on the duration and investments required to bring products from initial research through translation phases to eventual market availability. Similarly, there is a need to understand if and how various workforce development (WFD) training approaches impact participant career trajectories and earnings potential over the short, medium, and long terms. Additionally, geography and technology domain introduce further complexity, as relevant data and benchmarks likely differ substantially across regions and sectors. This proposed study aims to address the current limitations by developing modeling solutions to quantify these PBI-related aspects and provide data-driven insights for policymakers and stakeholders. Intellectual Merit: To achieve this goal, this study will significantly advance our understanding of PBI by addressing the intertwined knowledge gaps surrounding innovation cultivation, training efficacy, and the contextual factors that alter their dynamics. The proposed research will develop a foundational graph neural network (GNN) model that enables informed measurements and predictions about the time and capital requirements for commercialization, impacts on WFD, and cross-sector contextual differences in PBI. The GNN model will be pre-trained using widely accessible public datasets and fine-tuned on place- or domain-specific datasets, effectively handling data scarcity challenges that hinder conventional modeling approaches. It will automatically integrate spatial, temporal, and cross-sectional cues in complex data from various sources to address multiple questions. By integrating multi-task and multi-view learning strategies, our innovative approach will leverage diverse datasets, including patent data, regional innovation indices, employment statistics, and cluster mapping information. This unified modeling approach has the potential to provide urgently needed breakthroughs in PBI quantification, addressing current limitations in understanding and data availability. Broader Impacts: This research has the potential to benefit a wide range of stakeholders across sectors, from private companies seeking to optimize technology translation and commercialization strategies to nonprofits and civil society organizations seeking to inform community development programs. Local and national governments can leverage the findings to prioritize resource allocation across sectors, while universities can employ the methods to track innovation cultivation and training outcomes over time. Additionally, individuals, including students and workforce participants, can utilize the provided benchmarks to guide their own career progression. Furthermore, this research has the potential to enhance the educational experiences of undergraduate and graduate students, particularly those from underrepresented groups, by providing them with hands-on training and research opportunities in place-based innovation. Through internships, research assistantships, and mentorship programs, students can gain valuable skills and knowledge in innovation cultivation, technology translation, and entrepreneurship. By empowering organizations and individuals across sectors with a deeper understanding of the timelines, investments, and contextual dependencies associated with converting ideas into societal impacts, this research has the potential to accelerate innovation cultivation, workforce readiness, and broadly advance economic prosperity.