Collaboration Patterns and Socio-Economic Impacts Analysis in Emerging Science and Technology with Machine Learning Algorithms

Our study, “Collaboration Patterns and Socio-Economic Impacts Analysis in Emerging Science and Technology with Machine Learning Algorithms,” aims to understand and forecast the societal and economic impacts of emerging Scientific and Technological (S&T) advancements driven by Leading Organizations (LOs) and their Contributing Partners (CPs). By analyzing historical collaboration patterns and using machine learning, we will estimate the effort and investment needed to advance new technologies. Additionally, we will develop interdisciplinary, data-driven solutions to monitor Workforce Development (WFD) and Diversity, Equity, Inclusion, and Accessibility (DEIA) trends within S&T projects. This research received $300,000 in funding from NSF.

Role: Co-PI

Funding Agency: NSF

Program: Division of Innovation and Technology Ecosystems (ITE)

Total Amount: $300K

Period: 09/2024 - 08/2026