The Management of INnovation, Entrepreneurial Research, and Venture Analysis (MINERVA) group uses cutting-edge Machine Learning (ML) and Artificial Intelligence (AI) techniques to explore the intersection of public policy, systems engineering, and finance. We address questions such as:
- What is the relationship between public and private financing of deep technology ventures?
- How do industrial variations impact these public-private interactions?
- How do gender and geographic disparities manifest in public and private funding?
- Which factors affect success in university technology transfer, and how can policy facilitate technology translation?
- How can we develop new advanced computation and machine learning tools to aid our understanding of these questions?
Our computation projects fall in two general categories: First, we are pioneering application of natural language processing (NLP) tools to analyze company statements such as grant abstracts, entrepreneurial pitches, and related documents. Second, as we study how knowledge is generated and innovation evolves, we are developing new tools to track innovative entities (individuals and firms), with a particular focus on disambiguation challenges in bibliometric analysis.