Research Analytics Summit 2024

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Research analytics has become an indispensable part in today’s higher education institutions’ organizational structure. It not only provides a mechanism or platform to present research awards and expenditures data to internal and external data users, but also serves a superior tool that enables data-driven strategic decision makings with more reliable, valid, and in-depth analysis. The development of Artificial Intelligence techniques could push the boundary of Research Analytics to an even higher level by incorporating the abundance of data scattered across the university. We propose to present one attempt adopted by University at Albany, State University of New York (UAlbany) to incorporate AI techniques to Research Analytics: a project called Research Highlighter-MatchMaker.

The Research Highlighter-MatchMaker project aims to identify research clusters at UAlbany, recommend funding opportunities for faculty/researcher, and suggest potential collaborations. Based on AI and Big Language Models, the project will identify core research clusters by UAlbany faculty and researchers and suggest potential collaborations for multidisciplinary projects across the campus. Moreover, the project aims to recommend more suitable and relevant funding opportunities to different faculty and researchers based on their research track records including their grant proposal abstracts and publication abstracts, with a potential to incorporate more research data associated with researchers such as technology transfer agreements and compliance documents in the future. One major strength of this self-established project is that it takes advantage of AI techniques to provide more personalized and tailored matches for faculty and researchers by considering their research track records on file, instead of providing limited recommendations based on only keyword match that is adopted by many current products on the market. Our purpose is to offer personalized suggestions to faculty and researchers with well-matched funding opportunities, save their time and effort on searching and screening a long list of funding opportunities from databases on their own, and avoid the occurrence of missing good funding opportunities that they could have successfully obtained.


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