ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls
This addresses the challenge of efficient team formation for researchers responding to periodic funding opportunities, though it appears incremental as it builds on existing matching and NLP techniques.
The paper tackles the problem of forming research teams in response to funding proposal calls by developing a data-driven AI system that extracts and normalizes technical skills using NLP, matches constraints, and provides initial feedback from university researchers, resulting in a published dataset for broader use.
We introduce an emerging AI-based approach and prototype system for assisting team formation when researchers respond to calls for proposals from funding agencies. This is an instance of the general problem of building teams when demand opportunities come periodically and potential members may vary over time. The novelties of our approach are that we: (a) extract technical skills needed about researchers and calls from multiple data sources and normalize them using Natural Language Processing (NLP) techniques, (b) build a prototype solution based on matching and teaming based on constraints, (c) describe initial feedback about system from researchers at a University to deploy, and (d) create and publish a dataset that others can use.