ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls

TL;DR

ULTRA is a data-driven AI system that automates team formation for research proposals by extracting skills from multiple sources, normalizing data with NLP, and matching researchers to calls based on constraints.

Contribution

The paper presents a novel AI-based prototype for team formation that integrates multi-source data extraction, NLP normalization, and constraint-based matching.

Findings

Initial positive feedback from researchers at a university.

Created and published a dataset for team formation research.

Demonstrated effective skill extraction and matching techniques.

Abstract

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.