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

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.

I. Introduction

Building teams in response to an opportunity is a common business activity. Examples are: responding to calls for pro-posals in product and services supply chains, expert teams for a medical procedure at a hospital, players for a match for team-based sports and crew for an airline flight. In this paper, we will focus on teaming for researchers applying to funding agencies in response to their call for proposals, denoted TeamingForFunding.