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

• URL: http://arxiv.org/abs/2201.05646v1
• Date: Thu, 13 Jan 2022 02:48:42 GMT
• Title: ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls
• Authors: Biplav Srivastava, Tarmo Koppel, Ronak Shah, Owen Bond, Sai Teja Paladi, Rohit Sharma, Austin Hetherington
• Abstract summary: 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.
• Score: 5.75290474288665
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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