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.
@article{arxiv.2201.05646,
title = {ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls},
author = {Biplav Srivastava and Tarmo Koppel and Sai Teja Paladi and Siva Likitha Valluru and Rohit Sharma and Owen Bond},
journal= {arXiv preprint arXiv:2201.05646},
year = {2022}
}
Comments
8 pages, Accepted to IEEE ICDM Workshop on AI for Nudging and Personalization (WAIN) 2022