mlsanityData to 7 Oct 2026

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

arXiv 2201.05646 ↗First posted 13 Jan 2022cs.IR cs.AI cs.CYcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 13 Jan 2022.

Builds on

5 of 19 references.

References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Introduction to Multi-Armed Banditscited by 475
  3. 2020SPECTER: Document-level Representation Learning using Citation-informed Transformerscited by 426
  4. 2017Fractional Hedonic Gamescited by 10
  5. 2018On Finding Stable and Efficient Solutions for the Team Formation Problemcited by 1

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Citing papers per year: 2023: 1; 2024: 2.

3 papers.