# Predicting Star Scientists in the Field of Artificial Intelligence: A Machine Learning Approach

arXiv: https://arxiv.org/abs/2407.14559 · HTML: https://mlsanity.com/paper/2407.14559/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 118 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Predicting Race and Ethnicity From the Sequence of Characters in a Name](https://mlsanity.com/paper/1805.02109/) (2018)
- [Quantifying patterns of research interest evolution](https://mlsanity.com/paper/1709.03319/) (2017)
- [Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics](https://mlsanity.com/paper/1406.7091/) (2014)
- [Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing](https://mlsanity.com/paper/2007.11604/) (2020)
- [Career on the Move: Geography, Stratification, and Scientific Impact](https://mlsanity.com/paper/1404.6247/) (2014)
- [COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images](https://mlsanity.com/paper/2301.01679/) (2023)
- [Gender-Specific Patterns in the Artificial Intelligence Scientific Ecosystem](https://mlsanity.com/paper/2106.01446/) (2021)
- [A gender analysis of top scientists' collaboration behavior: evidence from Italy](https://mlsanity.com/paper/1906.00754/) (2019)

## Abstract (arXiv)

Star scientists are highly influential researchers who have made significant contributions to their field, gained widespread recognition, and often attracted substantial research funding. They are critical for the advancement of science and innovation, and they have a significant influence on the transfer of knowledge and technology to industry. Identifying potential star scientists before their performance becomes outstanding is important for recruitment, collaboration, networking, or research funding decisions. Using machine learning techniques, this study proposes a model to predict star scientists in the field of artificial intelligence while highlighting features related to their success. Our results confirm that rising stars follow different patterns compared to their non-rising stars counterparts in almost all the early-career features. We also found that certain features such as gender and ethnic diversity play important roles in scientific collaboration and that they can significantly impact an author's career development and success. The most important features in predicting star scientists in the field of artificial intelligence were the number of articles, group discipline diversity, and weighted degree centrality. The proposed approach offers valuable insights for researchers, practitioners, and funding agencies interested in identifying and supporting talented researchers.