Project: Automating Data Science: the Next Frontiers
2021-01-01 – 2024-12-31
- Abstract
Data science has already had an important effect on science, economy, and society, and its impact continues to grow as high-throughput technologies are being developed, business models adapt, and new opportunities for exploiting data for societal proposes are being developed. Today, the uptake of data science in practice is hindered mostly by a growing skills gap. Indeed, the application of data science techniques still requires sophisticated and rapidly evolving technical skills, and the demand for such skills grows faster than the suitably trained workforce. This project aims to address this skills gap by developing novel principles for automating data science tasks that today seem too openended, too domain-dependent, or involve data that is too complex, to allow doing so.
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- Journal Article
- A1
- open access
Your next state-of-the-art could come from another domain : a cross-domain analysis of hierarchical text classification
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- Conference Paper
- P1
- open access
What large language models do not talk about : an empirical study of moderation and censorship practices
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Next-event prediction in soccer : assessing the impact of team and player information
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- Conference Paper
- C1
- open access
JoLA : job landscape aware job recommendation
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- Conference Paper
- C1
- open access
InfoClus : informative clustering of high-dimensional data embeddings
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Human-AI moral judgment congruence on real-world scenarios : a cross-lingual analysis
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- Conference Paper
- P1
- open access
Content-agnostic moderation for stance-neutral recommendations
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Building data-driven occupation taxonomies : a bottom-up multi-stage approach via semantic clustering and multi-agent collaboration
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- Conference Paper
- P1
- open access
ABCFair : an adaptable benchmark approach for comparing fairness methods
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InfoClus : informative clustering of high-dimensional data embeddings