AI-Driven Data Science
"Unlocking the value of data in a trusted and automated manner, supporting complex decision making and providing new insights that will empower individuals and society in generating major advances in healthcare, education, industry 4.0, energy systems and more."
Many industrial processes and systems in society impose complex preconditions for making decisions. This research line, subtitled 'Making Data Science Hybrid, Automated, Trusted and Actionable', focuses on making automatic analyzes of available data, formulating existing expertise and generating new knowledge through machine learning. And all of this taking into account the requirements in terms of security, ethics and privacy.
| Bart de Moor | KULeuven - ESAT/STADIUS | Management team |
| Piet Demeester | UGent - IDLab | Management team |
| Ann Ackaert | UGent - IDLab | Management team |
| Oscar Mauricio Agudelo | KULeuven - ESAT/STADIUS | Management team |
| Luc De Raedt | KULeuven -CS / DTAI | WP1 Lead: AI-assisted Data Acquisition and Pre-Processing |
| Hendrik Blockeel | KULeuven -CS / DTAI | WP2 Lead: Integrated learning and reasoning |
| Tijl De Bie | UGent - IDLab | WP3 Lead: AI-Assisted Data Exploration |
| Tom Dhaene | UGent - IDLab | WP4 Lead: Automation in machine learning |
| Yves Moreau | KULeuven - ESAT/STADIUS | WP5 Lead: Trustworthy AI |
| Matthew Blaschko | KULeuven -ESAT/PSI | WP6 Lead: Decision Support Systems |
| Yvan Saeys | UGent -VIB - DAMBI | WP7 Lead: Use Cases Healthcare |
| Bart de Moor | KULeuven – ESAT/STADIUS | WP8 Lead: Use Cases Industry |
Multiple research groups collaborate on this research domain. The above table only mentions the contact person and his/her affiliation.
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