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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