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.
Making data science hybrid, automated,trusted and actionable
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.
Multiple research groups collaborate on this research domain. This table mentions the contact person and his/her affiliation.
WP1 Lead: AI-assisted Data Acquisition and Pre-Processing, KU Leuven - CS/DTAI
WP2 Lead: Integrated learning and reasoning, KU Leuven - CS/DTAI
WP3 Lead: AI-Assisted Data Exploration, UGent - IDLab
WP4 Lead: Automation in machine learning, UGent - IDLab
WP5 Lead: Trustworthy AI, KU Leuven - ESAT/STADIUS
WP6 Lead: Decision Support Systems, KU Leuven- ESAT/PSI
WP7 Lead: Use Cases Healthcare, UGent - VIB - DAMBI
WP8 Lead: Use Cases Industry, KU Leuven - ESAT/STADIUS