Tijl De Bie

Tijl De Bie

Tijl De Bie

email: Tijl.DeBie@UGent.be
tel.: +32 9 331 49 13
research unit: FORSIED

Tijl De Bie has been a Full Professor at the University of Ghent since 2015. Before moving to Ghent, he was Assistant and Associate Professor at the University of Bristol (9 years), a postdoc at the KU Leuven (1 years) and the University of Southampton (1 year). He completed his PhD on machine learning and advanced optimization techniques in 2005 at the KU Leuven. During his PhD he also spent a combined total of about 1 year as a visiting research scholar in U.C. Berkeley and U.C. Davis.

He is currently most actively interested in data-driven Artificial Intelligence (AI), and more specifically in the foundations and applications of (exploratory) Data Science. His focus is increasingly on automating Data Science, human-centric AI (interactivity, privacy, explainability, fairness), and structured data such as graphs. He currently holds a grant portfolio of around EUR 4M, including an ERC Consolidator Grant titled “Formalizing Subjective Interestingness in Exploratory Data Mining” (FORSIED), as well as an FWO Odysseus grant titled “Exploring Data: Theoretical Foundations and Applications to Web, multimedia, and Omics Data”.

Keywords: Foundations of Data Science, Human-Centric Data Science, Exploratory Data Analysis, Machine Learning, Artificial Intelligence

Key publications
  • Lanckriet, Gert RG, Tijl De Bie, Nello Cristianini, Michael I. Jordan, and William Stafford Noble. “A statistical framework for genomic data fusion.” Bioinformatics 20, no. 16 (2004): 2626-2635.

  • Lampos, Vasileios, Tijl De Bie, and Nello Cristianini. “Flu detector-tracking epidemics on Twitter.” In Joint European conference on machine learning and knowledge discovery in databases, pp. 599-602. Springer, Berlin, Heidelberg, 2010.

  • De Bie, Tijl, Léon-Charles Tranchevent, Liesbeth MM Van Oeffelen, and Yves Moreau. “Kernel-based data fusion for gene prioritization.” Bioinformatics 23, no. 13 (2007): i125-i132.

  • De Bie, Tijl. “Maximum entropy models and subjective interestingness: an application to tiles in binary databases.” Data Mining and Knowledge Discovery 23, no. 3 (2011): 407-446.

  • Bie, Tijl D., and Nello Cristianini. “Convex methods for transduction.” In Advances in neural information processing systems, pp. 73-80. 2004.

Publication links