Tom Dhaene is Full Professor at Ghent University in the Department of Information Technology (INTEC-IDLab) of the Faculty of Engineering and Architecture (FEA). He is also affiliated with imec.
As author or co-author, he has contributed to more than 500 peer-reviewed papers and abstracts in international conference proceedings, journals and books. He is the holder of 5 U.S. patents. His machine learning software (SUMO toolbox) and simulation software is successfully used by academic, government and business organizations worldwide.
His current research interests include data-efficient machine learning, surrogate modelling, Gaussian processes, Bayesian optimization, and system identification.
Keywords: Data-Efficient Machine Learning (DE-ML), Surrogate Modeling, Data-driven engineering design, Bayesian Machine Learning
A surrogate modeling and adaptive sampling toolbox for computer based design D Gorissen, I Couckuyt, P Demeester, T Dhaene, K Crombecq Journal of Machine Learning Research 11 (Jul), 2051-2055
ooDACE toolbox: a flexible object-oriented Kriging implementation I Couckuyt, T Dhaene, P Demeester The Journal of Machine Learning Research 15 (1), 3183-3186
FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data S Van Gassen, B Callebaut, MJ Van Helden, BN Lambrecht, P Demeester, … Cytometry Part A 87 (7), 636-645
A novel hybrid sequential design strategy for global surrogate modeling of computer experiments K Crombecq, D Gorissen, D Deschrijver, T Dhaene SIAM Journal on Scientific Computing 33 (4), 1948-1974
Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization I Couckuyt, D Deschrijver, T Dhaene Journal of Global Optimization 60 (3), 575-594