Dirk Deschrijver obtained in 2007 the PhD in Computer Science from the Dept. of Mathematics and Computer Science at the University of Antwerp, Belgium. In May-October 2005, he was a Marie Curie Fellow in the Scientific Computing group at the Eindhoven University of Technology in Eindhoven, The Netherlands. In 2006 and 2008, he was a visiting researcher at SINTEF Energy Research in Trondheim, Norway and the University of L’Aquila in Italy. From 2008-2014, he worked as an FWO post-doctoral research fellow in the IDLab research group in the Department of Information Technology at Ghent University. In 2012, he obtained a second PhD degree degree, in engineering, at Ghent University. Since October 2014, he has been working as a senior researcher at iMinds/imec. Since October 2016, he is as an associate professor in the IDLab research group of Ghent University, working on data analytics, machine learning and surrogate modeling algorithms.
Keywords: Data analytics, machine learning, time series, surrogate modeling
K. Crombecq, D. Gorissen, D. Deschrijver, T. Dhaene, A Novel Hybrid Sequential Design Strategy for Global Surrogate Modeling of Computer Experiments, SIAM Journal on Scientific Computing, vol. 33, no. 4, pp. 1948-1974, 2011.
I. Couckuyt, D. Deschrijver, T. Dhaene, Fast Calculation of the Multiobjective Probability of Improvement and Expected Improvement Criteria for Pareto Optimization, Journal of Global Optimization, vol. 60, no. 3, pp. 575-594, November 2014.
N. Staelens, D. Deschrijver, E. Vladislavleva, B. Vermeulen, T. Dhaene, P. Demeester, Constructing a No-Reference H.264/AVC Bitstream-based Video Quality Metric using Genetic Programming-based Symbolic Regression, IEEE Transactions on Circuits and Systems in Video Technology, vol. 32, no. 8, pp. 1322-1333, August 2013.
J. van der Herten, I. Couckuyt, D. Deschrijver, T. Dhaene, A Fuzzy Hybrid Sequential Design Strategy for Global Surrogate Modeling of High-Dimensional Computer Experiments, SIAM Journal on Scientific Computing, vol 37, no. 2, pp. A1020–A1039, April 2015.
.L. De Baets, J. Ruyssinck, C. Develder, T. Dhaene, D. Deschrijver, On the Bayesian Optimization and Robustness of Event Detection Methods in NILM, Energy and Buildings, vol. 145, pp. 57-66, June 2017.