Faculty member of the Department of Information Technology (INTEC)
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iGent, Technologiepark-Zwijnaarde 126,
B-9052 Gent, Belgium
+32 (0)9 331 49 29
+32 (0)9 331 48 99
eric.laermans - @ - ugent.be
Professor Laermans's current research focuses on machine learning, surrogate modeling (or metamodeling) and design of experiments.
Eric Laermans was born in Brussels, Belgium on September 27, 1971.
He received the M.S. degree in Engineering Physics and the Ph.D. degree in Electrical Engineering from Ghent University, Ghent, Belgium, in 1994 and 1999, respectively.
From 1994 to 1998, he was a Research Assistant at Ghent University, in the Department of Information Technology (INTEC), where his research focused on electromagnetic compatibility (EMC) and on the sensitivity analysis of electromagnetic modeling.
Since 1999, he has been a Postdoctoral Fellow in the same research department. His research domain has evolved from the electromagnetic modeling of high-speed interconnection structures (with special attention to via holes) and reverberation chambers to surrogate modeling and design of experiments (DoE).
Since 2003, he has also been an Assistant Professor in the Internet Technology and Data Science Lab (IDLab) research group of the Department of Information Technology (INTEC), Ghent University, Ghent, Belgium, in the Faculty of Engineering and Architecture.
As author or co-author, he has contributed to several peer-reviewed papers and abstracts in international conference proceedings and journals about computational science and engineering, and numerical analysis.
His research interests include distributed scientific computing, machine learning, design of experiments, optimal design, surrogate modeling (or metamodeling) of complex systems, and numerical analysis techniques.
- Numerical techniques [Multivariate interpolation and approximation, Radial Basis Functions (RBF), Scattered data interpolation, Model Order Reduction (MOR), Model Based Parameter Estimation (MBPE), Optimization]
- Experimental Design / Computer-aided Design [Design Of Experiments (DOE), Response Surface Modeling (RSM), Design and Analysis of Computer Experiments (DACE), Kriging methods, Metamodeling, Data-driven information processing]
- Artificial Intelligence (AI) [Machine Learning (ML), Supervised Learning, Adaptive/Sequential sampling, Reflective Exploration (RE), Genetic Algorithms (GA), Artificial Neural Networks (ANN), Evolutionary Computing (EC)]
- System and Control theory [Data-driven Model Order Reduction (MOR), System Identification based on deterministic simulation-based data, Passivity enforcement, State-space realization, Compressive sampling]
- Communication Networks / Computer Systems [Modeling of Sensor Networks, Characterization of Wireless Networks, Design of Experiments for ICT Testbed]