Faculty member of the Department of Information Technology (INTEC)
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iGent, Technologiepark-Zwijnaarde 15,
B-9052 Gent, Belgium
Previous affiliation : University of Antwerp - COMP research group
Professor Dhaene's current research focuses on distributed scientific computing, machine learning, bioinformatics, macromodeling of electronic components, and surrogate modeling (or metamodeling) of complex (simulation-based) systems.
Tom Dhaene received the M.S. degree and the Ph.D. degree in Electrical Engineering from Ghent University, Ghent, Belgium, in 1989 and 1993, respectively.
From 1989 to 1993, he was Research Assistant at Ghent University, in the Department of Information Technology (INTEC), where his research focused on different aspects of full-wave electromagnetic circuit modeling, transient simulation, and time-domain characterization of high-frequency and high-speed interconnections.
In 1993, he joined the EDA company Alphabit (IMEC spin-off, later acquired by Hewlett-Packard, and now part of Agilent Technologies). He was one of the key developers of the world-leading planar EM simulator ADS Momentum, and he is the principal developer of the multivariate EM-based adaptive metamodeling tool ADS Model Composer and the broadband macromodeling tool ADS Broadband SPICE Model Generator.
Since October 2000, he has been a Professor in the Computer Modeling and Simulation (COMS) research group, University of Antwerp, Antwerp, Belgium, in the Department of Mathematics and Computer Science.
Since October 2007, he is a Full Professor in the Internet Based Communication Networks and Services (IBCN) research group of the Department of Information Technology (INTEC), Ghent University, Ghent, Belgium, in the Faculty of Engineering and Architecture, Ghent University. He is affiliated with iMINDS, an independent ICT research institute founded by the Flemish government.
His modeling and simulation EDA software (Electronic Design Automation) is successfully used by academic, government and business organizations worldwide, for study and design of high-speed electronics and broadband communication systems. Several methods and algorithms he developed are now included in most commercial EDA software packages. As author or co-author, he has contributed to more than 350 peer-reviewed papers and abstracts in international conference proceedings, journals and books about computational science and engineering, numerical analysis, and computer science. He is the holder of 5 U.S. patents. His research interests include distributed scientific computing, machine learning, bioinformatics, signal integrity, electromagnetic compatibility, model order reduction, optimal design, surrogate modeling (or metamodeling) of complex systems, circuit and EM modeling of high-speed interconnections and broadband communication systems, adaptive system identification of LTI systems, and numerical analysis techniques. IEEE Senior member, Fellow of The Electromagnetics Academy (FEMA), and listed in "Who's Who in Computational Science and Engineering".
- Numerical techniques [Multivariate interpolation and approximation, Rational functions, Radial Basis Functions (RBF), Scattered data interpolation, Orthonormal bases for parameter estimation, Model Order Reduction (MOR), Model Based Parameter Estimation (MBPE), Optimization]
- Distributed/parallel computing [Scheduling and Load Balancing, Performance Evaluation and Modeling]
- 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]
- Machine Learning (ML) [Supervised Machine Learning, Adaptive/Sequential sampling, Active Learning, Genetic Algorithms (GA), Evolutionary Computing (EC)]
- Microwave and RF engineering [Computational Electromagnetics (EM), High-speed Interconnect and packaging, Simulation of high-speed/ broadband circuits, Electronic Design Automation (EDA), Signal Integrity (SI)]
- 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]
- Bioinformatics [Gene splicing, Feature Selection, Instance Selection, Model Selection, Ensembles, Classification]