Johan Suykens

Research

NEW:

[ENTER] ERC Advanced Grant E-DUALITY

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2012-2017:

[ENTER] ERC Advanced Grant A-DATADRIVE-B

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Interdisciplinary research with emphasis on the theory and applications of:

- Support vector machines and kernel-based learning

- Data-driven modelling and machine learning

- Neural networks and deep learning

- Systems, modelling and control

- Complex networks

- Optimization

- Nonlinear circuits and systems

- Nonlinear signal processing.

 

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AI @ KU Leuven 

• Program director Master AI at KU Leuven
  www.mai.kuleuven.be
  
  
• Steering committee member of Leuven.AI Institute at KU Leuven
  ai.kuleuven.be
  
  
• ELLIS Fellow, Leuven ELLIS (European Laboratory for Learning and Intelligent Systems) unit
  https://ellis.eu/units

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"Deep learning and Kernel Machines": keynote talk at AIAI/EANN 2021 conference (17th International Conference on Artificial Intelligence Applications and Innovations and 22nd International Conference on Engineering Applications of Neural Networks), June 25-27 2021, June 2021 [pdf]

"Kernel spectral clustering and networks applications": keynote talk at 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, The Hague, Netherlands, 7-10 Dec 2020, Dec 2020 [pdf]

"Deep Learning and Kernel Machines: a Unifying Picture": plenary talk at Second Symposium on Machine Learning and Dynamical Systems, The Fields Institute, Toronto, Sept 2020 [pdf]

"Deep Learning, Neural Networks and Kernel Machines: new synergies": keynote talk at IEEE World Congress on Computational Intelligence (WCCI-IJCNN 2020), Glasgow July 2020 [pdf]

"Deep Learning and Kernel Machines": invited talk at Visum summer school, Porto, July 2020 [pdf]

"Generative Restricted Kernel Machines: towards fully explainable deep learning": talk at Data Science Meetup LLN, Dec 2019 [pdf]

"Deep Learning, Neural Networks and Kernel Machines: towards a unifying framework": invited AI Seminar at BeCentral Brussels, Oct 2019 [pdf]

"Deep Learning, Neural Networks and Kernel Machines": invited lecture series at DeepLearn 2019 International Summer School, Warsaw July 2019:
- Part I: Neural networks, (LS)-SVMs and kernel methods [pdf]
- Part II: RBMs, kernel machines and deep learning [pdf]
- Part III: Deep RKM and future perspectives [pdf]

"Future Data-driven Modelling": keynote talk at 26th International Workshop on Intelligent Computing in Engineering (EG-ICE 2019), Leuven, July 2019 [pdf]

"Deep Learning and Kernel Machines: towards a Unifying Framework": plenary lecture at CIARP 2018, Madrid, Nov 2018 [pdf]

"Deep Learning and Kernel Machines": invited lecture series at DeepLearn 2018 International Summer School, Genova July 2018:
- Part I: Neural networks, (LS)-SVMs and kernel methods [pdf]
- Part II: RBMs, kernel machines and deep learning [pdf]
- Part III: Deep RKM and future perspectives [pdf]

"Kernel Methods for Big Data": invited talk at Workshop on New Developments in Statistics and Big data, Leuven July 2018 [pdf]

"Function Estimation, Model Representations and Nonlinear System Identification": keynote talk at Workshop Nonlinear System Identification Benchmarks, Liege April 2018 [pdf]

"Deep Restricted Kernel Machines":

- talk at Leuven Statistics Days, April 2019 [pdf]
- data science seminar at UGent, Feb 2018 [pdf]
- talk at TU Eindhoven, Oct 2017 [pdf]
- LICT Workshop Deep Learning, Leuven June 2017 [pdf]
- Stadius seminar, Leuven June 2017 [pdf]
- related paper (website or [pdf] (open access))

"Learning with primal and dual model representations: new extensions": invited talk at MFO Oberwolfach 2016, Workshop on Learning Theory and Approximation: [pdf]

"Learning with primal and dual model representations: a unifying picture": plenary talk ICASSP 2016, Shanghai: [pdf][youtube]

"SVD meets LS-SVM: a unifying picture": invited seminar at UCL, LLN 2015: [pdf]

"Learning with primal and dual model representations": invited lecture at CIMI Workshop, Toulouse 2015: [pdf]

"Kernel methods for complex networks and big data": invited lecture at Statlearn 2015, Grenoble 2015: [pdf]

"Fixed-size Kernel Models for Big Data": invited lectures at BigDat 2015, International Winter School on Big Data, Tarragona, Spain 2015:
- Part I: Support vector machines and kernel methods: an introduction [pdf]
- Part II: Fixed-size kernel models for mining big data [pdf] [video]
- Part III: Kernel spectral clustering for community detection in big data networks [pdf]

"Fixed-size kernel methods for data-driven modelling": plenary talk at ICLA 2014, International Conference on Learning and Approximation, Shanghai China 2014 [pdf]

"Kernel-based modelling for complex networks": plenary talk at NOLTA 2014, International Symposium on Nonlinear Theory and its Applications, Luzern Switzerland 2014 [pdf]

"Learning with matrix and tensor based models using low-rank penalties": invited talk at Workshop on Nonsmooth optimization in machine learning, Liege Belgium 2013 [pdf]

Invited lecture series - Leerstoel VUB 2012 [pdf]

• Advanced data-driven black-box modelling - inaugural lecture [pdf]
• Support vector machines and kernel methods in systems, modelling and control [pdf]
• Data-driven modelling for biomedicine and bioinformatics [pdf]
• Kernel methods for exploratory data analysis and community detection [pdf]
• Complex networks, synchronization and cooperative behaviour [pdf]

"Models from Data: a Unifying Picture": invited talk at Workshop on Grand Challenges of Computational Intelligence, Nicosia, Cyprus 2012 [youtube]

Invited talk at ICCHA4 2011, Hong Kong [pdf]

Tutorial at IEEE World Congress on Computational Intelligence WCCI 2010, Barcelona Spain [Part I - pdf] [Part II - pdf]

Invited talk at SYNCLINE 2010, Bad Honnef Germany [pdf1] [paper-pdf]

Semi-plenary talk at Symposium on System Identification SYSID 2009, Saint-Malo [pdf]

Plenary talk at International Conference on Multivariate Approximation, 2008 Bommerholz [pdf-1/page] [pdf-4/page]

Plenary talk at MFO Workshop on Learning Theory and Approximation, 2008 Oberwolfach (organizers: K. Jetter, S. Smale, D.-X. Zhou) [pdf-1/page] [pdf-4/page] [paper-pdf]

Invited tutorial: Johan Suykens, International Conference on Artificial Neural Networks ICANN 2007 Porto Portugal: "Support Vector Machines and Kernel Based Learning" [pdf-1/page] [pdf-4/page]

Invited talk at International Conference on Computational Harmonic Analysis 2007 Shanghai China: "Data visualization and dimensionality reduction using kernel maps with a reference point" [pdf] [paper-pdf]

Invited talk at International Workshop on Current Challenges in Kernel Methods CCKM 2006 Brussels Belgium: "Engineering Kernel Machines" [pdf-1/page] [pdf-4/page]

Series of lectures SCCB2006 Modena Italy:
Parts I, II "Support Vector Machines and Kernel Based Learning" [pdf-1/page] [pdf-4/page]
Part III "Ovarian cancer studies" [pdf]
Part IV "Complex networks, synchronization and cooperative behaviour" [pdf-1/page] [pdf-4/page]

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Current research team

Sonny Achten, Yingyi Chen, Henri De Plaen, Fan He, Alex Lambert, Joran Michiels, Joran Michiels, Qinghua Tao, Francesco Tonin, Meixi Wang, Xinjie Zeng

Biography

Johan A.K. Suykens was born in Willebroek Belgium, May 18 1966. He received the master degree in Electro-Mechanical Engineering and the PhD degree in Applied Sciences from the Katholieke Universiteit Leuven, in 1989 and 1995, respectively. In 1996 he has been a Visiting Postdoctoral Researcher at the University of California, Berkeley. He has been a Postdoctoral Researcher with the Fund for Scientific Research FWO Flanders and is currently a full Professor with KU Leuven. He is author of the books "Artificial Neural Networks for Modelling and Control of Non-linear Systems" (Kluwer Academic Publishers) and "Least Squares Support Vector Machines" (World Scientific), co-author of the book "Cellular Neural Networks, Multi-Scroll Chaos and Synchronization" (World Scientific) and editor of the books "Nonlinear Modeling: Advanced Black-Box Techniques" (Kluwer Academic Publishers), "Advances in Learning Theory: Methods, Models and Applications" (IOS Press) and  "Regularization, Optimization, Kernels, and Support Vector Machines" (Chapman & Hall/CRC). In 1998 he organized an International Workshop on Nonlinear Modelling with Time-series Prediction Competition. He has served as associate editor for the IEEE Transactions on Circuits and Systems (1997-1999 and 2004-2007), the IEEE Transactions on Neural Networks (1998-2009), the IEEE Transactions on Neural Networks and Learning Systems (from 2017) and the IEEE Transactions on Artificial Intelligence (from April 2020). He received an IEEE Signal Processing Society 1999 Best Paper Award, a 2019 Entropy Best Paper Award and several Best Paper Awards at International Conferences. He is a recipient of the International Neural Networks Society INNS 2000 Young Investigator Award for significant contributions in the field of neural networks. He has been awarded the 2024 IEEE CIS Neural Networks Pioneer Award. He has served as a Director and Organizer of the NATO Advanced Study Institute on Learning Theory and Practice (Leuven 2002), as a program co-chair for the International Joint Conference on Neural Networks 2004 and the International Symposium on Nonlinear Theory and its Applications 2005, as an organizer of the International Symposium on Synchronization in Complex Networks 2007, a co-organizer of the NIPS 2010 workshop on Tensors, Kernels and Machine Learning, and chair of ROKS 2013. He has been awarded an ERC Advanced Grant 2011 and 2017, has been elevated IEEE Fellow 2015 for developing least squares support vector machines, and is ELLIS Fellow. He is currently serving as program director of Master AI at KU Leuven.

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Books and edited books

 

 

J.A.K. Suykens, J.P.L. Vandewalle, B.L.R. De Moor, Artificial Neural Networks for Modeling and Control of Non-Linear Systems, Springer, 1996 (ISBN 0792396782) [more information]

J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Singapore, 2002 (ISBN 981-238-151-1) [more information]

M.E. Yalcin, J.A.K. Suykens, J.P.L. Vandewalle, Cellular Neural Networks, Multi-Scroll Chaos and Synchronization, World Scientific Series on Nonlinear Science, Series A - Vol. 50, Singapore, 2005 (ISBN 981-256-161-7) [more information]

J.A.K. Suykens, J.P.L. Vandewalle (Eds.) Nonlinear Modeling : Advanced Black-Box Techniques, Springer, 1998 (ISBN 0792381955) [more information]

J.A.K. Suykens, G. Horvath, S. Basu, C. Micchelli, J. Vandewalle (Eds.) Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer & Systems Sciences, Volume 190, IOS Press Amsterdam, 2003, 436pp. (ISBN: 1 58603 341 7) [more information]

J.A.K. Suykens, M. Signoretto, A. Argyriou (Eds.) Regularization, Optimization, Kernels, and Support Vector Machines, Chapman & Hall/CRC, Machine Learning & Pattern Recognition Series, Boca Raton US, 2014, 525 pp (ISBN 9781482241396) [more information]

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Publications: Complete List

 

Publications: Support vector machines and kernel-based learning

Publications: Deep learning, neural networks

Publications: Robustness & AI

Publications: Explainability & AI

Publications: Chaos, synchronization, complex networks

Publications: Systems and control, nonlinear signal processing

Publications: Biomedical applications and bioinformatics

Publications: Optimization

Publications: Quantum mechanics

 

Publications at Google Scholar

 

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LS-SVMlab

Chaoslab

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• TCMM 2014 (September 8-12, 2014 Leuven)
  International Workshop on Technical Computing for Machine Learning and Mathematical Engineering

• ROKS 2013 (July 8-10, 2013 Leuven)
  International workshop on advances in
  Regularization, Optimization, Kernel Methods and Support Vector Machines: theory and applications
  [Videolectures]

• SynCoNet2007 (July 2-4, 2007 Leuven)
  International Symposium on Synchronization in Complex Networks

• NATO-ASI 2002 (July 8-19, 2002 Leuven)
  Advanced Study Institute on Learning Theory and Practice

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Active projects

• Tensor Tools for Taming the Curse
• E-DUALITY - Exploring Duality for Future Data-driven Modelling
• FAIR - AI Research Program

Finished projects

• Deep Restricted Kernel Machines: Methods and Foundations
• Adaptief systeem voor de online detectie en decodering van hersensignalen uit electrode arrays
• Optimization frameworks for deep kernel machines
• A-DATADRIVE-B - "A-DATADRIVE-B: Advanced Data-Driven Black-box modelling"
• AMBioRICS - Algorithms for Medical and Biological Research, Integration, Computation and Software
• Brain-Machine intelligence - Brain-machine interfacing with micro-electrode arrays in the visual cortex
• Cooperative behaviour - Cooperative behaviour in systems: control and optimization
• DYSCO I - Dynamical systems, control and optimization
• DYSCO II - Dynamical systems, control and optimization
• Ford - KUL - Stability analysis and performance improvement of deep reinforcement learning agorithms
• iMinds SBO dotatie - iMinds Medical Information Technologies- SBO (dotatie)
• IntelliCIS - Intelligent Monitoring, Control and Security of Critical Infrastructure Systems
• Knowledge Mining - Knowledge mining with neural networks and support vector machines for the development of customer scoring models
• MaNet - Mathematical engineering tools for Networks: Data driven mining, identification, control and optimization
• Nonlinear Identification - Identification of nonlinear systems and its applications to microwave systems.
• OPTEC I - Optimization in Engineering
• Optec II - Optimization in Engineering Center
• Piecewise - Piecewise Linear Technique in Support Vector Machines and Applications
• POM I - Prognostics for optimal maintenance
• POM II - Prognostics for optimal maintenance
• Robust Statistics - Integration of parametric robust methods and non-parametric kernel based methods.
• STRUCTURED - Modelling structured dynamical systems: parametric and non-parametric approaches
• SVM and kernel methods - Support Vector Machines and Kernel Based Learning: Advanced Algorithms
• SVM-Theory - Support vector machines and kernel methods : theory, algorithms and applications
• Synchronization theory - BIL-Synchronization theory of complex oscillatory networks and applications.
• TAILOR - Foundations of Trustworthy AI Integrating Learning Optimisation and Reasoning
• Tensor-based data similarity - Tensor-based study of data similarity
• yooBeePlus - High Accuracy Indoor Positioning

Awards

• 2024 IEEE CIS Neural Networks Pioneer Award
• Johan Suykens has been elected ELLIS Fellow
• 2019 Entropy Best Paper Award
• "Gouden krijtje" award "Best Prof" in Mathematical Engineering 2018-2019
• ERC Advanced Grant 2017 E-DUALITY
• Fellow IEEE (2015)
• VUB Leerstoel for the academic year 2012-2013 granted to Prof. Johan Suykens (KU Leuven, ESAT-SCD-SISTA-SMC/IBBT Future Health Department)
• ERC Advanced Grant 2011
• International Neural Networks Society INNS 2000 Young Investigator Award
• IEEE Signal Processing Society 1999 Best Paper Award (Senior award)

Editorship

• Associate Editor IEEE Transactions on Artificial Intelligence
• Associate Editor IEEE Transactions on Neural Networks and Learning Systems
• E-Reference Signal Processing: Section Editor Machine Learning
• Associate Editor IEEE Transactions on Circuits and Systems - Express Briefs (2004-2007)
• Associate Editor IEEE Transactions on Neural Networks (1998-2009)
• Associate Editor IEEE Transactions on Circuits and Systems-I (Fundamental Theory and Applications) (1997-1999)
• Associate Editor IEEE Circuits and Systems Magazine (2010-2011)
• Guest associate editor International Journal of Bifurcation and Chaos (2010-2011)

Membership

• Technical committee member IEEE Neural Networks since July 2006
• TI genootschap BIRA bestuurslid since June 2004
• Technical committee member IEEE Signal Processing Society Machine Learning for Signal Processing since January 2004
• Technical committee member IEEE Circuits & Systems Society Nonlinear Circuits and Systems since October 2003
• Technical committee member IEEE Signal Processing Society Neural Networks for Signal Processing since May 2002
• Technical committee member IEEE Circuits & Systems Society Cellular Neural Networks and Array Computing since August 2001

Honours

• invited speaker for the workshop of IEEE Computational Intelligence Society on Grand challenges of computational intelligence.
• Invited talks KVCV-BCS 2010, SYNCLINE 2010, ICCHA 2011, IEEE-CIS Grand Challenges 2012, VUB Leerstoel 2012, RANSO2013, NOLTA 2014, ICLA 2014, BigDat 2015, Statlearn 2015, CIMI 2015, ICASSP 2016, MFO 2016, Nonlinear Sysid Benchmarks 2018, DeepLearn 2018, CIARP 2018, EG-ICE 2019, DeepLearn 2019, VISUM 2020, ICORS 2020, WCCI-IJCNN 2020
• Invited talks before 2010: NDES 1999, PASE 2000, IEEE-IMTC 2001, IJCNN 2001, ICRM 2002, FOCM 2002, VOC 2005, STATUA 2006, SCCB 2006, CCKM06, ASCI 2007, ICCHA 2007, MFO 2008, BOMM08, SYSID 2009
• Tutorial speaker IJCNN 2000, IJCNN 2003, IJCNN 2005, ICANN 2007, WCCI 2010
• Chairman and programme director of Master of Artificial Intelligence Programme K.U. Leuven (2004-2008 and Aug 2017- now)
• IEEE Computational Intelligence Society Benelux Chapter
• Program co-chair International Joint Conference on Neural Networks (IJCNN 2004)
• Director NATO Advanced Study Institute on Learning Theory and Practice 2002
• Minicourse speaker ECC 2001
• Program co-chair International Symposium on Nonlinear Theory and its Applications (NOLTA 2005)
• Co-chair International Symposium on Synchronization in Complex Networks (SynCoNet 2007)
• co-organizer NIPS 2010 Workshop on Tensors, Kernels and Machine Learning (TKML 2010)

Publications

Publication list of Johan Suykens.