Louis Wehenkel

Welcome to
Louis Wehenkel's home page



Affiliation at the University of Liège:


Montefiore Institute

About me (briefly):

I am a former research associate of the Belgian National Fund for Scientific Research (F.N.R.S.) and, since 1998, professor of stochastic methods in the
Department of Electrical Engineering and Computer Science, at the University of Liège.

I do research in machine learning, stochastic simulation, and optimization, with applications in electric power systems, industrial process control, bioinformatics and computational systems biology. I am teaching courses on probability and statistics, information theory and coding, stochastic processes, machine learning and data mining.

ORCID iD iconorcid.org/0000-0001-6649-2405

To reach me :
Mail : University of Liège - Institut Montefiore, Sart-Tilman B28, B-4000 Liège, Belgium.

Phone : Int+ 324 366 2684

Fax : Int+ 324 366 2984

Email : L.Wehenkel@uliege.be

My research interests :

Back to top of this page Machine learning, in particular tree based models, graphical models, and reinforcement learning, with a focus on complex and large scale systems analysis, optimization and control.

Electric power systems reliability management and optimal decision making under uncertainties (system operation, asset management, grid development).

Bioinformatics and computational systems biology, in particular biological systems and structures modeling, exploitation of proteomics, genomics, clinical and biomedical imaging data.


Back to top of this page Information and coding theory: (ULg)

Introduction to machine learning: (ULg)

Advanced machine learning: (ULg)

Analysis of electric power and energy systems (ULg)

Planning and operation of electric power and energy systems (ULg)

Practices and evolution of the electric power and energy industry (ULg)

Publications :

Back to top of this page Google Scholar.

ORBI: University of Liège Repository of Publications.

Selected Presentations :
Back to top of this page
CAMBRIDGE 2019: Probabilistic Reliability Management for Electric Power Systems Operation Seminar at the Control Group (University of Cambridge), Cambridge, Apr. 25, 2019. (9.3 Mbytes)

DTU 2018: Machine Learning for Probabilistic Power Systems Reliability Management Seminar at the DTU (Danish Technical University), Lyngby, Nov. 23, 2018. (7.3 Mbytes)

LIST 2016: Big data, machine learning, and optimization, for power systems reliability Workshop at the LIST (Luxembourg Institute of Technology), Belvaux, Nov. 9, 2016 (10.2 Mbytes)

NRC 2015: How to combine observational data sources with first principles of physics to build stable and transportable models for power system design and control? Plenary presentation at "Analytic Research Foundations for the Next-Generation Grid". A workshop of the National Research Council of the National Academies, Irvine Feb. 11-12, 2015 (10.2 Mbytes)

PSCC 2014: Adavanced optimization for power systems, Plenary survey presentation at 18th PSCC - Wroclaw, August 20, 2014 (4.6 Mbytes)

LRI-Orsay-2011 : Regression tree ensembles in the perspective of kernel-based methods, Laboratoire de Recherche en Informatique - Paris 11 - Orsay, Avril 23, 2011 (1.9 Mbytes)

MPI-Tuebingen-2009 : Regression tree ensembles in the perspective of kernel-based methods, Max Planck Insitute for Biological Cybernetics - Tuebingen, October 30, 2009 (1.9 Mbytes)

IAP V Study day 05 : Decision and regression tree ensemble methods and their applications in automatic learning, IAP V Study day, Colonster, May 19, 2005 (2563581 bytes)

ORBEL05 : Decision and regression tree ensemble methods and their applications in automatic learning, ORBEL Symposium on data mining, Brussels, March 16, 2005 (2596449 bytes)

EC-ICT05 : Closure of session 3, The future of ICT for power systems: emerging security challenges, European Commission, Brussels, February 3-4, 2005 (441856 bytes).

IREP2004 : Whither dynamic congestion management?, IREP Workshop, Contrina d'Ampezzo, August 2004. (98816 bytes)

CBRN2001 : Recent developments in tree induction for KDD. «Towards soft tree induction», Brasilian conference on Neural Networks, Rio, April 2001. (1592832 bytes)

PICA99 : Automatic learning and data mining applied to security assessment, PICA99 panel session, Santa Clara (Ca), May 1999, slides powerpoint gzipped (765770 bytes).

IBM-ARC 99 and IFSA97: Discretization of continuous attributes for supervised learning. Variance evaluation and variance reduction, May 1999, slides pdf (135148 bytes).

IBM-ARC 99 and IPMU92 : A global tree quality measure and its use for pruning, May 1999, slides pdf (123098 bytes).

LESCOPE98 : Visualizing Dynamic Power System Scenarios for Data Mining, LESCOPE98, Halifax (NS), June 1998, slides (351583 bytes).

IEEEWM98 : Artificial Intelligence Methods for Voltage Stability Assessment, IEEE PES Winter Meeting, Tampa (Fl), February 1998, slides of presentation to the Power System Stability Subcommittee (452086 bytes).

KDDLyon97 : Data mining and KDD Winter School, University of Lyon, Lyon (Fr), December 1997, slides of presentation (813919 bytes).

CPSPP97 : Tutorial on Intelligent Systems and their Power System applications, IFAC-Cigré Symp. on Control of Power Systems and Power Plants, Beijing (PRC), August 1997, course notes (330583 bytes).

PICA97 : Tutorial on Automatic Learning Methods. Application to Dynamic Security Assessment, IEEE Power Industry Computer Applications Conference, Columbus (Oh), May 1997, course notes (530313 bytes).

Last update: September 2019