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|Name||EWG DSO, EURO working group on Data Science meets Optimization|
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|Board||Edmund Burke (Queen Mary University London, UK) |
Patrick De Causmaecker (KU Leuven, Belgium)
Holger Hoos (University of British Columbia, Canada)
Jin-Kao Hao (University of Angers and Institut Universitaire de France, France)
Andrea Lodi (Polytechnique Montral, Canada)
Marco Lübbecke (RWTH Aachen University, Germany)
Michela Milano (Universita di Bologna, Italy)
Barry O'Sullivan (University College Cork, Ireland)
Ender Özcan (University of Nottingham, UK)
Kate Smith-Miles (Monash University, Australia)
Andrew J. Parkes (University of Nottingham, UK)
Thomas Stützle (Universite Libre de Bruxelles, Belgium)
Mike Trick (Carnegie Mellon University, USA)
|Coordinator||Patrick De Causmaecker|
Katholieke Universiteit Leuven
Etienne Sabbelaan 53
BE-8500 Kortrijk Flanders
Phone: +32 (0) 56 24 60 02
Fax: +32 (0) 56 24 60 52
University of Nottingham
Jubilee Campus, Wollaton Road
NG8 1BB Nottingham
|Coordinator||Andrew J. Parkes|
University of Nottingham
School of Computer Science
NG9 1BB Nottingham
Phone: +44(0) 115 9514210
|Purpose and history||The group was officially created May 2016. |
It aims to promote the interaction of data science (DS) and optimisation (O), and better exploitation of the areas in which they overlap. By 'optimisation techniques' we intend a broad interpretation that includes the wide range from exact methods (branch and bound mathematical programming, etc.) to heuristics and metaheuristics, and others. Of particular interest are the two natural directions: 'Usage of DS for O', and 'Usage of O for DS'.
'DS for O'
Many decision support problems require the usage of advanced optimisation techniques in order to make good, or the best, decisions. Examples, arising in OR, CS, and AI, include direct practical resource allocation problems such as scheduling, timetabling, networks, facility location, transport and routing, and many others, However, the complexity of such techniques has increased to the point where their configuration and control can in itself become a significant challenge; these 'optimisation of the optimiser' tasks are already being addressed by machine learning and statistical techniques and so are good ground for applications of DS. This includes data driven model description, algorithm configuration and construction as well as active learning of best decision making strategies.
'O for DS'
Many problems within DS are themselves naturally framed as optimisation problems. Examples include feature selection, data classification, clustering, data mining, big data problems, expectation maximisation, error minimisation, etc. The group aims to support the exploration of a wide range of state-of-the-art optimisation techniques to these problems.
Applications and theory of data science and optimisation require inherently different skills than those in developing search methods for specific domains. This group aims to bring together the relevant groups of people.
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