You are invited to two events on "Data Science for Optimisation"
- Stream at EURO 2016 (July 3-6, 2016 in Poznan, Poland)
session code: a7cf2cd3
Deadline: March 15
- Workshop in Leuven, Belgium. April 13-15
Deadline: March 25
There are many real-world tasks that require using some optimisation
technique; ranging from scheduling, timetabling, vehicle routing, to the
optimisation problems that occur within machine learning, statistics, or
data science in general. A recurrent challenge is that optimisation
algorithms are themselves difficult and time-consuming to design,
implement, configure, and deploy. A wide range of existing methods
attempt to remedy this, e.g. by using statistical and machine learning
techniques so that optimiser itself can learn to adapt and so optimise
better. Many of these can be described as using data science methods to
improve optimisation methods. For example, in operational research (OR)
and artificial intelligence (AI), machine learning techniques are used
to configure, tune and select different optimisation algorithms.
However, although there there are many approaches in this direction, the
common interests can be obscured by a wide variety of different names
and terminologies, due to their origins in different research
We are motivated by the view that increased interaction between 'data
science' and 'optimisation' is potentially of great benefit to both. In
particular, applying data science techniques to improve optimisation
algorithms also has potential benefits to data science itself. The
optimisation algorithms are themselves a rich source of data and the
ability to test new ideas for prediction, and the control of the
decisions that optimisation algorithms make during their execution. The
control system of the optimisation algorithm may well use a combination
of agents, evolutionary search methods (genetic algorithms and genetic
programming) and data science techniques. Besides classic OR areas such
as scheduling, timetabling, and routing, the target optimisation
algorithms may also be ones used in data science. That is, data science
techniques, working along with OR optimisation techniques, have the
potential to improve the optimisation techniques used in data science
itself as well as in more traditional optimisation areas.
We hence invite participation to help promote interaction such methods
within OR, but also enhance interaction with other related disciplines
developing data science, such as AI, evolutionary computing, and control
Stream at EURO 2016 (July 3-6, 2016 in Poznan, Poland)
Deadline: March 15, 2016
In the European Conference in OR "EURO 2016"
we are running a stream "Data Science in Optimisation".
You are invited to submit a brief abstract (maximum 1500 characters;
about 1-2 paragraphs), and if accepted then to give an associated
presentation. Note that the process is very lightweight as it does not
require a full paper or even extended abstract. Hence although the
deadline of March 15 is rather close there is still ample time to
Please use the following web page for submissions:
using invitation/session code a7cf2cd3
Workshop in Leuven, Belgium. April 13-15
We are also organising a meeting on ""Data Science for Optimisation"
in Leuven (just outside Brussels) with the intent of setting
up a EURO Working Group
The motivation is to contribute to increasing the interaction and
exchange of techniques between data scientists and those developing
practical optimisation algorithms. Please consider attending, and we are
also seeking presentations from anyone that is working in optimisation
and or data science and would like to (informally) present relevant
work. Please register, before March 25, and/or feel free to contact us
for further details or to send us an abstract for a contributed talk.
[Please distribute to anyone that you think might be interested.]
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