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Big Data, Large-Scale Optimization and Applications


June 7-9, 2016 – Clermont-Ferrand

In most organizations, decision makers manipulate data to describe, predict, and improve business performance. This data-driven process, known as analytics, now applies advanced mathematical and computational techniques to extract insights from the data and support well-founded decisions.

Today data is available from more sources and is increasing in volume, velocity, variety, variability, and complexity. The exponential growth and availability of data, often referred to as big data, allow us to develop models with unprecedented scale and details. An effective use of these models in decision making represents a challenge for existing computational techniques. Recent theoretical and computational breakthroughs show the promise of pushing the boundaries of our knowledge in mathematics and computer science to answer the challenges of big data analytics.

Optimization is an interdisciplinary area of mathematics and computer science that lies at the center of modern science and engineering. It aims at identifying the best of a set of available alternatives given in a mathematical model. Its ultimate goal is to devise efficient methodologies (i.e., algorithms) to generate the best possible solution to a problem. The promise of modern analytics depends on these methodologies which represent an exciting area of current research.

Among applications, molecular biology offers a wide range of challenges dealing with big data as well as optimization techniques. High-throughput datasets interpretation has remained one of the central challenges of computational biology over the past decade. In addition, as the amount of biological knowledge increases, it is still more and more difficult to meaningfully manage, integrate, and analyze this data. Graph theory, machine learning and optimization provide methods and tools that can contribute to deal with those challenges.

This workshop first intends to bring together, on June 06 and 07, mathematicians and computer scientists from Vancouver, British Columbia and Clermont-Ferrand, France, to exchange new ideas and discuss research directions in the fields of big-data analytics and large-scale optimization. Through an industrial day, organized on June 09, the workshop will also gather together mathematicians and computer scientist from both academia and industry to confront challenging problems in industrial big data and large-scale optimization.

The goals of this workshop are twofold. First, it aims to set up a collaborative network of Canadian and French researchers in mathematics and computer science. Secondly, it intends to identify promising new research projects with an emphasis on big data, optimization problems of increasingly greater scale and their applications.

Voir en ligne : plus d’informations sur le workshop