Banniere
MAE

Big Data, Large-Scale Optimization and Applications

June 7-9, 2016 – Clermont-Ferrand
LIMOS – CNRS UMR 6158

Organizers

NameDepartmentAffiliation
Hervé KERIVINDept of Mathematics and Computer Science - LIMOSBlaise Pascal UniversityClermont-Ferrand, France
Tamon STEPHENDept of MathematicsSimon Fraser UniversityBurnaby, BC, Canada
Farouk TOUMANILIMOSBlaise Pascal UniversityClermont-Ferrand, France

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 scientists 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.

Participants

NameDepartmentAffiliation
Mourad BAÏOUCNRS - LIMOSBlaise Pascal UniversityClermont-Ferrand, France
Laurent BEAUDOUPOLYTECH' - LIMOSBlaise Pascal UniversityClermont-Ferrand, France
Gisèle BRONNERDept of Biology - LMGEBlaise Pascal UniversityClermont-Ferrand, France
Leonid CHINDELEVITCHSchool of Computing ScienceSimon Fraser UniversityBurnaby, BC, Canada
Rafael COLARES BORGES DE OLIVEIRALIMOSBlaise Pascal UniversityClermont-Ferrand, France
Guilherme D. DA FONSECALIMOSUniversity of AuvergneClermont-Ferrand, France
Yan GERARDLIMOSUniversity of AuvergneClermont-Ferrand, France
Luis GODDYNDept of MathematicsSimon Fraser UniversityBurnaby, BC, Canada
Hervé KERIVINDept of Mathematics and Computer Science
LIMOS
Blaise Pascal UniversityClermont-Ferrand, France
Sergei KUZNETSOVDept of Data Analysis and Artificial IntelligenceNational Research University Higher School of EconomicsMoscow, Russia
Vincent LIMOUZYDept of Mathematics and Computer Science
LIMOS
Blaise Pascal UniversityClermont-Ferrand, France
Etienne MAÎTREResearch and DevelopmentMichelinClermont-Ferrand, France
Engelbert MEPHU NGUIFODept of Mathematics and Computer Science
LIMOS
Blaise Pascal UniversityClermont-Ferrand, France
Lhouari NOURINEDept of Mathematics and Computer Science
LIMOS
Blaise Pascal UniversityClermont-Ferrand, France
Marie PAILLOUXISIMA - LIMOSBlaise Pascal UniversityClermont-Ferrand, France
Catherine RAVELINRA - GDECBlaise Pascal UniversityClermont-Ferrand, France
Maurice QUEYRANNESauder School of BusinessUniversity of British ColumbiaVancouver, BC, Canada
Tamon STEPHENDept of MathematicsSimon Fraser UniversityBurnaby, BC, Canada
Annegret WAGLERISIMA - LIMOSBlaise Pascal UniversityClermont-Ferrand, France
Jinhua ZHAOLIMOSBlaise Pascal UniversityClermont-Ferrand, France

Program

Tuesday, June 07, 2016
TimeActivityLocation
9:30am – 10:00amCoffee and RegistrationISIMA A001
10:00am – 12:00pm Graph and Optimization at LIMOS
Mourad Baïou, Blaise Pascal University
Big Data and Bioinformatics at Blaise Pascal University
Engelbert Mephu Nguifo, Blaise Pascal University
Operations and Logistics Division at Sauder Business School
Maurice Queyranne, University of British Columbia
Mathematics and Computer Science at Simon Fraser University
Tamon Stephen, Simon Fraser University
ISIMA A102
12:00pm – 2:00pmLunchHauts de l'Artière
2:00pm – 3:30pm Modeling convex subsets of points and related shape requirements with integer programming
Maurice Queyranne, University of British Columbia
Minimal dominating set enumeration
Lhouari Nourine, Blaise Pascal University
ISIMA A102
3:30pm – 4:00pmCoffee breakISIMA A001
4:00pm – 6:00pm Specialized gray codes
Luis Goddyn, Simon Fraser University
Chi-binding functions and algorithmic consequences
Annegret Wagler, Blaise Pascal University
On the Combinatorial Complexity of Approximating Polytopes
Guilherme D. Da Fonseca, University of Auvergne
ISIMA A102

Wednesday, June 08, 2016
TimeActivityLocation
9:00am – 10:30am Prediction of ionizing radiation resistance in bacteria using a multiple instance learning model
Engelbert Mephu Nguifo, Blaise Pascal University
Environnemental Microbiology : Metagenomics and bioinformatics.
Gisèle Bronner, Blaise Pascal University
ISIMA A102
10:30am – 11:00amCoffee breakISIMA A001
11:00am – 12:30pm Genomic insights into infectious disease epidemics
Leonid Chindelevitch, Simon Fraser University
High throughput data for wheat genomic selection
Catherine Ravel, Blaise Pascal University
ISIMA A102
12:30pm – 2:00pmLunchHauts de l'Artière
2:00pm – 3:30pm Circuit-Based Pivoting Algorithms
Tamon Stephen, Simon Fraser University
Matchings, partitions, connected subgraphs, and
some application in underprovisioned peer-to-peer networks

Hervé Kerivin, Blaise Pascal University
ISIMA A102
3:30pm – 4:00pmCoffee breakISIMA A001
4:00pm – 5:30pm Extended formulation for the dominating set polytope
Mourad Baïou, Blaise Pascal University
Recognition of digital polyhedra with a fixed number of faces
Yan Gérard, University of Auvergne
ISIMA A102
5:30pm – 5:45pmBreak
5:45pm – 6:30pmDiscussion and closing remarksISIMA A102

Thursday, June 09, 2016
TimeActivityLocation
8:30am – 9:00amCoffee and RegistrationISIMA A001
9:00am – 11:0pm Cancer traitment optimization by learning closed descriptions
Sergei Kuznetsov, National Research University Higher School of Economics
Probing biological networks to support drug discovery
Leonid Chindelevitch, Simon Fraser University
ISIMA A102
11:00am – 11:30amCoffee breakISIMA A001
11:30am – 12:30am Technology Intelligence
Etienne Maître, Michelin
ISIMA A102
12:45am – 2:30pmLunchISIMA A104
2:30pm – 3:30pmDiscussion and closing remarksISIMA A102