Laloux.me | Jean-Francois Laloux - Personal Website

Professional, highly qualified Engineer with excellent organisational and team working skills. Ideal candidate for position requiring drive, initiative, responsibility and challenge.

Master in Computer and Management Engineering (Faculté Polytechnique de Mons, Belgium, Graduated in 2010)
The FPMs is a member of the TIME network (Top Industrial Managers for Europe), an association of 53 leading Engineering Schools and Faculties worldwide.

Master in Business Analytics (Michael Smurfit Graduate School of Business, Ireland, Graduated in 2011)
Michael Smurfit Graduate School of Business is Ireland’s leading business school and research centre, one of less than 50 schools worldwide to hold triple accreditation from the US, Europe and the UK accrediting bodies.

Skills:

Management mathematics and operational research: mathematical programming, stochastic models of operational research, decision-making and management support, computer-aided manufacturing, quantitative analysis techniques, numerical analysis, etc.

Computer science: programming methodology and languages, databases, software engineering, artificial intelligence, system security and administration, etc.

Business management: human resources and communication, marketing, financial analysis and economic and financial calculations, managing innovation, team building, etc.

Telecommunication: Digital and analog signal transmission, network science, social network analysis, computer networking, etc.

Software: Eclipse, PHPMyAdmin, WinDev, Microsoft Visual Studio .NET, Matlab, Project R, Microsoft Office suite, Adobe Creative suite

Programming: Java, C, C++, C#, R, Matlab, Java, PHP, SQL, XML, CSS, VB, SQL, XML, Assembler

Specialties

Management Mathematics, Operational Research, Computer Science, Business Management, Telecommunication, Business Analytics

Summary

Michael Smurfit Graduate School of Business UCD

Master, Business Analytics

2010 – 2011

Master Thesis: Using Genetic Algorithms to Optimize the Chicken Revenue Production System (for Carton Bros Ltd Ireland)

Michael Smurfit Graduate School of Business is Ireland’s leading business school and research centre, one of less than 50 schools worldwide to hold triple accreditation from the US, Europe and the UK accrediting bodies.

Business Analytics is a branch of applied mathematics that uses quantitative and computer techniques to optimise decision-making in business, taking advantage of the large amounts of data now available and meeting the challenges of a dynamic environment.
This programme is comparable to a Masters degree (typically MSc) in Operational Research (UK) or Operations Research (USA).

Activities and Societies: Auditor of the International Students Society

University College Dublin

Master, Computer Science

2010 – 2010

University College Dublin is Ireland’s largest university and is ranked within top 1% of higher educations world-wide.

Erasmus - Master Thesis: Distributed Data Mining: Creating Hierarchical Clustering on Grid

UMons - Faculté Polytechnique de Mons

Master, Computer and Management Engineering

2003 – 2010

Master Thesis on Distributed Data Mining: Creating Hierarchical Clustering on Grid

The FPMs is a member of the TIME network (Top Industrial Managers for Europe), an association of 53 leading Engineering Schools and Faculties worldwide.

The Computer Science, Management and Engineering course programme responds to two essential requirements of our time: on the one hand, the ever-growing need to computerise businesses, and on the other hand the need to manage activities more and more efficiently. Engineers have natural skills placing them at the forefront of computer science. They also have the ability to make a rational and methodical contribution to business management.

Education

Efficient Distributed Approach for Density-Based Clustering
June 2011. IEEE 20th International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises

Nowadays, large bodies of data in different domains are collected and stored. An efficient extraction of useful knowledge from these data becomes a huge challenge. This leads to the need for developing distributed data mining techniques. However, only a few research concerns distributed clustering for analysing large, heterogeneous and distributed datasets. Besides, current distributed clustering approaches are normally generating global models by aggregating local results that would lose important knowledge. In this paper, we present a new distributed data mining approach where local models are not directly merged to build the global ones. Preliminary results of this algorithm are also discussed.