Mohamed Laib

Research topic

I am interested in data science and knowledge extraction from data. My current work is about developing new tools for mining environmental data using several statistical tools and machine learning algorithms. My research aims at understanding and investigating the mechanisms that govern an environmental phenomenon in Switzerland (e.g. wind speed, precipitation …).

My work focuses on some important problems that can be faced when dealing with environmental data. My colleagues and I proposed some methodologies and tools:

  • Novel methodologies for exploring multivariate data using complex network approach and time series analysis;
  • A new filter algorithm for unsupervised feature selection problems, to reduce the redundancy in the input space, using space filling concept;
  • A new framework of spatial modelling of data characteristics such as extreme wind speed distribution and multifractal parameters using extreme learning machine.
  • Some useful and open-source R libraries and Python codes devoted to data mining tasks, available on CRAN and/or GitHub.

I have a master degree in applied statistics from the University of Constantine in Algeria. During my master, I collaborated with some researchers at the Mathematics department. We developed some algebraic methods for constructing experimental sampling designs presented as well in some R packages available on CRAN.