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MLMDA : Machine Learning & Massive Data Analysis

The hub on Machine Learning and Massive Data Analysis was created in January 2012 as a result of the growing activity of CMLA in the context of predictive modeling and data-driven applications. It addresses decision problems involving real data with systematic confrontation to concrete applications.

Given the fast evolution in the field of theoretical machine learning, it had become clear that further innovation would mainly result from understanding the challenges related to real-life applications where additional constraints on the process of data collection and specific decision criteria occur.

The group focuses on exploring digital data from internet, industry, and simulation through three machine learning or statistical approaches:
  • Scoring and ranking nonparametric methods for high dimensional data,
  • Graph data mining, modeling, and inference,
  • Active learning and sequential optimization.


  • Recommender systems for e-commerce applications
  • Pattern recognition and active vision
  • Machine learning methods applied to financial data
  • Statistical estimation and monitoring of extreme or abnormal events in the field of energy and water management
  • Uncertainty control and experimental design in physics and fluid mechanics

Coordinator :

MVA Master Program

The research master MVA (for Mathematics, Vision, Learning) is by now considered by experts the best master on imaging and learning theory worldwide. More...


Organizing the "Learning Theory : State of the Art" conference with the French Mathematical society (SMF), May 9-11th 2011, at the IHP Paris More...