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Le Vivant, notre vocation

L’université Paris-Saclay voit officiellement le jour le 5 novembre 2019

AgroParisTech en est l’un des membres fondateurs

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MIA-Paris (Applied mathematics and computing-Paris)

UMR (Joint Research Unit) AgroParisTech, Inra

General scientific orientation

The MIA-Paris unit brings together statisticians and computer scientists specializing in the modeling of machine learning for biology, ecology, the environment, agronomy and food science. The team is skilled in methods of statistical inference (complex models, models with latent variables, Bayesian inference, learning, model selection, etc.) and algorithms (generalization, domain transfer, knowledge representation). The unit develops original statistical and computing methods, which may be generic or driven by precise problems in life sciences. Its activities are based on a strong background in the disciplines concerned : ecology, environmental science, food science, molecular biology, and systems biology.

Fields of research

The unit is divided into three research teams :
Modeling and risks in environmental statistics team (MoRSE)
♦ Studies of environmental and climatic risks, particularly in the domains of pollution and hydrology
♦ Development of statistical techniques for tackling domains in which data are becoming increasingly complex, such as ecology
Research topics : spatial and spatiotemporal statistics (Bayesian hierarchical models, point processes, studies of dependence, conditional simulations of processes), multivariate and spatialized extremes, numerical experiments, uncertainty propagation and Bayesian decision theory, analysis and inference of random graphs, trajectory modeling

Statistics and the genome team
♦ Creation and development of original statistical methods, principally for high-throughput technologies from molecular biology
Research topics : segmentation and detection of breakpoints, modeling of time series, modeling of mixtures and models with hidden structures, analysis and inference of random graphs, detection of motifs, machine learning (model selection, variable selection, classification)

Learning and integration of knowledge team (LInK)
♦ Development of methods for the exploitation of data from multiple, heterogeneous sources, based on an informed choice of shared, multi-scale semantic representation, in the fields of life and food sciences
♦ Study and use of machine learning methods capable of processing dynamic data, possibly from changing environments or from different tasks. One of the objectives of this work is to contribute to the enrichment of expert knowledge
Research topics : modeling and analysis of heterogeneous data
from multiple sources, human and machine multi-expertise
(taking into account in semantics), collaborative and
incremental learning methods

For more information
- Check out the open access publications of MIA-Paris on HAL-AgroParisTech :
- Check out all MIA-Paris’ recorded productions on HAL-AgroParisTech

Contact details
mia-paris chez agroparistech.fr
+ 33 (0)1 44 08 16 64

16 rue Claude Bernard
F-75231 Paris Cedex 05
Tel: 33 (0) 1 44 08 18 43
Fax: 33 (0) 1 44 08 16 00
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