Current position
Director of research
Unit MIA-PS (Applied Mathematics and Computer science) University Paris-Saclay & INRAE, campus AgroParisTech Palaiseau 91120 France
Email : david.makowski@inrae.fr or david.makowski@universite-paris-saclay.fr
Full list of publications accessible here:
- https://scholar.google.fr/citations?hl=en&user=vcnWi7IAAAAJ&view_op=list_works&sortby=pubdate
- https://www.researchgate.net/profile/David-Makowski/publications
Highly Cited Researcher (Clavirate)
Research
My main interests are in statistical/machine learning modelling applied to environmental risk management, agriculture, and food safety. I manage scientific projects and support research groups in analyzing complex datasets using statistical and machine learning methods.
Methods
- Meta-analysis
- Mixed-models
- Machine learning
- Forecasting
- Dynamic models
- Bayesian statistics
Fields of application
- Food security and food safety
- Climate change impact and adaptation
- Greenhouse gas and pollutants
- Agroecology
- Environmental risk assessment
- Decision support
Teaching in graduate schools
- Generalized linear models and Mixed-models applied to agroecology
- Meta-analysis and analysis of networks of experiments
- Uncertainty and Sensitivity analysis with complex crop and environmental models
- Time series and Machine learning
Check some of my lectures on GitHub:
https://github.com/davemakowski?tab=repositories
Consultancy
I support research groups and public organisations in analyzing their datasets:
- Risk analysis (environmental risk, food safety),
- Climate change,
- Development of modelling, classification and forecasting tools,
- Support in conducting meta-analysis.
Education
- HDR (equivalent to DSc) University Paris-Sud (University Paris-Saclay), France, 2007
- PhD INA PG (AgroParisTech-University Paris-Saclay), Paris, France, 2001
- MSc and Engineer INA PG (AgroParisTech-University Paris-Saclay), Paris, France, 1996
Books in english and special issues
- From Experimental Network to Meta-analysis. Methods and Applications with R for Agronomic and Environmental Sciences. Springer. https://www.springer.com/gp/book/9789402416954
- Working with Dynamic Crop Models (3rd edition). Methods, Tools and Examples for Agriculture and Environment. Elsevier. https://www.elsevier.com/books/working-with-dynamic-crop-models/wallach/978-0-12-811756-9
- Environmental Impact of Land Use Change in Agricultural Systems. Sustainable Agriculture Reviews 30. Springer. https://www.springer.com/gp/book/9783319962887
- Special issue in European journal of Agronomy: Evidence synthesis in agronomy. https://www.sciencedirect.com/journal/european-journal-of-agronomy/special-issue/10CLKSSZSF5
- Special issue in Journal of Environmental Management: Multifunctional agriculture - From farm diagnosis to farm design and institutional innovation. https://www.sciencedirect.com/journal/journal-of-environmental-management/vol/90/suppl/S2
Books in French
- Data science pour l’agriculture et l’environnement - Méthodes et applications avec R et Python. Editions Ellipses. https://www.editions-ellipses.fr/
- De l’analyse des réseaux expérimentaux à la méta-analyse. Quae. https://www.quae.com/produit/1514/9782759228164/de-l-analyse-des-reseaux-experimentaux-a-la-meta-analyse
- Initiation à la statistique bayésienne - Bases théoriques et applications en alimentation, environnement, épidémiologie et génétique. Editions Ellipses. https://www.editions-ellipses.fr/
- Analyse statistique des risques agro-environnementaux. Springer. https://link.springer.com/book/10.1007/978-2-8178-0251-0
- Analyse de sensibilité et exploration de modèles. Quae. https://www.quae.com/produit/1179/9782759219070/analyse-de-sensibilite-et-exploration-de-modeles