Benoit Parmentier is a Data Scientist at SESYNC. My interests pertain broadly to Data Science, GIS/Geospatial Sciences, Remote Sensing and global environmental change with a focus on the development of datasets and spatio-temporal data analysis methods for urban and land change, sustainability, conservation, health and development, and; climate change/variability.
• Data Science tools,Statistics/Modeling Softwares: R, Python, Matlab, SQL, Bash, Keras, Scikit-learn,git/github,markdown, R-markdown, slidify, Shiny, pandoc, Postgres, Jupyterlab, ImageMagick,ffmpeg, Jekyll, Hugo, SPSS, Statistica,Stella. • Project Management: redmine, github. • Programming/Scripting/Others: R, Python, Delphi, Matlab, Pascal, VB/VBA, Javascript (Google Earth Engine), SQL. • Website development: Markdown, Jekyll, Hugo, Shiny, leaflet, blogdown. • High Performance Computing: SLURM, PBS. • OS and DevOps Systems: Unix/Linux (bash), MacOS, Windows, Docker, Vmware, VirtualBox. • GIS/Image processing softwares: ArcGIS 10, PCI Geomatica, IDRISI/TerrSet, ENVI, Definiens-Ecognition, GDAL/OGR, PkTools, R (raster, gstat, sp, sf, spdep, caret etc.), QGIS, GRASS, Postgres/Postgis, PgAdmin, Python (geopanda, rasterio, pysal,shapely,fiona, Scikit-learn), Fragstat.
I have a strong statistical and quantitative background with experience in multivariate empirical methods (such as Mixed Effects Models, Generalized Linear Models, Generalized Additive Models, splines and Principal Component Analysis), geospatial analyses (spatial regression, kriging, GWR), data mining and machine learning methods (neural network, knn, decision tree, random forest, k-means, SVM, fuzzy art map) and time series analyses (ARIMA, FFT, Spectral analyses, wavelets).
PhD Geography, 2012
Clark University
MA Geography, 2010
Clark University
MS/DES Cartography and Remote Sensing, 2004
Université Libre de Bruxelles
BS/Licenciate Geography, 2003
Université Libre de Bruxelles