Babak Naimi
Researcher at Utrecht University
Utrecht, Utrecht, Netherlands
Summary
👤
Senior🎓
Top SchoolBabak Naimi is a senior researcher based in Utrecht with 13 years of expertise in spatial data science, remote sensing, and spatio-temporal/statistical learning applied to ecosystem dynamics and biodiversity. He has a strong academic track record across European institutions (Utrecht, Helsinki, Amsterdam, ETH Zurich, Max Planck) and a PhD in Geo-Information Science, blending deep learning with high-performance, cloud-based workflows for large-scale environmental monitoring. Babak builds reproducible, extensible tools for spatio-temporal modelling and has hands-on experience deploying Dockerized apps and movebank-style movement-data pipelines. His background spans both consulting and academia, giving him a practical edge in translating complex models into operational research products. Colleagues describe him as a method-driven scientist who pairs statistical rigor with scalable engineering to tackle global ecosystem questions.
13 years of coding experience
10 years of employment as a software developer
PhD, Geo-Information Science & Earth Observations, PhD, Geo-Information Science & Earth Observations at University of TwenteBachelor of Science (B.Sc.), Natural Resources, Bachelor of Science (B.Sc.), Natural Resources at Gorgan University of Agricultural Sciences and Natural ResourcesMaster of Science (M.Sc.), Remote Sensing and GIS, Master of Science (M.Sc.), Remote Sensing and GIS at Tarbiat Modares UniversityPhD, Environmental Science, PhD, Environmental Science at IAU University
Stackoverflow
86reputation
858reached
3answers
0questions
Github Skills (30)
r-package9
spatial8
raster8
python8
deep-learning8
machine-learning8
geospatial8
r8
ecology7
geographic7
spatial-data7
vector7
tensorflow7
agriculture6
neural-network6
Programming languages (4)
Github contributions (5)
babaknaimi/sdm
Jul 2015 - Mar 2019
sdm is an extensible framework to develop species distributions models using individual and community-based approaches, generate ensembles of models, evaluate the models, and predict species potential distributions in space and time.
Contributions:18 commits, 2 PRs, 94 pushes in 3 years 8 months
r-forge/rts
Nov 2012 - Aug 2017
Contributions:44 commits in 4 years 10 months
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