Summary
Jean Golay is a geospatial data scientist based in Lausanne with eight years of experience applying statistical and machine learning methods to satellite imagery and in-situ environmental data. He develops algorithms for feature selection and intrinsic dimension estimation, and maintains the IDmining R package on CRAN alongside Python implementations on GitHub. His work bridges academic research—publishing in top machine learning, spatial statistics, and statistical physics journals—with practical projects like boosting small-scale CNNs for land-use scene classification. At UNIL he combined research, teaching, thesis supervision, and editorial duties, and now applies that expertise at Engelhart to real-world geospatial problems. He is particularly focused on mining large, high-dimensional datasets efficiently, an area where his tools help reduce model complexity while preserving signal. Fluent in both research rigor and production-ready code, he brings a pragmatic yet innovative approach to geomatics and remote sensing.
8 years of coding experience
5 years of employment as a software developer
Doctor of Science in Environmental Geosciences, Doctor of Science in Environmental Geosciences at University of Lausanne - UNIL
Successful completion of the first year cycle in microengineering, Successful completion of the first year cycle in microengineering at EPFL (École polytechnique fédérale de Lausanne)
English Language and Culture Program, English Language and Culture Program at Simon Fraser University