Jonathan Giezendanner is a Senior Researcher and machine learning scientist with 10 years of experience applying deep learning to environmental and geospatial problems. He builds and deploys scalable models—recently Graph Neural Networks for local weather forecasting—after developing CNN-LSTM and Vision Transformer systems for flood mapping and global monitoring. His work spans HPC, cloud pipelines, Google Earth Engine, and collaborations with NASA, IRRI and humanitarian and insurance stakeholders to turn satellite and reanalysis data into operational risk products. Holding a PhD from EPFL and recent postdoctoral experience at MIT, he blends theoretical modeling with practical engineering in Python and PyTorch. Outside academia he co-founded indie game studios, bringing a product mindset and creative prototyping skills to research teams. Based in the Bay Area, he focuses on improving forecast lead times and model interpretability for climate resilience applications.
10 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy, Environmental Science, Doctor of Philosophy, Environmental Science at EPFL
Matura, Economics and Law, Matura, Economics and Law at Gymnasium Alpenstrasse Biel
Unity Odin editor helper which permits to set a "SOVariant" attribute to a ScriptableObject and override, or not, certain fields (similar to prefab variants but for scriptable objects).
Contributions:4 releases, 98 commits, 8 PRs in 9 months
Contributions:1 release, 1 PR, 20 pushes in 1 year 1 month
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Jonathan Giezendanner - Senior Researcher at Early Coffee Games SNC