Santiago Cadena
Machine Learning Engineer at Proxima Fusion
Munich, Bavaria, Germany
Summary
👤
Senior🎓
Top SchoolSantiago Cadena is a machine learning engineer with a decade of experience bridging computational neuroscience and applied ML, currently developing ML-driven design tools for next-generation stellarators at Proxima Fusion in Munich. He holds an MSc and PhD focused on computational neuroscience and machine learning from the University of Tübingen and began his career combining neurophysiology and electronics background from dual bachelor’s degrees in Biomedical and Electronics Engineering. Santiago has moved between research and industry roles—including Meta and Lyft—bringing research-grade modeling, neuromotor interface expertise, and production ML engineering to high-impact problems. His work uniquely blends scientific rigor from academia with product-oriented delivery in fast-moving startups and scale-ups. Colleagues describe him as the kind of engineer who can translate complex physics and neural data into deployable ML solutions that accelerate hardware innovation.
10 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Computational Neuroscience and Machine Learning, Doctor of Philosophy - PhD, Computational Neuroscience and Machine Learning at University of TübingenBachelor's degree, Biomedical/Medical Engineering, Bachelor's degree, Biomedical/Medical Engineering at University of the Andes
English, Spanish, German
Github Skills (36)
system-identification9
transfer-learning8
deep-learning5
mass5
missing-data5
pytorch5
tsne4
tidy4
model-fitting4
neuroscience3
pipeline3
debug3
testing3
machine-learning3
datasets3
Programming languages (2)
Github contributions (5)
sacadena/ptrnets
Jan 2020 - Oct 2022
Collection of pretrained networks in pytorch readily available for transfer learning tasks (e.g. neural system identification)
Contributions:4 releases, 1 review, 58 commits in 2 years 9 months
pytorchdeep-learningtransfer-learningsystem-identificationneural-system-identification
sacadena/nnfabrik
Oct 2020 - Nov 2022
A generalized model fitting pipeline that houses models, trainers, and datasets in datajoint and returns as well as stores trained models.
Contributions:3 pushes, 1 branch in 2 years 1 month
returnspipelinemachine-learningfittingtrainers
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