Eneko Uruñuela is a postdoctoral researcher and AI engineer with nine years of experience applying machine learning to neuroimaging, holding a PhD focused on ML for brain imaging. He builds and deploys deep learning models to predict tissue outcome in stroke and has designed scalable ML pipelines for large neuroimaging datasets on HPC systems. His work spans inverse-problem algorithms, tensor methods, and regularized regression, and he has contributed open-source tools widely used by the neuroimaging community. A seasoned presenter and peer reviewer, he has collaborated with institutions like NIH, Oxford, and Dartmouth and earned recognition for his conference presentations. Based in Calgary, he also engages with the DeSci and Nova communities, blending academic rigor with practical system engineering. An uncommon strength is his track record of moving mathematical inverse-problem solutions from voxel-wise theory to whole-brain, production-ready implementations.
9 years of coding experience
6 years of employment as a software developer
Master's degree Bioinformatics and Data Analysis, Master's degree Bioinformatics and Data Analysis at University of Navarra
UPC Universitat Politècnica de Catalunya
Doctor of Philosophy - PhD Machine learning for neuroimaging, Doctor of Philosophy - PhD Machine learning for neuroimaging at Universidad del País Vasco/Euskal Herriko Unibertsitatea
Contributions:19 reviews, 60 PRs, 307 pushes in 2 years 10 months
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