Eduard Trulls
Research Scientist at Google
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolEduard Trulls is a research scientist at Google Zurich with 12 years of experience in computer vision and deep learning, built on a PhD and postdoc background from UPC and EPFL. He specializes in robust image and voxel segmentation pipelines, contributing practical improvements to open-source tools like ilastik to enhance stability and algorithmic compatibility. His work blends academic rigor with production-minded engineering, moving ideas from research prototypes to reliable implementations. Based in Zurich, he combines expertise in robotics, control, and telecommunications with hands-on ML engineering, and maintains an active research feed for the most up-to-date project highlights.
12 years of coding experience
4 years of employment as a software developer
UPC Universitat Politècnica de Catalunya
Spanish, Catalan, English
Github Skills (6)
machine-learning10
il10
python10
numpy9
scikit-image8
qt4
Programming languages (12)
Github contributions (5)
ilastik/ilastik
Oct 2018 - Nov 2018
ilastik-shell, applets, and workflows to string them together.
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 1 PR in 22 days
Contributions summary:Eduard's commits primarily focus on modifications to the `ilastik` codebase, specifically within the `voxelSegmentation` workflow. Their contributions include implementing compatibility fixes, adjusting parameters for the SLIC algorithm, re-enabling the use of pathos for pickling, and refining various components within the workflow. These changes suggest a focus on improving the functionality and stability of the voxel segmentation pipeline, likely to optimize the performance or reliability of the segmentation process.
to-stringpythonstringilastikmachine-learning
ubc-vision/image-matching-benchmark
Feb 2020 - Jan 2022
Public release of the Image Matching Benchmark: https://image-matching-challenge.github.io
Contributions:50 commits, 3 PRs, 36 pushes in 1 year 11 months
feature-matchingrobustnessmatchingbenchmarkimage-matching
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