Qin Yu
Research Software Engineer at The Francis Crick Institute
Stony Stratford, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolQin Yu is a Research Software Engineer with 10 years' experience at the intersection of machine learning, statistics, and high-performance computing for bioimage analysis. Having progressed from a BSc in Computer Science and Mathematics through an MSc in Bioinformatics to doctoral work focused on ML for bioimages, Qin translates cutting-edge research into robust software used in labs. Recent roles at EMBL and the Francis Crick Institute reflect a track record of building and improving image-analysis pipelines, including concrete open-source contributions to a PyTorch 3D U-Net where they fixed metric bugs and optimised AdaptedRandError checks. Qin combines rigorous academic training with practical engineering—able to debug subtle numeric and dimensional issues in deep learning code—while still engaging in documentation and reproducible research practices. Based in Stony Stratford, they bring both domain-specialist insight and systems-level pragmatism to multidisciplinary teams.
10 years of coding experience
1 year of employment as a software developer
GCE A-Level Sciences and Fine Art, GCE A-Level Sciences and Fine Art at Bosworth Independent SchoolMSc Bioinformatics and Theoretical Systems Biology, MSc Bioinformatics and Theoretical Systems Biology at Imperial College LondonDr. rer. nat. Machine Learning for Bioimage Analysis, Dr. rer. nat. Machine Learning for Bioimage Analysis at Heidelberg UniversityUniversity College London
Chinese, English
Stackoverflow
336reputation
34kreached
17answers
0questions
Github Skills (14)
computer-vision10
pytorch10
develop10
python10
meter10
metric10
numpy9
pandas6
docker6
dictionary6
tensorflow6
dataframe6
wandb6
list-comprehension6
Programming languages (8)
Github contributions (5)
wolny/pytorch-3dunet
Nov 2022 - Dec 2022
3D U-Net model for volumetric semantic segmentation written in pytorch
Role in this project:
ML Engineer
ML EngineerContributions:3 reviews, 5 commits, 3 PRs in 13 days
Contributions summary:Qin focused on improving the `AdaptedRandError` metric within the PyTorch 3D U-Net framework. Their contributions included bug fixes related to label checking and dimension handling. They also optimized the performance of the single-value ground truth checker within the `AdaptedRandError` metric. Further improvements included removing duplicate classes and fixing documentation.
semantic-segmentationunet-pytorchresidual-unetu-netvolumetric-data
qin-yu/R-global-financial-crisis
Jun 2018 - Jun 2021
Contributions:15 commits, 12 pushes, 1 branch in 2 years 11 months
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