Anastasia Yasakova
Software Engineer at Huawei
Russia
Summary
🤩
Rockstar🎓
Top SchoolAnastasia Yasakova is a software engineer with seven years of experience building reliable backend systems and test automation, currently working at Huawei after contributing to CVAT.AI’s industry-leading annotation platform. She focused on dataset import/export robustness and COCO skeleton support for cvat-ai/cvat, pairing feature work with test development to raise annotation quality and stability. Her background includes internships and engineering roles at Intel and a master’s in Information Technology from Nizhny Novgorod State University. Comfortable in complex data-processing domains, she brings a pragmatic mix of engineering discipline and open-source collaboration. A detail-oriented problem solver, she often surfaces subtle organization-specific data issues and resolves them through both code and automated tests.
7 years of coding experience
3 years of employment as a software developer
Степень магистра, Information Technology, Степень магистра, Information Technology at Нижегородский Государственный Университет им. Н.И. Лобачевского (ННГУ)
Github Skills (13)
testing10
datasets10
pytest10
python10
data-management10
back-end-development10
django10
data-set10
coco9
rest-api9
computer-vision9
tensorflow8
pytorch8
Programming languages (4)
Github contributions (5)
cvat-ai/cvat
May 2022 - Jan 2023
Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:95 reviews, 32 commits, 66 PRs in 8 months
Contributions summary:Anastasia primarily contributed to improving the CVAT's backend functionality, with a focus on dataset import and export features. Their work included fixing dataset import issues related to organization-specific configurations and extending support for COCO format skeletons. Furthermore, the user developed and integrated several tests to ensure the stability and correctness of the annotation processes, including testing dataset import, export, and data processing with skeletons. These changes indicate a strong focus on improving data handling and ensuring annotation quality.
semantic-segmentationcvatannotation-toolimage-classificationtensorflow
yasakova-anastasia/pp_2020_spring
Mar 2020 - Dec 2020
Contributions:25 PRs, 29 pushes, 9 branches in 9 months
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