Tianrun Yan
Software Engineer at Ant International
Malden, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolTianrun Yan is a software engineer with nine years of hands-on experience delivering distributed, cloud-native and data-driven applications. He is currently a Software Engineer at Ant International and pursuing a Master’s in Computer Engineering at Northeastern University, blending industry practice with advanced study. At Nokia, he architected a monitoring system for 900+ servers, cutting downtime by 25% and accelerating deployments by 15% through Ansible, Terraform, and CI/CD automation, while also automating tests to reduce manual effort. His ML and open-source work includes contributing to DeblurGAN, tuning training pipelines and model components for compatibility. An open-source-minded engineer passionate about microservices, big data, and algorithms, he is committed to continuous learning and delivering scalable, reliable software from backend services to data workflows.
9 years of coding experience
2 years of employment as a software developer
Master's degree, Computer Engineering, Master's degree, Computer Engineering at Northeastern UniversityBachelor's degree, Software Engineering, GPA 3.61/4.0, Bachelor's degree, Software Engineering, GPA 3.61/4.0 at Beijing University of Technology
Github Skills (8)
computer-vision10
pytorch10
deep-learning10
generative-adversarial-networks10
trainings10
python10
modeling10
data-loading9
Programming languages (2)
Github contributions (5)
KupynOrest/DeblurGAN
Mar 2019 - Mar 2019
Image Deblurring using Generative Adversarial Networks
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 1 PR in 9 days
Contributions summary:Tianrun primarily contributed to the project by modifying core model files and training-related scripts. They updated model initialization, loss functions, and data loading components to address version compatibility issues. These changes indicate a focus on adapting and maintaining the model training pipeline for the image deblurring task using GANs. Additionally, the user performed minor adjustments to the web-based result display.
pytorchdeep-learningadversarialdeblurringoptical-flow
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