Tianrun Yan
Software Engineer at Ant Group
Malden, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolTianrun Yan is a software engineer with nine years of practical experience and a current Master's candidate in Computer Engineering at Northeastern University. He blends strong Java and Python expertise with hands-on knowledge of microservices, big data tooling, and cloud-native technologies gained through roles at Ant Group, Nokia, and earlier positions. At Nokia he served as a co-op and has since worked on production-grade systems using Spring Boot, Kafka, Redis, Docker/Kubernetes, and CI/CD pipelines. Beyond backend systems, he contributes to ML projects—most notably adapting and maintaining a DeblurGAN training pipeline—demonstrating an ability to bridge model engineering and software infrastructure. Comfortable across languages from Kotlin and Scala to C++ and Swift, he is passionate about algorithms and continuous learning. Based in Malden, MA, he brings a pragmatic, cross-disciplinary approach to building scalable, maintainable systems.
10 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, Bachelor's degree, Software Engineering at Beijing University of Technology
Github Skills (8)
computer-vision10
pytorch10
deep-learning10
trainings10
python10
generative-adversarial-network10
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.
generative-adversarial-networkdeep-learninggandeblurringimage-processing
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