Hao Ren
Sr. Software Engineer at LinkedIn
Shanghai, United States
Summary
🤩
Rockstar🎓
Top School任志雄 Ren is a seasoned server engineer with 11 years in the games industry and over 4 years leading development teams, currently based in Shanghai. He has deep C++ server-side expertise—network frameworks, memory optimization, database design—and broad experience across client-side systems (Unity C#, VR, physics, AI and pathfinding). His background includes senior roles at Tencent and multiple major Chinese game studios, where he built cross-platform networking, automated tooling, and publish pipelines. He also contributes to ML-related open source (SRGAN implementation with a CapsuleNet variant), showing an appetite for research-driven techniques beyond traditional game engineering. Known for strong communication with internal teams and external customers, he pairs hands-on coding with code review and tooling in Python and Go. Collected academic training up to doctoral-level CS underpins his pragmatic approach to complex system design.
10 years of coding experience
11 years of employment as a software developer
Bachelor's degree, Electronic Science and Technology, Bachelor's degree, Electronic Science and Technology at Nanjing University of Science and TechnologyMaster's degree, Information Technology, Master's degree, Information Technology at Carnegie Mellon University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (4)
machine-learning10
super-resolution10
computer-vision10
pytorch10
Programming languages (7)
Github contributions (5)
leftthomas/SRGAN
Nov 2017 - Oct 2021
A PyTorch implementation of SRGAN based on CVPR 2017 paper "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
Role in this project:
ML Engineer
ML EngineerContributions:303 commits, 11 PRs, 300 pushes in 3 years 11 months
Contributions summary:Hao contributed to the implementation of a Capsule Network for a super-resolution task. Their commits demonstrate the development of a generator and a discriminator for the SRGAN model, with changes to the CapsuleNet architecture. They also made modifications to loss functions and test procedures, including the integration of metrics like PSNR and SSIM for evaluation of the super-resolution performance.
adversarialsingle-image-super-resolutionrealisticcomputer-visioncvpr-2017
leftthomas/ACNet
Mar 2021 - Feb 2023
Contributions:122 commits, 7 pushes, 4 comments in 1 year 11 months
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