Yunshu Wu is a PhD candidate and graduate student researcher at UC Riverside with seven years of research and engineering experience specializing in generative modeling, particularly diffusion and flow-based methods. They have a track record of turning research into practical systems—from publishing and evaluating LLM-based summarization methods (EACL 2024) to building counterfactual-consistency objectives for diffusion game-world models (ICLR 2026). Yunshu pairs academic depth with hands-on DevOps and backend work, maintaining one-click deployment tooling for networking stacks (Xray/Nginx) and implementing firewall and TCP acceleration features. Comfortable in both experimental ML research and production-focused engineering, they bridge model development, evaluation, and deployment while exploring efficient, cost-aware evaluation pipelines. Based in Los Angeles, Yunshu blends rigorous PhD-level methods with pragmatic scripting and tooling that improve reproducibility and operational robustness.
7 years of coding experience
Bachelor, Computer Science, Bachelor, Computer Science at Northwestern Polytechnical University
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of California, Riverside
Contributions:70 commits, 1 PR, 817 pushes in 2 years
Contributions summary:Yunshu primarily focused on maintaining and improving the one-click install script for Xray, a tool supporting Reality/VLESS WebSocket/gRPC+TLS protocols. Their contributions included modifying file paths, optimizing VLESS configurations, and adding support for VLESS links. The user also implemented features related to firewall configuration and the addition of a TCP acceleration script. Furthermore, they worked on aspects such as installing the Nginx web server.
Contributions:48 commits, 36 PRs, 375 pushes in 1 year 3 months
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