Hao He is a PhD researcher and software engineer with eight years of experience building large-scale, data-driven systems that improve open-source software sustainability and developer productivity. At CMU he applies causal inference and program analysis to measure the effects of dependency strategies and LLM assistants on software quality and velocity, work that earned an FSE’25 Distinguished Paper Award and ICSE publications. His hands-on systems work includes designing Abandabot (an LLM-based dependency recommender) and StarScout, a BigQuery-based fraud detector that processed 20+ TiB of GitHub data and exposed millions of fake stars in production. Previously at Peking University and Huawei he produced high-impact ML pipelines and program analysis tools for recommending “good first issues” and mining library migration patterns. He combines rigorous empirical methods with production-grade engineering, and often tackles problems at repository scale that reveal latent ecosystem behaviors.
8 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD (Transferred to CMU), Computer Software and Theory, 3.81/4.0, Doctor of Philosophy - PhD (Transferred to CMU), Computer Software and Theory, 3.81/4.0 at Peking University
Doctor of Philosophy - PhD, Software Engineering, 4.0/4.0, Doctor of Philosophy - PhD, Software Engineering, 4.0/4.0 at Carnegie Mellon University
Repository for SANER 2021 paper "A Multi-Metric Ranking Approach for Library Migration Recommendations", with all source code, data, evaluation scripts, and a RESTful backend.
Contributions:357 commits, 35 PRs, 69 pushes in 1 year 11 months
Contributions:50 commits, 6 PRs, 40 pushes in 23 days
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