My interest in computer science started from small but formative moments. In middle school, I used Excel formulas to process grade sheets and realized software could eliminate repetitive work. In high school, I saw a simple batch script automate scheduled shutdown and became curious about how systems execute instructions reliably. Those early experiences pushed me from \"using tools\" to \"building systems.\"
I completed my B.E. in Network Engineering at Hangzhou Dianzi University and built a foundation in operating systems, computer networks, computer organization, and data structures. I then extended that base through cryptography and web engineering: implementing RSA and Diffie-Hellman, practicing secure deployment with Docker and TLS, and developing practical service-oriented projects.
My research and project work focused on secure systems and anomaly detection. I contributed to an OTP-based password manager, a Paillier-homomorphic voting system, and later time-series anomaly detection models. In PGTN, I worked on graph-transformer design, training pipelines, and experiment engineering. I also used Rust extensions (PyO3) to optimize Python-heavy workloads for computational hotspots.
Along the way, I developed a strong engineering bias: reproducible experiments, clean deployment workflows, and robust infrastructure. I rely on Linux-first tooling, automate aggressively, and care about observability and maintainability as much as model quality or feature delivery.
I am currently looking for a DevOps Intern role, with long-term goals in machine learning infrastructure and production AI systems. This site documents what I build, what I learn, and how I iterate on research and engineering in real projects.