Curriculum Vitae

Ruilizhen (Ray) Hu 胡瑞李蓁

Email: huruilizhen@gmail.com

LinkedIn: linkedin.com/in/ruilizhen-hu

GitHub: github.com/HuRuilizhen

Website: huruilizhen.github.io

Twitter: x.com/RuilizhenH

Telegram: @HuRuilizhen

Ruilizhen Hu

Education

The Chinese University of Hong Kong, Shenzhen CampusAug. 2022 - Jun. 2026

Bachelor of Science in Computer Science and Technology

Shenzhen, Guangdong, China

  • Academic Standing Top 8.5% (11th/129 in MGPA Ranking)
  • Undergraduate Student Teaching Fellowship (USTF)
    • CSC1003 - Introduction to Computer Science and Java Programming (Autumn 2023)
    • CSC1003 - Introduction to Computer Science and Java Programming (Autumn 2024)
    • CSC2003 - Introduction to Java Programming (Spring 2024)
    • CSC3170 - Database System (Autumn 2025)

Nanyang Technological UniversityJan. 2025 - May. 2025

Exchange Program in Computer Science / Electrical and Electronic Engineering

Singapore

Work Experience

The Jianchen Science and Technology Ltd.Jul. 2024 - Jan. 2025

Co-founder & Full Stack Developer

Shenzhen, Guangdong

  • Co-founded a student startup focused on automating text revision workflows for the publishing industry.
  • Contributed to the construction of an AI-assisted editorial correction system, including data acquisition from publishers and preparation of annotated PDF datasets containing correction marks for OCR model fine-tuning.
  • Designed and implemented a task scheduling and orchestration module to support asynchronous and batch processing of large-scale PDF document correction jobs.
  • Developed the company official website and implemented the majority of business-facing front-end and back-end functionalities (excluding algorithm development), enabling end-to-end integration between OCR models and downstream vision workflows.
  • Built front-end interfaces using Vue.js and back-end services with Flask, supporting real-world publisher trials and internal production workflows.

Tencent Holdings Limited – Weixin GroupMay. 2025 - Sep. 2025

Intern, PC Client Developer

Guangzhou, Guangdong

  • Developed cross-platform desktop modules using Qt and C++, interfacing with native Windows and macOS APIs to ensure consistent client behavior.
  • Optimized build and link workflows in a large-scale C++ codebase using Clang and Bazel, contributing to modularization and reducing incremental build latency.
  • Investigated and resolved complex runtime issues in multi-threaded components; improved code stability and debuggability through systematic profiling and debugging practices.

Research Experience

Research on Directed Acyclic Graph Scheduling AlgorithmsNov. 2023 - Jul. 2024

Supervised by Prof. Wenye Li; Experimentation Lead

Shenzhen, Guangdong

  • Studied scheduling algorithms for directed acyclic graphs (DAGs), with a focus on approximate dynamic programming approaches.
  • Led the replication, maintenance, and extension of experimental implementations from prior literature, and conducted large-scale data collection and performance evaluation.
  • Co-authored the paper An Approximate Dynamic Programming Method for Directed Acyclic Graph, accepted by ICONIP 2024 (Auckland, New Zealand) (paper).
  • Served as the primary technical contributor for a patent application on a novel DAG scheduling method and system (CN202410923163.0, patent).
  • Maintained an open-source experimental codebase to support reproducibility and benchmarking (code).

Research on Kolmogorov-Arnold Auto-Encoder for Representation LearningSep. 2024 - Jan. 2025

Supervised by Prof. Wenye Li; Research Contributor (Method & Experiments)

Shenzhen, Guangdong

  • Proposed the Kolmogorov-Arnold Auto-Encoder (KAE), integrating Kolmogorov-Arnold Networks (KAN) with auto-encoders to enhance representation learning.
  • Designed and conducted extensive experiments demonstrating improved latent representation quality, reduced reconstruction error, and superior performance on retrieval, classification, and denoising tasks.
  • Co-authored the paper KAE: Kolmogorov-Arnold Auto-Encoder for Representation Learning (paper).
  • Contributed to the development and public release of an open-source implementation to ensure reproducibility (code).

Publications & Patents

An Approximate Dynamic Programming Method for Directed Acyclic Graph
Yuqi Ma, Ruilizhen Hu, Ruixuan Qi, Jianfeng Mao, and Wenye Li
ICONIP 2024 (Accepted)
KAE: Kolmogorov-Arnold Auto-Encoder for Representation Learning
Fangchen Yu, Ruilizhen Hu, Yidong Lin, Yuqi Ma, Zhenghao Huang, Wenye Li
arXiv preprint (Submitted)
A Novel Scheduling Method and Associated Equipment for Directed Acyclic Graphs
Ruilizhen Hu, Yuqi Ma, Wenye Li, Yungbin Zhao
China National Intellectual Property Administration

Technical Skills

  • Operating Systems: Ubuntu, Mac OS
  • Markup Languages: HTML5, CSS3, Markdown, LaTeX
  • Programming Languages: Java, JavaScript, TypeScript, C++, Python, Rust
  • Development Frameworks: Vue.js, React.js, Next.js, Nest.js, Flask, Django
  • Database Systems: MongoDB, PostgreSQL, MySQL, SQLite
  • Version Control: Git
  • Hosting Tools: Heroku, Alibaba Cloud
  • Testing Tools: Postman
  • Machine Learning: TensorFlow, PyTorch

Awards & Recognition

Bronze Medal, National Olympiad in Informatics (NOI) Aug. 2021

China Computer Federation

Yuyao, Zhejiang, China

Full Admission Scholarship Jun. 2022

Muse College, The Chinese University of Hong Kong, Shenzhen

Shenzhen, Guangdong, China

Silver Medal, China Collegiate Programming Contest (CCPC) Weihai Station Nov. 2022

China Collegiate Programming Contest Committee

Weihai, Shangdong, China

Dean's List, SDS/FE Programme Sep. 2024

School of Data Science, The Chinese University of Hong Kong, Shenzhen

Shenzhen, Guangdong, China

Dean's List, SDS/FE Programme Sep. 2025

School of Data Science, The Chinese University of Hong Kong, Shenzhen

Shenzhen, Guangdong, China

or, if you’re old-school, you can still download the PDF version here!