Xiaosen Li
IC Design Engineer at Horizon Robotics
Haidian District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolXiaosen Li is an IC Design Engineer with seven years of experience in ASIC design at Horizon Robotics, blending rigorous analogue and digital IC training from Imperial College London with top‑ranked undergraduate credentials from Beijing Jiaotong University. He applies a strong algorithmic and systems mindset—evidenced by open-source contributions to the Angel ML parameter server where he improved community detection and K-core examples—to bridge machine learning workloads and hardware implementation. Based in Haidian, Beijing, he is comfortable moving between low-level circuit design and higher-level algorithm refinement, making him effective at translating ML research needs into silicon-aware solutions. Colleagues value his detail-oriented approach and knack for spotting subtle bugs in algorithm examples that improve reproducibility and performance.
7 years of coding experience
Master of Science (MSc), Analogue and Digital Integrated Circuit Design, 74.1/100, Master of Science (MSc), Analogue and Digital Integrated Circuit Design, 74.1/100 at Imperial College LondonBachelor of Engineering (B.Eng.), Electronic Science and Technology, 91.9/100,TOP1, Bachelor of Engineering (B.Eng.), Electronic Science and Technology, 91.9/100,TOP1 at Beijing Jiaotong University
Chinese, English
Github Skills (4)
machine-learning10
spark10
scala10
modello9
Programming languages (3)
Github contributions (5)
Angel-ML/angel
May 2020 - Nov 2020
A Flexible and Powerful Parameter Server for large-scale machine learning
Role in this project:
ML Engineer
ML EngineerContributions:144 commits, 137 PRs, 65 pushes in 6 months
Contributions summary:Xiaosen primarily contributes to the local example within the `angel-ml/angel` repository. Their work includes adding and updating local examples, specifically around the LPA (Label Propagation Algorithm) for community detection and KCore algorithms. The commits involve modifying example code, deleting unused parameters, and addressing label-counting bugs, indicating a focus on algorithm implementation and example refinement for the Angel machine learning framework.
parameter-serverdeep-learningmodelmachine-learningonline-learning
xs-li/papers-we-learn
Jun 2020 - Jul 2020
Contributions:30 commits, 30 pushes, 1 branch in 25 days
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