Sheng-qi Shen

Mid-level Engineer at Institute of Automation, Chinese Academy of Sciences

Haidian District, Beijing, China
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Summary

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Senior
🎓
Top School
Sheng-qi Shen is a mid-level AI engineer based in Haidian District, Beijing, with about a decade of experience focused on decision making in games using deep reinforcement learning, self-play, FSP and PFSP methods. At the Institute of Automation, Chinese Academy of Sciences he has progressed from internship to mid-level engineer while driving multi-agent and wargame AI research and prototyping. He combines a formal control systems background (Master's in Control Science and Engineering) with practical web development training from Udacity and Free Code Camp, giving him both theoretical depth and implementation versatility. Known as a machine-learning enthusiast on GitHub, he brings repeated hands-on experience turning RL research into robust game-playing agents.
code10 years of coding experience
job3 years of employment as a software developer
bookFull Stack Web Development Certification, Computer Software Engineering, Full Stack Web Development Certification, Computer Software Engineering at Free Code Camp
bookMaster's degree, 控制科学与工程, Master's degree, 控制科学与工程 at 北京化工大学
bookFront-End Web Developer Nanodegree, Front End Nano Degree, Front-End Web Developer Nanodegree, Front End Nano Degree at Udacity
languagesChinese, English
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Github Skills (42)

latent-dirichlet-allocation10
batch-normalization10
mixture-model10
hierarchical-clustering10
mapreduce10
data-structure10
quicksearch10
kd-tree10
face-recognition10
locality-sensitive-hashing10
neural-style-transfer10
randomized-algorithm10
dendrogram10
convolution10
logistic-regression10

Programming languages (5)

TypeScriptJavaHTMLJupyter NotebookPython

Github contributions (5)

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Notebook for quick search
Contributions:7 commits, 6 pushes, 1 branch in 6 months
quicksearchcoursera-coursestanfordalgorithm-specialisation
Contributions:32 commits, 31 pushes, 1 branch in 7 months
quicksearchjupyter-notebookgreedy-algorithmminimum-spanning-treesdynamic-programming
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