Shu W

Tempe, Arizona, United States
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Summary

👤
Senior
🎓
Top School
Shu Wan is a data-driven laboratory manager and researcher with a decade of experience applying statistical and causal inference methods to real-world marketplace problems. Currently leading operations at ASU’s Data Mining and Machine Learning lab while pursuing a Doctor of Science in Data Science, he bridges academic rigor with production experience. Previously a senior algorithm engineer and tech lead at DiDi, he drove pricing elasticity modeling, heterogeneous treatment effect estimation, and long-term policy evaluation for ride-hailing markets. Shu combines strong foundations in mathematics and statistics (Fudan University; GWU) with hands-on experimental design and decision-making in high-impact commercial settings. He has a track record of translating causal ML research into deployable systems and contributes to communities including DMML-ASU and CausalBench. Colleagues describe him as methodical and pragmatic—equally comfortable with theoretical nuance and messy, large-scale data.
code10 years of coding experience
job5 years of employment as a software developer
bookDoctor of Science, Data Science, Analytics and Engineering, Doctor of Science, Data Science, Analytics and Engineering at Arizona State University
bookBachelor’s Degree, Mathematics and Applied Mathematics, Bachelor’s Degree, Mathematics and Applied Mathematics at Fudan University
bookMaster’s Degree, Statistics, Master’s Degree, Statistics at The George Washington University Columbian College of Arts & Sciences
languagesChinese, English
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Github Skills (30)

algolia10
github-pages10
jekyll10
algolia-search10
jekyll-theme10
computer-vision9
machine-learning8
linux8
r7
javascript7
windows7
shiny7
sparklyr7
dplyr7
macos7

Programming languages (5)

RC++JavaScriptHTMLPython

Github contributions (5)

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Shu-Wan/CrimeAroundYou

Jul 2016 - Feb 2020

An interactive crime map using Shiny
Contributions:15 commits, 2 PRs, 16 pushes in 3 years 7 months
shinycrimerstatsmap
Shu-Wan/zotero-arxiv-daily

Aug 2025 - Jan 2026

Recommend new arxiv papers of your interest daily according to your Zotero libarary.
Contributions:2 reviews, 2 PRs, 5 pushes in 5 months
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