Renzhe Xu
Assistant Professor at Shanghai University of Finance and Economics
Shanghai, Shanghai, China
Summary
👤
Senior🎓
Top SchoolRenzhe Xu is a tenure-track assistant professor at the Institute for Theoretical Computer Science, Shanghai University of Finance and Economics, with a Ph.D. in Computer Science from Tsinghua University. His research bridges theoretical foundations and practical systems, focusing on algorithmic aspects of recommendation, pricing, and fairness informed by internships at Meituan and Tencent. He published work on multi-product ranking (NeurIPS 2022) and studied social welfare effects of regulatory constraints during a visiting stint with Duke’s theory group. Comfortable moving between rigorous theory and industry-scale problems, he brings ten years of experience synthesizing models, proofs, and empirical evaluation. Colleagues note his knack for uncovering subtle welfare or bias trade-offs that are easy to overlook in applied settings. Based in Shanghai, he continues to collaborate internationally and mentor students at the intersection of algorithms and real-world platform design.
10 years of coding experience
High School Diploma, High School Diploma at High School Affiliated to Nanjing Normal UniversityDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Tsinghua UniversityCoursework in Parallel Programming, Computer Science, Coursework in Parallel Programming, Computer Science at National Tsing Hua University
Github Skills (26)
adversarial-learning10
ranking9
react8
computer-science8
science8
fairness-ml8
marp6
discrimination5
vscode5
vscode-extension5
markdown4
visual-studio-code4
slide4
remote-development4
docker4
Programming languages (5)
Github contributions (5)
BabyDriver/NewsClient
Sep 2017 - Jan 2020
Contributions:77 PRs, 77 pushes, 3 branches in 2 years 4 months
windxrz/DCFR
Aug 2020 - Jun 2022
Source code for KDD 2020 paper "Algorithmic Decision Making with Conditional Fairness".
Contributions:11 commits, 5 PRs, 13 pushes in 1 year 9 months
adversarial-learningfairness-aifairness-ml
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