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Zhenyu Zhao

Zhenyu Zhao
1 BLOG ARTICLES 3 RESEARCH PAPERS
Zhenyu Zhao is a senior data scientist with Uber's Platform Data Science team.

Engineering Blog Articles

Building an Intelligent Experimentation Platform with Uber Engineering

Composed of a staged rollout and intelligent analytics tool, Uber Engineering's experimentation platform is capable of stably deploying new features at scale across our apps. In this article, we discuss the challenges and opportunities we faced when building this product.

Research Papers

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform

Z. Zhao, R. Anand, M. Wang
In machine learning applications for online product offerings and marketing strategies, there are often hundreds or thousands of features available to build such models. Feature selection is one essential method in such applications for multiple objectives: improving the prediction accuracy by eliminating irrelevant features, accelerating the model training and prediction speed, reducing the monitoring and maintenance workload for feature data pipeline, and providing better model interpretation and diagnosis capability. [...] [PDF]
IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019

Uplift Modeling for Multiple Treatments with Cost Optimization

Z. Zhao, T. Harinen
Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of interventions such as marketing campaigns and product designs. [...] [PDF]
IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019

Safely and Quickly Deploying New Features with a Staged Rollout Framework Using Sequential Test...

Z. Zhao, M. Liu, A. Deb
During the rapid development cycle for Internet products (websites and mobile apps), new features are developed and rolled out to users constantly. Features with code defects or design flaws can cause outages and significant degradation of user experience. The traditional method of code review and change management can be time-consuming and error-prone. In order to make the feature rollout process safe and fast, this paper proposes a methodology for rolling out features in an automated way using an adaptive experimental design. [...] [PDF]
International Conference on Computational Intelligence and Applications, (ICCIA), 2018

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