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Sr. Tech Manager at Meituan-Dianping
I have years of experience in search ranking and ads system, leveraging on skills including machine learning, reinforcement learning, learning to rank, computational advertising, and algorithmic mechanism design. Additionally, I have contributed to and still keep interested in open source projects and public service. As a proactive person, I'm often motivated to improve myself by expanding the knowledge base and putting it into practice. Moreover, I'm keen on studying different languages and eager to experience the cultures around the world.
University of Washington
Machine Learning Specialization offered on Coursera
January 1, 2015 – January 1, 2016
University of Electronic Science and Technology of China
Master of Engineering (MEng), Computer Technology
January 1, 2011 – January 1, 2014
University of Electronic Science and Technology of China
Bachelor of Science (B.Sc.), Computer Science
January 1, 2007 – January 1, 2011
Meituan-Dianping
Sr. Tech Manager
July 1, 2014 – Present
Chaoyang, Beijing
Wolong Data
Software Engineer
February 1, 2013 – March 1, 2014
Chengdu, Sichuan, China
University of Electronic Science and Technology of China
Teaching Assistant
September 1, 2011 – January 1, 2012
Chengdu, Sichuan, China
University of Electronic Science and Technology of China
Research Assistant
October 1, 2010 – January 1, 2013
Chengdu, Sichuan, China
Dreamfly Wireless Studio
Software Engineer
August 1, 2008 – April 1, 2012
Chengdu
Behavior forecasting
March 1, 2017 – Present
Leveraging recurrent neural networks, model time-series behaviors and learn behavior representation, in order to better understand users' intention and boost CTR prediction.
Native Search Ads
January 1, 2016 – Present
Responsible for ranking and auction. Monetized the the search traffic and took it to reach a relatively steady state. Then boosted the RPS(revenue per search) by 54.5% with the bound of user experience. Balancing user experience, advertisers' return and platform's outcome with various auction strategies and a pairwise model developed by my self.
Recommendation Ads
November 1, 2015 – January 1, 2016
Designed and initiated the ranking system of Recommendation Ads in Meituan. Built the online CTR (click-through rate) prediction system, offline training workflow, A/B testing framework and online debugging system.
Subject Ranking
September 1, 2015 – December 1, 2015
Optimised the ranking quality under some specific subjects such as Everyday Bargains, with the CVR (conversion rate) constrained objective of increasing the GMV (gross merchandise volume). Boosted the revenue per thousand UVs (user views) by 35.25% and the conversion rate by 9.54% with a gradient boosting machine developed by ourselves.
User Profile Mining
June 1, 2015 – September 1, 2015
Identified users' preferences for locations based on both GPS and WiFi access points, helping the search team and the recommendation team boost their CVRs(conversion rates), ranging from 0.9% to 2.8%.
Box Office Prediction
January 1, 2015 – June 1, 2015
Predicted the box office in China mostly based on user behaviours on maoyan.com. Also aggregated information from other movie review sites, search engines, and other online movie databases.
Pomodoit
November 1, 2014 – Present
In order to make best use of two task&time management techniques GTD and Pomodoro Technique, this Chrome extension integrates a popular GTD app doit.im with Pomodoro app pomotodo.com. With this extension users are capable of planing effectively and working productively.
Search Trends
November 1, 2014 – September 1, 2015
Personalised the search trends on mobile applications, and then optimised its ranking. Boosted the conversion rate by 63.5% in a year, making it the entrance with the highest conversion rate.
Golang Pool
October 1, 2014 – Present
A tolerant and on-demand pool implementation for golang.
Data Mining for Highly Profitably Potential Customers
November 1, 2013 – January 1, 2014
Leveraging on a Apache Spark Cluster, we work out the potential customers of Group Service for China Mobile(Yunfu,Guangdong Province).
Douban Books
September 1, 2013 – January 1, 2015
An android client for book.douban.com, which is more than goodreads.com. Besdies douban.com is the 4th largest SNS website in China. This client includes some features for books. Douban users can conveniently search or scan a book to view its reviews or notes online. Also users can handily manage their notes and favorites via this client.
Chrome Extension for Shanbay
August 1, 2013 – September 1, 2013
A Google Chrome Extension enhancing some usabilities of shanbay.com, a highly appreciated website helping English Learners in China. Whatever webpage users are browsing, they can easily clip unknown English words and save them into their Shanbay account. Additionally, users can more efficiently memorize their unknown words with the increased shortcuts.
Search Reranking
July 1, 2013 – October 1, 2014
Improved the search ranking quality of www.meituan.com. Relatively increased the top-4 search CVR(Conversion Ratio) by about 4%.
Scala SDK for Douban
March 1, 2013 – August 1, 2013
This adopted Scala SDK for douban.com fully covers the released API, including interfaces for Albums, Books, Bubblers, Comments, Discussions, Doumails, Events, Movies, Music, Notes, Online, Status, Travelling, and Users.
Chance Project
July 1, 2012 – March 1, 2013
Chance is a social app with innovative ways of interaction. In the project Chance, I mainly worked on data modeling and a new feature called Who are you(WRU). Conventional social apps help people find friends nearby through locations based on GPS or base station signals, while they have not solve the problem of matching the virtual ID to the real person. Benefiting from the Doppler Effect, I worked on the feature letting person A match person B to B's Chance ID with a gentle swipe of a smart phone if they are within eyesight.
Machine Learning
University of Washington
June 24, 2026 – Present
Algorithms: Design and Analysis
Coursera
June 24, 2026 – Present
Introduction to Data Science
Coursera
June 24, 2026 – Present
Learning How to Learn: Powerful mental tools
Coursera Course Certificates
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Functional Programming Principles in Scala
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project history shows a strong inclination towards data-intensive problems, machine learning applications, and system optimization, which aligns well with the analytical and problem-solving culture often found in Big Data engineering teams. The breadth of personal projects, from Chrome extensions to social apps and SDKs, indicates a curious and self-driven individual. However, the lack of explicit team collaboration details in project descriptions makes it difficult to fully assess cultural fit in a collaborative environment.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a results-oriented approach, focusing on quantifiable improvements (e.g., 'boosted the RPS by 54.5%', 'increased the top-4 search CVR by about 4%'). This indicates a strong operational fit for roles requiring impact and measurable outcomes. The diverse project portfolio suggests adaptability and a proactive learning attitude, as evidenced by certifications in Machine Learning and Data Science.