基本信息
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职业迁徙
个人简介
I am an applied researcher, engineer and technical leader with experience in solving web-scale data mining problems using machine learning and statistical modeling techniques.
• I love to do scientifically-rooted work with practical impact. I am at equal ease doing deep individual work, managing people, leading exploratory R&D projects and driving challenging cross-functional projects involving a lot of uncertainty.
• At Facebook, as an engineer and technical lead in the Ads Ranking and Personalization team, I have led and launched several projects for showing more relevant ads. Most recently, I have been leading the efforts for optimizing Dynamic Product Ads and offsite conversion ads. Before working on ads, I was the sole engineer responsible for a rewrite and several iterations of the Pages You May Like recommendations backend which improved the overall conversions by more than 300% and improved system efficiency (e.g. > 50% latency improvement).
• Before Facebook, I was a founding R&D contributor behind Yahoo’s content optimization and relevance engine. Started as an engineer/applied researcher and later led the applied science team. Deployed advanced algorithms for recommending content across several Yahoo! sites significantly improving engagement, notably more than 270% improvement in click-through rates on the Today Module on Yahoo Front Page and more than 400% improvement in clicks on the Yahoo news pages. Co-authored several patents and papers on this topic, which include a best research paper award on explore-exploit techniques. Received several internal awards which include a Yahoo superstar team and a best paper in Yahoo's internal tech pulse conference.
Specialties:
• Data mining, machine learning, statistical modeling; applications: computational advertising, recommender systems, personalization
• Leading R&D projects, cross-functional efforts
• C/C++, Java; R, Matlab; Python, Perl; Hive, Pig ( Hadoop / map reduce), MySQL
研究兴趣
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semanticscholar(2015)
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COMMUNICATIONS OF THE ACMno. 6 (2013): 92-101
IRpp.485-494, (2012)
KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
San Diego
California
USA
August, 2011pp.132-140, (2011)
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