基本信息
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职业迁徙
个人简介
I lead the Machine Learning Algorithms team in Cloud+Enterprise division; we are a part of Microsoft Azure ML. Our learners, predictors and tools are used by many product groups across the company, and we collaborate extensively with MSR and applied ML/data science groups. If you love both ML fundamentals and coding, and would enjoy working on creating state-of-the-art ML systems with a fun group of excellent engineers and scientists, please ping me.
Before that, I was a researcher in the Machine Learning Department at Microsoft Research. I enjoy building ML systems and tools, and working on large-scale prediction problems on behavioral, transactional and textual data. Specific applications on which I focused recently are high-throughput ML, click probability prediction, relevant advertisement selection, constructing user profiles for targeting, and improving search relevance by mining logs of browsing behavior. In the past, I worked on semi-supervised clustering and record linkage (entity resolution, de-duplication, etc.). I am generally interested in adaptive similarity/distance functions, implementing learning algorithms on parallel/distributed platforms, and creating tools for machine learning practitioners.
I completed my Ph.D. in the Department of Computer Science at the University of Texas at Austin in 2006, where I was a member of the Machine Learning Group. Along the way, I spent the summer of 2002 at IBM T.J. Watson Research Center, and the summer/fall of 2004 at Google.
研究兴趣
论文共 10 篇作者统计合作学者相似作者
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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Sydney
NSW
Australia
August, 2015pp.1185-1194, (2015)
IJCAI '13: Proceedings of the Twenty-Third international joint conference on Artificial Intelligencepp.1924-1931, (2013)
引用19浏览0EI引用
19
0
KDDpp.413-421, (2011)
KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Paris
France
June, 2009pp.1165-1173, (2009)
ICMLpp.177-1, (2009)
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrievalpp.705-706, (2008)
IRpp.159-166, (2007)
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