一、 报告内容：
Matrix completion aims to recover the missing entries of a partially observed low-rank matrix. It has solid theoretical foundations and has achieved remarkable success on a wide range of domains. In this talk, I will present some of my recent research in this area. Specifically, I will first briefly introduce matrix completion, and then talk about using this technique to solve multiple challenging machine learning problems: (i) how to accurately infer mobile users’ location categories purely based on their highly inaccurate location updates; (ii) how to use static learning models to analyze time series data; and (iii) how to make demand-aware recommendations for trillions of (user, item) pairs.
二、专家介绍：
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姓 名 |
易金峰 |
性 别 |
男 |
国 籍 |
中国 |
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专业技 术职务 |
研究员 |
学 历 |
博士 |
专 业 （学科） |
数据挖掘 |
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工作单位 |
IBM T.J. Watson Research Center |
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主要 社会 兼职 |
无 |
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简历 |
Dr. Jinfeng Yi is currently a Research Staff Member in the AI Foundations Lab at IBM T.J. Watson Research Center, Yorktown Heights, NY, USA. He received his B.E. degree from University of Science and Technology of China in 2009 and Ph.D. degree from Michigan State University in 2014. Dr. Yi's research interests lie in machine learning and its applications to big data analytics. Most recently, he has focused on large-scale matrix and tensor recovery and deep learning. |
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主要 学术 成就 （成果） |
Dr. Yi has published over 20 papers on prestigious machine learning and data mining venues, including ICML, NIPS, KDD, AAAI, ICDM, and Machine Learning Journal. Besides, he holds more than 10 US patents across large-scale data management, privacy preserved data sharing, location context inference, events prediction, feature engineering, spatial-temporal analysis, and customer profile learning. Dr. Yi regularly serves as a program committee member or reviewer for major conferences and journals such as NIPS, IJCAI, AAAI, ICDM, SDM, TPAMI, TKDE, DMKD, and IEEE Transactions on Cybernetics. |
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