报告题目：Low-rank Matrix Completion and its New Applications
报告时间：2017年7月31日 周一 14:00—15:00
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.
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 neural networks. 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.