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  • 统计与管理学院2018年学术报告第40期

    【主 题】 Individualized Multilayer Tensor Learning with An Application in Imaging Analysis

    【报告人】 Peiyong(Annie)Qu , Professor

               University of Illinois at Urbana-Champaign

    【时 间】 2018年10月15日(星期一)10:00-11:30

    【地 点】 上海财经大学统计与管理学院大楼1208会议室

    摘 要】This work is motivated by multimodality breast cancer imaging data, which is quite challenging in that the signals of discrete tumor-associated microvesicles (TMVs) are randomly distributed with heterogeneous patterns. This imposes a significant challenge for conventional imaging regression and dimension reduction models assuming a homogeneous feature structure. We develop an innovative multilayer tensor learning method to incorporate heterogeneity to a higher-order tensor decomposition and predict disease status effectively through utilizing subject-wise imaging features and multimodality information. Specifically, we construct a multilayer decomposition which leverages an individualized imaging layer in addition to a modality-specific tensor structure. One major advantage of our approach is that we are able to efficiently capture the heterogeneous spatial features of signals that are not characterized by a population structure as well as integrating multimodality information simultaneously. To achieve scalable computing, we develop a new bi-level block improvement algorithm. In theory, we investigate both the algorithm convergence property, tensor signal recovery error bound and asymptotic consistency for prediction model estimation. We also apply the proposed method for simulated and human breast cancer imaging data. Numerical results demonstrate that the proposed method outperforms other existing competing methods.

    嘉宾简介】Dr. Qu is the Professor of Statistics, and the Director of the Illinois Statistics Office at the University of Illinois at Urbana-Champaign. She is a Brad and Karen Smith Professorial Scholar awarded by the College of LAS of UIUC (2017-2020). She received her Ph.D. in Statistics from Penn State in 1998. Her research interests include machine learning, medical imaging, recommender system, natural language processing, personalized medicine, longitudinal/correlated data analysis, missing data, model Selection and nonparametric models.  Dr. Qu received an NSF Career award in 2004-2009. She is a fellow of the Institute of Mathematical Statistics and of the American Statistical Association, and the past Chair of the Statistics Learning and Data Science Section of the American Statistical Association. Previously, she was Assistant and Associate Professor at Oregon State in 1999-2008, and a biostatistician at the Cleveland Clinic Foundation in 1999. She has also held visiting faculty positions in the Department of Biostatistics at the M. D. Anderson Cancer Center in 2004-2005 and at the University of Washington in 2005-2006..