Time:10:00-11:00am, Jun. 19th, 2019, Wednesday
Venue: RoomA1514, Science Building, North Zhongshan Road Campus
Spreker: Professor Annie Qu, University of Illinois at Urbana-Champaign
Abstract: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.
Speaker’s Bio：Annie Qu，教授，博士生导师。本科毕业于复旦大学，博士毕业于美国宾夕法尼亚州立大学。1999—2004年为美国俄勒冈州立大学助理教授，2004—2008年为美国俄勒冈州立大学副教授，2008—2011年为美国伊利诺伊大学香槟分校副教授，2011—至今为美国伊利诺伊大学香槟分校教授。在包括统计学顶级杂志Annals of Statistics, Journal of the American Statistical Association和Biometrika等杂志上发表论文五十余篇。
