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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, September 23, 2021 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|Zoom meeting |
+^  **SPEAKER:**|Shaofei Zhao, Binghamton University |
+^  **TITLE:**|Feature selection on tensor response data  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+We have seen several feature selection approaches for ultrahigh
+dimensional data recently. However, most of the approaches are applicable
+for n*1, or at most n*q response variables. If we encounter a more complex
+data structure when the response is tensor-shaped, e.g. human facial shape,
+multi-omics gene data, Electroencephalography (EEG), or  functional
+magnetic resonance imaging (fMRI), current feature selection approaches
+will have difficulty to handle the data. We propose a simple yet useful
+feature selection method that can be applied to tensor response data, and
+show the selection consistency of our method.
+</WRAP>