Shuai You

Topic

Predictive modelling, Anomaly Detection, and Empirical Extraction of Variation Patterns within Longitudinal Data

Department of Mathematics and Statistics

Date & location

• Wednesday, March 13, 2024
• 2:30 P.M.
• Virtual Defence

Examining Committee

Supervisory Committee

• Dr. Mary Lesperance, Department of Mathematics and Statistics, University of Victoria (Co-Supervisor)
• Dr. Xuekui Zhang, Department of Mathematics and Statistics, UVic (Co-Supervisor)
  Dr. Youlian Pan, Department of Mathematics and Statistics, UVic (Member)
  Dr. Tao Lu, Department of Electrical and Computer Engineering, UVic (Outside Member)

External Examiner

• Dr. Depeng Jiang, Department of Community Health Sciences, University of Manitoba

Chair of Oral Examination

• Dr. Jamie Lawson, Department of Political Science, UVic

Abstract

This thesis represents a comprehensive exploration and enhancement of statistical methodologies, addressing complex challenges in transdisciplinary data analysis. It integrates novel techniques across various domains to bridge gaps in existing algorithms, focusing on advancing multitask prediction and anomaly detection. Significant contributions involve extending stacking algorithms for survival and longitudinal data prediction, a generalizable workflow, and an innovative unsupervised learning algorithm for real-time spectral anomaly identification and classification. This thesis emphasizes the collective contributions of these interconnected research findings, aiming to advance predictive modeling methodologies and anomaly detection across disciplines.