Abstract
This thesis, titled “Data-Driven Control and Dynamic Learning of Population Systems using Moment Representations”, presents innovative machine learning techniques for controlling and learning complex biological and computational systems characterized by inherent diversity and dynamics. The work explores a novel reinforcement-based ensemble control strategy for both labeled and unlabeled systems (Chapter 2), and advances moment-based methods for learning both static and dynamic data (Chapter 3), including a new framework for multivariate time series data. A significant portion of the thesis (Chapter 4) is dedicated to the application of these techniques to real-world medical datasets, specifically targeting Monoclonal Gammopathy of Undetermined Significance (MGUS) and Multiple Myeloma (MM). Overall, the thesis offers advanced data handling methods through moment representations and validates their practical utility in not only the control of complex systems, but also the medical domain for machine-learning-assisted medical decision-making.
Committee Chair
Jr-Shin Li
Degree
Doctor of Philosophy (PhD)
Author's Department
Electrical & Systems Engineering
Document Type
Dissertation
Date of Award
Summer 9-14-2023
Language
English (en)
DOI
https://doi.org/10.7936/yzr3-f545
Recommended Citation
Yu, Yao-Chi, "Data-Driven Control and Dynamic Learning of Population Systems using Moment Representations" (2023). McKelvey School of Engineering Theses & Dissertations. 960.
The definitive version is available at https://doi.org/10.7936/yzr3-f545