Title: Tensor-based Predictive Modeling and Control for High-dimensional Data
Date: May 15, 2026 (Friday)
Time: 10:00 am - 12:00 pm EST
Zoom link:
(Meeting ID: 971 7870 0611; Passcode: 387776)
Zihan Zhang
Ph.D. Candidate in Industrial Engineering
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology
Thesis Committee:
Abstract:
Advances in sensing technologies have dramatically increased the volume and complexity of high-dimensional data, such as high-resolution images and videos, that challenge the foundations of traditional control methodologies. Conventional approaches, rooted in low-dimensional signal processing, often struggle to capture the complex spatio-temporal dependencies inherent in such data. Naive vectorization techniques destroy essential structural information, while many learning-based methods require large datasets and often lack interpretability.
This dissertation develops tensor-based control frameworks that preserve the underlying spatial and temporal structure of high-dimensional data. Chapter 2 addresses incomplete sensing in automatic process control by introducing methods for response imputation and control under missing-data conditions. Chapter 3 presents a system modeling framework that captures localized correlations in system responses and the spatial effects of control actions, followed by a dynamic controller design that optimizes controller placement to improve performance. Chapter 4 incorporates diffusion models to capture nonlinear spatio-temporal correlations and enable uncertainty quantification.
Together, these contributions advance a data-efficient and interpretable framework for controlling intelligent systems that operate with high-dimensional, multimodal sensory data.