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:

https://gatech.zoom.us/j/97178700611?pwd=x3BIcKHMf0trFzXunDRap84ARbL8nx.1&from=addon

(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:

• Dr. Jianjun Shi (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech

• Dr. Kamran Paynabar (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech

• Dr. Yao Xie, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech

• Dr. Xiao Liu, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech

• Dr. Mostafa Reisi, Department of Industrial and Systems Engineering, University of Florida

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.