Computer Science ETDs

Publication Date

Summer 7-14-2025

Abstract

Complex systems are difficult to study because of their many interacting parts, emergent phenomena, and feedback loops. These systems underpin all life on Earth. We need improved tools for seeking an understanding of them. This body of research presents my investigations into data-driven methods for understanding complex systems, including my invention of a novel causal discovery meta-algorithm for space-time gridded data. I demonstrated machine learning feature importance and causal discovery capabilities for comparing simulated and observed climate data. I developed a new benchmark for modeling space-time dynamics of locally driven phenomena and examined a prominent causal discovery algorithm. Finding that contemporary causal discovery struggles with the high-dimensionality of space-time gridded data, I developed CaStLe, a causal discovery meta-algorithm for recovering the space-time evolution of advective phenomena. Finally, I extended CaStLeto recover multivariate space-time dynamics. This research enhances scientists' capabilities to explore and understand complex systems in our universe.

Language

English

Keywords

causal discovery, machine learning, Earth science, climate, volcano

Document Type

Dissertation

Degree Name

Computer Science

Level of Degree

Doctoral

Department Name

Department of Computer Science

First Committee Member (Chair)

Melanie E. Moses

Second Committee Member

G. Matthew Fricke

Third Committee Member

Abdullah Mueen

Fourth Committee Member

Tobias P. Fischer

Fifth Committee Member

Laura P. Swiler

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