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Author ORCID Identifier

https://orcid.org/0000-0002-0546-9444

AccessType

Open Access Dissertation

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Electrical and Computer Engineering

Year Degree Awarded

2024

Month Degree Awarded

February

First Advisor

Prof. Jay Taneja

Subject Categories

VLSI and Circuits, Embedded and Hardware Systems

Abstract

Under the umbrella concept of Artificial Intelligence (AI) for good, recent advances in machine learning and large-scale data analysis have opened new opportunities to solve humanity’s most pressing challenges. Improvements in computation complexity and advances in AI (e.g., Vision Transformers) have led to faster and more effective techniques for extracting high-dimensional patterns from large-scale heterogeneous datasets (big data). Further, as satellite data become increasingly available at varying temporal-spatial resolutions, AI tools are helping us to better understand the underlying causes of environmental and socioeconomic changes at an unprecedented scale, ushering in an era of data-driven decision-making to support sustainable and equitable development. Based on these, we propose data-driven methods and techniques for critical infrastructure measurement and sustainable development. Using machine learning and remotely sensed data, we show that we can exploit knowledge and temporal-spatial characteristics learned from data-rich regions to improve data-driven predictions in regions with scant to no data. Specifically, we focus on three critical infrastructures: rivers, roads, and electricity access. Knowledge rivers, particularly their discharge, can help us understand how climate change is evolving, its manifestation on global water resources, and its impact on critical sectors like agriculture and renewable energy generation. On the other hand, better roads facilitate societal development, enabling access to local and global markets and socioeconomic opportunities, leading to better equality in service provision, faster socioeconomic development, and, ultimately, better human outcomes. Finally, we develop tools to support sustainable development, focusing on supporting electricity demand stimulation to improve energy access in rural communities. These methodologies and techniques can help emerging economies achieve their primary sustainable development goals (SDGs) by 2030.

DOI

https://doi.org/10.7275/36332652

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