Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Final Installation Showcase

ContributorsHaagen, Jordan (Author) / Turaga, Pavan (Thesis director) / Drummond Otten, Caitlin (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor)
Created2022-05
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Augnosis: Self-Diagnosis in Augmented Reality

Description
Oftentimes, patients struggle to accurately describe their symptoms to medical professionals, which produces erroneous diagnoses, delaying and preventing treatment. My app, Augnosis, will streamline constructive communication between patient and doctor, and allow for more accurate diagnoses. The goal of this project was to create an app capable of gathering data…

Oftentimes, patients struggle to accurately describe their symptoms to medical professionals, which produces erroneous diagnoses, delaying and preventing treatment. My app, Augnosis, will streamline constructive communication between patient and doctor, and allow for more accurate diagnoses. The goal of this project was to create an app capable of gathering data on visual symptoms of facial acne and categorizing it to differentiate between diagnoses using image recognition and identification. “Augnosis”, is a combination of the words “Augmented Reality” and “Self-Diagnosis”, the former being the medium in which it is immersed and the latter detailing its functionality.
ContributorsGoyal, Nandika (Author) / Johnson, Mina (Thesis director) / Bryan, Chris (Committee member) / Turaga, Pavan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
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25 Years of Pokémania: The Evolution of Pokémon

Description
Pokémon is one of the most profitable multimedia franchises of all time, yet few have endeavored to examine how it has reached such a status. The story of Pokémon is not only the story of its many media ventures and the people who create them, but the story of its…

Pokémon is one of the most profitable multimedia franchises of all time, yet few have endeavored to examine how it has reached such a status. The story of Pokémon is not only the story of its many media ventures and the people who create them, but the story of its fans as well. Through a comprehensive analysis of developer interviews, contemporary news articles, fan blogs and forums, and existing scholarly work, this thesis presents the history of the Pokémon franchise and its fandom as never before, emphasizing four main themes of technology, nostalgia, community, and capitalism as key to understanding how Pokémon has become the titan of popular culture that it is today and how its fandom has developed alongside it.
ContributorsFedje, Kylan (Author) / Boyce-Jacino, Katherine (Thesis director) / Ingram-Waters, Mary (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor)
Created2022-05
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COVID-19 Vaccination Hesitancy Among Pregnant People in Arizona

Description

With recent reports indicating that there is a relatively low number of pregnant people vaccinated against COVID-19 in the United States (~30% per the Centers for Disease Control and Prevention, October, 2021), this study aims to understand the reasons for COVID-19 vaccine hesitancy among the pregnant population in the state…

With recent reports indicating that there is a relatively low number of pregnant people vaccinated against COVID-19 in the United States (~30% per the Centers for Disease Control and Prevention, October, 2021), this study aims to understand the reasons for COVID-19 vaccine hesitancy among the pregnant population in the state of Arizona. Using a mixed-methods approach, this cross-sectional study employs both semi-structured qualitative interviews (n = 40) and a quantitative survey instrument (n = 400) to better understand the reasons for COVID-19 vaccine hesitancy among pregnant people, with data collected over the course of a few months. Descriptive statistics and logistic regression are employed to analyze the quantitative data and the semi-structured interviews are inductively coded to analyze themes across participant interviews. The results from this study are not only able to help better address disparities in COVID-19 vaccinations among pregnant people, but they also provide implications for vaccine hesitancy overall in order to develop interventions to address vaccine hesitancy. Future research is warranted to better understand regional differences in vaccine hesitancy and differences across populations.
ContributorsGamboa, Jazmin (Author) / Hernandez Salinas, Christopher (Co-author) / Perez, Valeria (Co-author) / Lopez, Gilberto (Thesis director) / Ingram-Waters, Mary (Committee member) / Barrett, The Honors College (Contributor) / School of Transborder Studies (Contributor) / School of Social Transformation (Contributor) / Watts College of Public Service & Community Solut (Contributor)
Created2022-05

The Transfer Experience: Overcoming Barriers And Paving The Path To Success

Description

Students who transfer to a university from a community college are a diverse, resilient group of individuals who often face many challenges and barriers upon transitioning from a 2-year institution to a 4-year institution. Due to their upper-division status upon arrival at the university, transfer students are often overlooked and…

Students who transfer to a university from a community college are a diverse, resilient group of individuals who often face many challenges and barriers upon transitioning from a 2-year institution to a 4-year institution. Due to their upper-division status upon arrival at the university, transfer students are often overlooked and even unsupported throughout multiple aspects of the transfer process. To further understand the issues that are faced by transfer students throughout the transfer process, we conducted research to get a better understanding of exactly who transfer students are, what challenges they face, and how universities can better support these students so they are able to complete their baccalaureate. We compiled this research into an annotated bibliography and developed a presentation to discuss our findings, personal anecdotes, and the suggestions we have to help Barrett, the Honors College move towards a more transfer-receptive culture. All questions asked during the presentation have been documented.
ContributorsAutote, Abreanna (Author) / Loera, Cristian Peter (Co-author) / Ingram-Waters, Mary (Thesis director) / Abril, Lauren (Committee member) / Hugh Downs School of Human Communication (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05

The Transfer Experience: Overcoming Barriers And Paving The Path To Success

Description

Students who transfer to a university from a community college are a diverse, resilient group of individuals who often face many challenges and barriers upon transitioning from a 2-year institution to a 4-year institution. Due to their upper-division status upon arrival at the university, transfer students are often overlooked and…

Students who transfer to a university from a community college are a diverse, resilient group of individuals who often face many challenges and barriers upon transitioning from a 2-year institution to a 4-year institution. Due to their upper-division status upon arrival at the university, transfer students are often overlooked and even unsupported throughout multiple aspects of the transfer process. To further understand the issues that are faced by transfer students throughout the transfer process, we conducted research to get a better understanding of exactly who transfer students are, what challenges they face, and how universities can better support these students so they are able to complete their baccalaureate. We compiled this research into an annotated bibliography and developed a presentation to discuss our findings, personal anecdotes, and the suggestions we have to help Barrett, the Honors College move towards a more transfer-receptive culture. All questions asked during the presentation have been documented.
ContributorsLoera, Cristian Peter (Author) / Autote, Aubreanna (Co-author) / Ingram-Waters, Mary (Thesis director) / Abril, Lauren (Committee member) / Hugh Downs School of Human Communication (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Knowledge Distillation with Geometric Approaches for Multimodal Data Analysis

Description
This thesis presents robust and novel solutions using knowledge distillation with geometric approaches and multimodal data that can address the current challenges in deep learning, providing a comprehensive understanding of the learning process involved in knowledge distillation. Deep learning has attained significant success in various applications, such as health and…

This thesis presents robust and novel solutions using knowledge distillation with geometric approaches and multimodal data that can address the current challenges in deep learning, providing a comprehensive understanding of the learning process involved in knowledge distillation. Deep learning has attained significant success in various applications, such as health and wellness promotion, smart homes, and intelligent surveillance. In general, stacking more layers or increasing the number of trainable parameters causes deep networks to exhibit improved performance. However, this causes the model to become large, resulting in an additional need for computing and power resources for training, storage, and deployment. These are the core challenges in incorporating such models into small devices with limited power and computational resources. In this thesis, robust solutions aimed at addressing the aforementioned challenges are presented. These proposed methodologies and algorithmic contributions enhance the performance and efficiency of deep learning models. The thesis encompasses a comprehensive exploration of knowledge distillation, an approach that holds promise for creating compact models from high-capacity ones, while preserving their performance. This exploration covers diverse datasets, including both time series and image data, shedding light on the pivotal role of augmentation methods in knowledge distillation. The effects of these methods are rigorously examined through empirical experiments. Furthermore, the study within this thesis delves into the efficient utilization of features derived from two different teacher models, each trained on dissimilar data representations, including time-series and image data. Through these investigations, I present novel approaches to knowledge distillation, leveraging geometric techniques for the analysis of multimodal data. These solutions not only address real-world challenges but also offer valuable insights and recommendations for modeling in new applications.
ContributorsJeon, Eunsom (Author) / Turaga, Pavan (Thesis advisor) / Li, Baoxin (Committee member) / Lee, Hyunglae (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2023
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Robust and Controllable Generative Models by Leveraging Physics-Based, Probabilistic, and Geometric Methods

Description
Generative models are deep neural network-based models trained to learn the underlying distribution of a dataset. Once trained, these models can be used to sample novel data points from this distribution. Their impressive capabilities have been manifested in various generative tasks, encompassing areas like image-to-image translation, style transfer, image editing,…

Generative models are deep neural network-based models trained to learn the underlying distribution of a dataset. Once trained, these models can be used to sample novel data points from this distribution. Their impressive capabilities have been manifested in various generative tasks, encompassing areas like image-to-image translation, style transfer, image editing, and more. One notable application of generative models is data augmentation, aimed at expanding and diversifying the training dataset to augment the performance of deep learning models for a downstream task. Generative models can be used to create new samples similar to the original data but with different variations and properties that are difficult to capture with traditional data augmentation techniques. However, the quality, diversity, and controllability of the shape and structure of the generated samples from these models are often directly proportional to the size and diversity of the training dataset. A more extensive and diverse training dataset allows the generative model to capture overall structures present in the data and generate more diverse and realistic-looking samples. In this dissertation, I present innovative methods designed to enhance the robustness and controllability of generative models, drawing upon physics-based, probabilistic, and geometric techniques. These methods help improve the generalization and controllability of the generative model without necessarily relying on large training datasets. I enhance the robustness of generative models by integrating classical geometric moments for shape awareness and minimizing trainable parameters. Additionally, I employ non-parametric priors for the generative model's latent space through basic probability and optimization methods to improve the fidelity of interpolated images. I adopt a hybrid approach to address domain-specific challenges with limited data and controllability, combining physics-based rendering with generative models for more realistic results. These approaches are particularly relevant in industrial settings, where the training datasets are small and class imbalance is common. Through extensive experiments on various datasets, I demonstrate the effectiveness of the proposed methods over conventional approaches.
ContributorsSingh, Rajhans (Author) / Turaga, Pavan (Thesis advisor) / Jayasuriya, Suren (Committee member) / Berisha, Visar (Committee member) / Fazli, Pooyan (Committee member) / Arizona State University (Publisher)
Created2023
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Hardware-Software Co-design for Light Transport Acquisition and Adaptive Non-Line-of-Sight Imaging

Description
In the rapidly evolving field of computer vision, propelled by advancements in deeplearning, the integration of hardware-software co-design has become crucial to overcome the limitations of traditional imaging systems. This dissertation explores the integration of hardware-software co-design in computational imaging, particularly in light transport acquisition and Non-Line-of-Sight (NLOS) imaging. By leveraging projector-camera systems and…

In the rapidly evolving field of computer vision, propelled by advancements in deeplearning, the integration of hardware-software co-design has become crucial to overcome the limitations of traditional imaging systems. This dissertation explores the integration of hardware-software co-design in computational imaging, particularly in light transport acquisition and Non-Line-of-Sight (NLOS) imaging. By leveraging projector-camera systems and computational techniques, this thesis address critical challenges in imaging complex environments, such as adverse weather conditions, low-light scenarios, and the imaging of reflective or transparent objects. The first contribution in this thesis is the theory, design, and implementation of a slope disparity gating system, which is a vertically aligned configuration of a synchronized raster scanning projector and rolling-shutter camera, facilitating selective imaging through disparity-based triangulation. This system introduces a novel, hardware-oriented approach to selective imaging, circumventing the limitations of post-capture processing. The second contribution of this thesis is the realization of two innovative approaches for spotlight optimization to improve localization and tracking for NLOS imaging. The first approach utilizes radiosity-based optimization to improve 3D localization and object identification for small-scale laboratory settings. The second approach introduces a learningbased illumination network along with a differentiable renderer and NLOS estimation network to optimize human 2D localization and activity recognition. This approach is validated on a large, room-scale scene with complex line-of-sight geometries and occluders. The third contribution of this thesis is an attention-based neural network for passive NLOS settings where there is no controllable illumination. The thesis demonstrates realtime, dynamic NLOS human tracking where the camera is moving on a mobile robotic platform. In addition, this thesis contains an appendix featuring temporally consistent relighting for portrait videos with applications in computer graphics and vision.
ContributorsChandran, Sreenithy (Author) / Jayasuriya, Suren (Thesis advisor) / Turaga, Pavan (Committee member) / Dasarathy, Gautam (Committee member) / Kubo, Hiroyuki (Committee member) / Arizona State University (Publisher)
Created2024
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Responsible Machine Learning: Security, Robustness, and Causality

Description
In the age of artificial intelligence, Machine Learning (ML) has become a pervasive force, impacting countless aspects of our lives. As ML’s influence expands, concerns about its reliability and trustworthiness have intensified, with security and robustness emerging as significant challenges. For instance, it has been demonstrated that slight perturbations to…

In the age of artificial intelligence, Machine Learning (ML) has become a pervasive force, impacting countless aspects of our lives. As ML’s influence expands, concerns about its reliability and trustworthiness have intensified, with security and robustness emerging as significant challenges. For instance, it has been demonstrated that slight perturbations to a stop sign can cause ML classifiers to misidentify it as a speed limit sign, raising concerns about whether ML algorithms are suitable for real-world deployments. To tackle these issues, Responsible Machine Learning (Responsible ML) has emerged with a clear mission: to develop secure and robust ML algorithms. This dissertation aims to develop Responsible Machine Learning algorithms under real-world constraints. Specifically, recognizing the role of adversarial attacks in exposing security vulnerabilities and robustifying the ML methods, it lays down the foundation of Responsible ML by outlining a novel taxonomy of adversarial attacks within real-world settings, categorizing them into black-box target-specific, and target-agnostic attacks. Subsequently, it proposes potent adversarial attacks in each category, aiming to obtain effectiveness and efficiency. Transcending conventional boundaries, it then introduces the notion of causality into Responsible ML (a.k.a., Causal Responsible ML), presenting the causal adversarial attack. This represents the first principled framework to explain the transferability of adversarial attacks to unknown models by identifying their common source of vulnerabilities, thereby exposing the pinnacle of threat and vulnerability: conducting successful attacks on any model with no prior knowledge. Finally, acknowledging the surge of Generative AI, this dissertation explores Responsible ML for Generative AI. It introduces a novel adversarial attack that unveils their adversarial vulnerabilities and devises a strong defense mechanism to bolster the models’ robustness against potential attacks.
ContributorsMoraffah, Raha (Author) / Liu, Huan (Thesis advisor) / Yang, Yezhou (Committee member) / Xiao, Chaowei (Committee member) / Turaga, Pavan (Committee member) / Carley, Kathleen (Committee member) / Arizona State University (Publisher)
Created2024