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.

With the rapid increase of technological capabilities, particularly in processing power and speed, the usage of machine learning is becoming increasingly widespread, especially in fields where real-time assessment of complex data is extremely valuable. This surge in popularity of machine learning gives rise to an abundance of potential research and projects on further broadening applications of artificial intelligence. From these opportunities comes the purpose of this thesis. Our work seeks to meaningfully increase our understanding of current capabilities of machine learning and the problems they can solve. One extremely popular application of machine learning is in data prediction, as machines are capable of finding trends that humans often miss. Our effort to this end was to examine the CVE dataset and attempt to predict future entries with Random Forests. The second area of interest lies within the great promise being demonstrated by neural networks in the field of autonomous driving. We sought to understand the research being put out by the most prominent bodies within this field and to implement a model on one of the largest standing datasets, Berkeley DeepDrive 100k. This thesis describes our efforts to build, train, and optimize a Random Forest model on the CVE dataset and a convolutional neural network on the Berkeley DeepDrive 100k dataset. We document these efforts with the goal of growing our knowledge on (and usage of) machine learning in these topics.

Obsessive Compulsive Disorder is a psychiatric disorder that affects 2-3% of children. OCD causes anxiety, fear, upsetting thoughts, and obsessions/compulsions. These symptoms can manifest in different ways and kids can become stuck in a stressful cycle of anxiety and the need to act on compulsions. Currently on the children's book market, OCD is an underrepresented topic. I chose to design a children's book that tackles the stigma of OCD in a form that is easy for children to understand.

Obsessive Compulsive Disorder is a psychiatric disorder that affects 2-3% of children. OCD causes anxiety, fear, upsetting thoughts, and obsessions/compulsions. These symptoms can manifest in different ways and kids can become stuck in a stressful cycle of anxiety and the need to act on compulsions. Currently on the children's book market, OCD is an underrepresented topic. I chose to design a children's book that tackles the stigma of OCD in a form that is easy for children to understand.

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.