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.

Being prepared to respond to difficult situations that arise in public health practice is an essential skill for the public health workforce.This empathic responding guide was designed to train students, volunteers, and staff of the ASU COVID-19 Case Investigation Team. The guide provides an overview of empathic communication, walks through a framework for responding with empathy, and outlines common difficult situations that arise in public health along with ways to respond with empathy to these situations. This guide can be adapted to a wide variety of settings and is meant to be used as a training tool for public health case investigators and other staff. This guide, available in a full and an abridged version, can be paired with hands-on workshops to provide engaging continuing education opportunities for public health teams.

This communication guide outlines examples of specific situations that are difficult to respond to, and pairs them with examples of how to respond with empathy. This guide depicts these difficult case statements as rows with bold, italic text. Beneath each scenario is an example of an empathic response (underlined) that can lead to a factual response or survey prompt (Figure 1). The responses use empathic communication to show the case that you are witnessing the emotion, rather than moving to the survey without acknowledging emotion. There is no one right answer to any difficult case statement.

Being prepared to respond to difficult situations that arise in public health practice is an essential skill for the public health workforce.This empathic responding guide was designed to train students, volunteers, and staff of the ASU COVID-19 Case Investigation Team. The guide provides an overview of empathic communication, walks through a framework for responding with empathy, and outlines common difficult situations that arise in public health along with ways to respond with empathy to these situations. This guide can be adapted to a wide variety of settings and is meant to be used as a training tool for public health case investigators and other staff. This guide can be paired with hands-on workshops to provide engaging continuing education opportunities for public health teams.