Health informatics is the multidisciplinary field of study that uses computing and information technology (IT) to collect, store, interpret, and deliver health information to doctors, patients, and consumers. Health informatics applications have historically been limited by the computing and IT capabilities of the time, whether it be equipment cost, size, processing abilities or general availability. However, in the past two decades, personal computers and peripheral equipment overcame these hurdles enabling an abundant amount of Electronic Healthcare (eHealth) applications, such as patient/doctor communication via telecommunication, electronic health record management, emergency information delivery, and medical data transmission. Furthermore, mobile devices such as smartphones and tablets are becoming a dominant computing source in the consumer market with over 1 billion smartphone and tablet sales in 2013 alone. Because of their prevalence, inexpensive costs, processing power, and small form factor, mobile devices are also becoming primary computing devices in Health Informatics, satisfying Mobile Healthcare (mHealth) applications. This thesis discusses 4 mHealth applications and the corresponding tablet-apps developed to handle the health information. Specifically, each tablet-app has a particular focus in either the collection, interpretation, or delivery of health information. The applications of the 4 mHealth apps are: surveying youth in Swaziland, Africa to research the connections between relationship-oriented, psychological development and impoverished environments, pain classification for pain treatment of veterans with spinal cord injuries and disorders (SCI/D), pressure ulcer prevention training for occupational therapists working with veterans with SCI/D, and self-health reporting and information delivery for senior citizens prescribed to anticoagulant medications. These apps aim to yield more extensive surveys and research studies, more standard diagnosis suggestions, less prevalence of pressure ulcers, and healthier senior citizens.

The recent global pandemic triggered by the spread of COVID-19 left the majority of school systems across the United States moving quickly toward remote learning and hybrid models of education. As school buildings closed, many K – 12 school systems adopted a form of whole-school distance learning, leaving students to learn from home and families to support these swift changes. A report released by "Urban District #1" in the Los Angeles area highlighted that Black students remained less active online than their White peers during the early stages of the pandemic. Black students and their families also navigated the country's "racial reckoning" due to the deaths of George Floyd, Breonna Taylor, and other unarmed Black men and women at the hands of the police. This qualitative study was guided by three goals: (1) to understand how Black students and their families experienced distance learning during the COVID-19 pandemic; (2) to examine how schools attempted to engage with Black students and their families during the COVID-19 pandemic, if at all; and (3) to build on the scant literature on the experiences of Black families during the COVID-19 pandemic and the implementation of distance learning. I conducted nine focus groups with 10 students and 20 parents that explored Black middle and high school students and their families' distance learning experiences. The data led to several notable findings. Black students and their families navigated a host of technological, social-emotional, mental health, and academic challenges in the move to implement remote learning. Parents' roles shifted dramatically to support this new normal. Additionally, students and parents felt that new flexibilities, school supports, and new tools were positives to implementing distance learning. Though students and parents identified some bright spots, communication between schools and families remained challenging. Further, I found that due to the coverage of deaths of unarmed Black women and men at the hands of the police and the media attention to subsequent protests, students and parents facilitated more conversations about race, racism, and the role of the police between each other and their peers. The study suggests that in the wake of the pandemic and potential new forms of hybrid education, education systems should invest in integrated supports for Black students and their families and more culturally-relevant, distance learning capacity-building opportunities for school staff. Recommendations based on the findings share directions for future research and implications for institutional change to better support Black students and their families.

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Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver one or many vehicles to high-valued locations to collect data with scientific merit. We consider the use of ocean model predictions to determine the locations to be visited by a team of AUVs, which then provides near-real time, in situ measurements back to the model to increase model skill and the accuracy of future predictions. Iterative application of this procedure determines relevant points of interest that allow the AUV fleet to monitor and track a chosen oceanographic feature. For this study, we select the ocean feature to be a freshwater plume, as their colder, nutrient-rich water promotes productivity, and may result in the formation of a Harmful Algal Bloom (HAB). Monitoring and predicting the formation and evolution of HABs is an area of active research for southern California coastal communities due to their production of harmful toxins that can affect humans and marine wildlife. The movement of the freshwater plumes is predicted by use of the Regional Ocean Modeling System (ROMS) oceanic model applied to our primary area of interest, the Southern California Bight (SCB). Based on the chosen feature, the ROMS prediction, the number of AUVs, each vehicle's operational velocity and the duration of the sampling mission, an algorithm determines waypoints (sampling locations) for the AUV(s) to visit. A trajectory for each vehicle is then generated based on the computed waypoints. We present samples of these trajectories and their implementation results for single and multiple-vehicle experiments that were conducted off the coasts of Los Angeles and Catalina Island. This research represents a first approach to an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks.

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