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We are proposing a workshop that instructs K-12 teachers on how to implement fun and engaging projects involving different technologies in their classrooms. These types of projects can be used by faculty to introduce programming skills and other computing concepts, and they are also a fantastic resource for outreach activities. Our students developed these outreach projects in a service learning course as a way to develop and promote their technical skills. We thoroughly documented these projects and developed various tutorial videos and instructions on how to interact with the technologies. We maintain these projects in a searchable online repository that we will introduce during the workshop. We will also share best practices and allow for discussion in utilizing these projects and possibly creating new ones at their institutions. 

Creativity has been shown to improve student learning and retention and is also one of the top skills wanted by employers in computing. However, it is seldom a clear component for most computing courses and is difficult to measure as a course outcome. This paper describes a course specifically designed for student retention with creativity as the central component, and a research study that investigated students’ perspectives on what creativity is, how it pertains to them based on a set of twelve traits, and how this course has impacted their creativity. Students indicated that this course was the most creative course that they have taken and our data shows that it influenced their self-reported creativity. Out of the traits we provided, open-minded and curious were the two selected by our students as top characteristics of creativity. We discuss our observations from this study and provide a qualitative analysis. 

Data valuation is essential for quantifying data’s worth, aiding in assessing data quality and determining fair compensation. While existing data valuation methods have proven effective in evaluating the value of Euclidean data, they face limitations when applied to the increasingly popular graph-structured data. Particularly, graph data valuation introduces unique challenges, primarily stemming from the intricate dependencies among nodes and the growth in value estimation costs. To address the challenging problem of graph data valuation, we put forth an innovative solution, Precedence-Constrained Winter (PC-Winter) Value, to account for the complex graph structure. Furthermore, we develop a variety of strategies to address the computational challenges and enable efficient approximation of PC-Winter. Extensive experiments demonstrate the effectiveness of PC-Winter across diverse datasets and tasks. 

The Technology Ambassador’s Program (TAP) was established in 2012 as an extra-curricular program and has been offered as a service learning course since spring 2016. To investigate the impact on program participants, we launched a longitudinal study in fall 2022 and surveyed the students who completed the course from spring 2016 to spring 2021. Analysis of the survey results discovered that students strongly agree that this program has provided them opportunities to conduct research, to network with other professionals in the field, and apply technical skills. Further analysis also revealed a strong correlation of these opportunities with improving soft skills and career readiness among participants. Overall, this program increased the confidence of the students and prepared them to learn new skills on their own. This paper describes the overall structure of the service learning program and presents the details of this study including the process and survey results. 

Summary Combination antiretroviral therapy (ART) with at least three different drugs has become the standard of care for people with HIV (PWH) due to its exceptional effectiveness in viral suppression. However, many ART drugs have been reported to associate with neuropsychiatric adverse effects including depression, especially when certain genetic polymorphisms exist. Pharmacogenetics is an important consideration for administering combination ART as it may influence drug efficacy and increase risk for neuropsychiatric conditions. Large-scale longitudinal HIV databases provide researchers opportunities to investigate the pharmacogenetics of combination ART in a data-driven manner. However, with more than 30 FDA-approved ART drugs, the interplay between the large number of possible ART drug combinations and genetic polymorphisms imposes statistical modeling challenges. We develop a Bayesian approach to examine the longitudinal effects of combination ART and their interactions with genetic polymorphisms on depressive symptoms in PWH. The proposed method utilizes a Gaussian process with a composite kernel function to capture the longitudinal combination ART effects by directly incorporating individuals’ treatment histories, and a Bayesian classification and regression tree to account for individual heterogeneity. Through both simulation studies and an application to a dataset from the Women’s Interagency HIV Study, we demonstrate the clinical utility of the proposed approach in investigating the pharmacogenetics of combination ART and assisting physicians to make effective individualized treatment decisions that can improve health outcomes for PWH.