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In 2016 the Hispanic enrollment in computer science and computer engineering for both undergraduate and graduate students at Texas A&M University initially sat at 17.9% and has decreased to approximately 11.76% in 2021, with undergraduate Hispanic enrollment in computing reducing from almost 22% down to under 15% in that same time frame[1]. This significant shift in Hispanic student representation spurred the development of this organization, Aggie Hispanics In Computing (AHIC), to create a computing community and provide support focused around the shared experiences of being part of a minority group at a predominately white institution (PWI) in an even less diverse discipline. This organization is not a lone member of Hispanic serving organizations at Texas A&M University, overall considered a Hispanic serving institution (HSI), rather it was designed to focus particularly on serving Hispanic students in the computer science and computer engineering disciplines at Texas A&M University. Since the organization was founded during the COVID-19 pandemic in 2020, AHIC has grown significantly in membership, financial support, and goal attainment focused on increasing representation of Hispanic students within the computing disciplines at Texas A&M University. The organization has grown from 6 to over 50 members from various disciplines in the past year alone. AHIC has also received financial support from a multitude of companies such as General Motors, Chevron, and others. The overall goal of AHIC is to create a supportive community for minorities in various computing fields. This community has been grown through hosting supporting events that provide information and resources about university research, professional career opportunities, workshops, and mentorship programs. AHIC has also initiated several long-term initiatives such as peer teaching for introductory computer science courses in the past year. We have focused on company panels and alumni coaching in which company representatives and alumni provide career advice for currently enrolled students. The organization has also hosted seminars and workshops educating freshmen on new computing skills and opportunities that a computer science and computer engineering degree can provide. This paper will discuss the need recognized for a minority focused and serving computing organization and how the formation of Aggie Hispanics In Computing provides a community that is promising for the future of minorities in the computing field at Texas A&M University. 

Many Internet of Things (IoT) applications are time-critical and dynamically changing. However, traditional data processing systems (e.g., stream processing systems, cloud-based IoT data processing systems, wide-area data analytics systems) are not well-suited for these IoT applications. These systems often do not scale well with a large number of concurrently running IoT applications, do not support low-latency processing under limited computing resources, and do not adapt to the level of heterogeneity and dynamicity commonly present at edge environments. This suggests a need for a new edge stream processing system that advances the stream processing paradigm to achieve efficiency and flexibility under the constraints presented by edge computing architectures. We present \textsc{Dart}, a scalable and adaptive edge stream processing engine that enables fast processing of a large number of concurrent running IoT applications’ queries in dynamic edge environments. The novelty of our work is the introduction of a dynamic dataflow abstraction by leveraging distributed hash table (DHT) based peer-to-peer (P2P) overlay networks, which can automatically place, chain, and scale stream operators to reduce query latency, adapt to edge dynamics, and recover from failures. We show analytically and empirically that DART outperforms Storm and EdgeWise on query latency and significantly improves scalability and adaptability when processing a large number of real-world IoT stream applications' queries. DART significantly reduces application deployment setup times, becoming the first streaming engine to support DevOps for IoT applications on edge platforms.