Traditionally computing education assumes a Western worldview, excluding non-Western perspectives and requiring assimilation to Western ways of knowing. To overcome this, we propose an Indigenous-centered process for software design and data visualization that decolonizes computing concepts and provides Indigenous students with a culturally relevant and empowering learning experience. Inspired by the traditional canoe-making techniques of the Yurok Tribe and the Eastern Cherokee Tribe, our approach integrates object-oriented programming, UML, and Class Diagrams into a canoe-based software design framework. By empowering Indigenous communities with better access to computing and an improved understanding of data, we foster inclusivity and diversity within the field. Furthermore, our Indigenous-centered process offers a valuable alternative to traditional computing education, no longer enforcing a single Western worldview. To evaluate the effectiveness of our proposed solution, we interviewed Indigenous Environmental Science Graduate students and an Indigenous Professor of Forestry at the University of Minnesota to gather early-stage qualitative feedback on the potential impact of applying our approach in learning computing concepts for integration in Traditional Ecological Knowledge-based coursework. Our work aims to provide Indigenous students with an enhanced ability to work with data while making data visualizations more meaningful. By embracing Indigenous perspectives and cultural practices, we create a new path to learning traditional computing not tied to a Western worldview.

With the rise of greenhouse gases in our atmosphere, it calls for new renewable energy technologies to be created to reduce the air pollution in our atmosphere. One technological advancement that is in early development is the carbon dioxide electrolyzer. A carbon dioxide electrolyzer is a process system that converts carbon dioxide into electricity through the use of an anode, cathode, and membrane. There is not much research done on this technology, as it is still a very new system that is being studied. I am working with Dr. Carlos Morales-Guio at the University of California, Los Angeles, and his group to develop a lab-scaled model of a carbon dioxide electrolyzer. The goal of this project is to create an efficient model by changing the anode, cathode, membrane, and area to maximize the performance of the carbon dioxide electrolyzer. We will 3D design the electrolyzer using Fusion 360 and will be 3D printing all the parts to create the lab-scale prototype. After the project is complete, we will send it to industries to scale-up to use at an industry size and to reduce the greenhouse gases in our atmosphere.

Wildfires occur globally and are increasing in frequency and severity. Wildfires are harmful due to the fire’s destruction and the generated smoke. Smoke consists of numerous air pollutants; however, particulate matter (PM) is a common pollutant that causes short and long-term health effects. PM is harmful due to its small size, enabling it to be easily dispersed. For humans, particulate matter of size 2.5 micrometers or less (PM2.5) can travel deeply into the respiratory tract and cause adverse health effects. The use of low-cost air quality sensors has increased to monitor real-time air quality. Network data analysis can be applied to sensors to assess PM2.5 concentrations. The goal is to determine if network data analysis can detect an extreme air quality event approaching an area equipped with low-cost air quality sensors. The Karuk Tribe reside in Northern California and are afflicted with unhealthy air quality due to wildfires. Low-cost air quality sensors exist throughout the tribe’s region. This research will utilize Python to develop a program that receives real-time data, removes outlier data, and creates spatial and temporal plots. The plots are observed to identify a pattern before periods of unhealthy air quality. Quantitative criteria will be defined based on the observed patterns. The defined criteria will be implemented in the code and tested with alternative historical data for validation. This research will produce a code that receives real-time data, cleans the data, and determines if the real-time air quality forecasts a period of unhealthy air quality.