This research was conducted through a paid summer undergraduate research experience (REU) with the University of Massachusetts-Amherst in the Department of Electrical and Computer Engineering, funded by the National Science Foundation (NSF). The specific research program I was part of is called Computing for an Equitable Energy Transition, its projects aiming to solve energy transition challenges equitably through computing and analysis.

Throughout the course of this nine-week research experience, I worked independently to download, process, and programmatically analyze and visualize data, leveraging a high performance computing cluster, and Python libraries including Pandas, Geopandas, Shapely, and Matplotlib. I worked closely with a faculty mentor to devise our hypotheses and methodology, as well as with two graduate students on more technical aspects of the project, which developed my fundamental understanding of the research process: how a project takes root, the initial exploration and pursuit of current knowledge on the topic, as well as the curiosity, adaptability, and incentive that is needed in the researcher at every step of the way. My project was not a continuation of research already completed, as some REUs are, but instead the very beginnings of an exploratory project meant to see if a new technological method could be devised to contribute to solving a current energy- and equity-related problem.

 

• To what extent can new metrics in advancing satellite technology be used to identify high-resolution patterns on Earth's surface?
• Can land surface temperature and pollution concentration datasets drawn from advancing satellite technology be used to locate charcoal production sites?

To read more about this project and see the outcomes, click here.