Devon Elizabeth Campbell

Devon began her work in STEM research at a Vanderbilt University Mechanical Engineering lab studying desalination. She explored sustainable development by analyzing the nanomolecular structure of materials for electrochemical desalination. In a project entitled “Electrochemical Evaluation of Novel Nanoscale Materials for Water Desalination,” she used a three-electrode electrochemical system to determine how electrode architecture can optimize ion adsorption in the desalination process. Her work won first place at the state level of the Stockholm Junior Water Prize and advanced to the national competition.

This research on desalination technology educated her on the breadth of water scarcity and the distance from concrete applications of novel desalination technologies. This awareness then inspired her to tailor a localized, impact-focused solution to the needs of at-risk communities. As a result, Devon independently designed a prototype for a portable pressurized chamber that facilitates reverse osmosis. After creating a 3D model of this product using the CAD software, Blender, she researched the fundamentals of patent law to gain the necessary knowledge to independently author and file a provisional patent application. In doing so, she formally articulated the mechanics of her invention, which has now earned patent-pending status.

Additionally, she interned for three weeks at the Children’s National Hospital in the Center for Genetic Medicine as a volunteer researcher. She worked on a project regarding amino acids in which she used a high-performance liquid chromatography (HPLC) machine to calculate the concentration of given amino acids, thus providing biomarkers for medical conditions like sickle cell anemia and phenylketonuria.

Most recently, within the field of Computer Science, Devon was motivated by the detrimental economic impacts of cyberattacks to conduct independent cybersecurity research. By creating a machine-learning algorithm to identify the presence of malware, she tested the effectiveness of deep neural networks on data extracted from deep packet inspection for the detection of malicious software. After designing a novel process of analyzing network traffic data, her model performed at a competitive rate with published literature and illustrated innovative conclusions regarding deep learning’s cybersecurity capabilities. This work earned her a National Honorable Mention by the National Center for Women & Information Technology.

At Columbia, Devon hopes to continue studying the intersection of Mechanical Engineering and Computer Science, specifically as a means of exploring sustainable development and technology.