Xingcheng Zhou is a PhD candidate whose research focuses on developing simple, accurate, and economical electrochemical diagnostics, which are especially crucial for healthcare in low-resource settings. Supported by her second MathWorks Fellowship, Xingcheng will extend her productive work on electrochemical sensors to diagnose bacterial and viral infectious diseases by conjugating biomolecules on the surface of electrodes to capture disease biomarkers and convert the capture to an electrical signal. Additionally, she aims to use statistical machine learning models to determine if combinations of signal changes through SWV, CV, chronoamperometry, and electrochemical impedance spectroscopy can decrease false negative rates. MathWorks tools are foundational to Xingcheng’s research, and her shared models could be beneficial to fellow researchers in the electrochemistry community. By providing affordable, accessible, and accurate diagnostic tools, Xingcheng’s work may contribute to improved health and health equity in low-resource settings around the world.