Hohyeon Kim

Hohyeon Kim is a graduate student in electrical engineering and computer science merging molecular interactions, device physics, and computational modeling into intelligent chemical sensing systems. Machine olfaction systems require sensor arrays with engineered responses combined with machine learning to generate chemical fingerprints, yet existing approaches lack necessary sensitivity and selectivity. As a step toward this goal, he develops fast, sensitive, and selective nanoelectromechanical hydrogen sensors by modeling device physics and characterizing performance through electrical measurements. Using MATLAB, Hohyeon integrates physical models with experimental measurements, employing the Signal Processing Toolbox to analyze transient responses and extract meaningful features. The Statistics and Machine Learning Toolbox enables evaluation of classification algorithms, while the Curve Fitting Toolbox reveals how response kinetics vary across operating conditions. These MATLAB-centered workflows guide material selection and device geometry optimization. As a MathWorks Fellow, Hohyeon will advance this computational framework toward fully embedded sensing platforms for distributed gas monitoring. His research could demonstrate the feasibility of machine olfaction systems for practical environmental and biological monitoring applications.