People spend nearly 90% of their time indoors, where they are exposed to various airborne contaminants. Indoor air quality (IAQ) has a substantial impact on human health and comfort. However, understanding and analyzing IAQ in diverse indoor environments remains challenging despite the well-established principles of mass transfer and fluid dynamics and various low-cost sensing technologies. This is due to the difficulty of collecting key information, such as contaminant generation rate, degree of air mixing, airflow patterns between spaces, etc. This project aims to develop a method for analyzing time series IAQ data using physics-informed machine learning (ML). The method will incorporate mass balance equations, represented by ordinary differential equations, as physical knowledge. A set of probabilistic ML models, regulated by domain knowledge, will address the imperfection of the mass balance equations and the impact of missing key information. Probabilistic programming will serve as the overarching framework to integrate all the model components. The student will work with Professor Jeffrey Siegel (CIVMIN, IAQ expert) and Professor Seungjae Lee (CIVMIN, ML expert in building science). Indoor air quality data collected from multiple homes and other indoor environments will be used to test the developed method.