With robots becoming increasingly prevalent in various domains, it has become crucial to equip them with tools to achieve greater fluency in interactions with humans. One of the promising areas for further exploration lies in human trust. A real-time, objective model of human trust could be used to maximize productivity, preserve safety, and mitigate failure. In this work, we attempt to use physiological measures, gaze, and facial expressions to model human trust in a robot partner. We are the first to design an in-person, human-robot supervisory interaction study to create a dedicated trust dataset. Using this dataset, we train machine learning algorithms to identify the objective measures that are most indicative of trust in a robot partner, advancing trust prediction in human-robot interactions. Our findings indicate that a combination of sensor modalities (blood volume pulse, electrodermal activity, skin temperature, and gaze) can enhance the accuracy of detecting human trust in a robot partner. Furthermore, the Extra Trees, Random Forest, and Decision Trees classifiers exhibit consistently better performance in measuring the person's trust in the robot partner. These results lay the groundwork for constructing a real-time trust model for human-robot interaction, which could foster more efficient interactions between humans and robots.