The prelim scheduling subteam is dedicated to scheduling prelim exams and prioritizes aligning with professor preferences and student needs. The subteam refines schedules based on feedback, historical data, and faculty expectations, ensuring balanced exam timing while adapting to new scheduling challenges.

By analyzing pre-enrollment data, the Forecasting subteam projects post add-drop enrollment trends to help inform the construction of prelim and finals schedules. The subteam works on generating data features and machine learning models to create more accurate predictions.

The Modeling subteam builds and optimizes algorithms for schedule generation. The subteam gathers feedback and creates new models that can be tested and refined over time, with a focus on improving scheduling efficiency, minimizing student conflicts, and maximizing the practicality of exam timings.