Her research involves building mathematical models to represent biological systems. The model terms are derived based on data. The goal is to understand what features of the system are important and how they interact. In particular, she has studied systems where both predictable (deterministic) and random (stochastic) processes are crucial to the observed behavior. Thus, she included both types of quantities, resulting in hybrid deterministic-stochastic mathematical models. One key accomplishment in her thesis work was creating an algorithm to solve the hybrid model numerically on the computer. The most interesting aspect of these hybrid models is how the interplay between predictable and random processes affects the behavior of the model and its implication for the system under study – a key theme of her work. Her current interests involve machine learning and the interplay between dynamical modeling and machine learning models.