Professor Mangan’s primary research goal is to connect the speed and automation of top-down data-driven model selection methods with the explanatory power of bottom-up mechanistic modeling. Mechanistic modeling provides a powerful framework for systems design and optimization but relies on the sometimes slow integration of model development and experimental validation. Data science methods promise rapid, automated, descriptions and predictions for complex systems, generally without knowledge of underlying mechanism. Statistics allow us to evaluate how well our models describe a system, given the data. By combining methods, she will rapidly develop explanatory mechanistic models and use them to optimize system performance and design engineering solutions.