Zach Wilson is a PhD candidate in chemical engineering at Carnegie Mellon University. In the Sahinidis research group, his research focuses on optimal data-driven model selection and application of discrete optimization to traditional machine learning techniques. Zach contributes to ALAMO, a computational methodology developed in the Sahindis group for the automated learning of algebraic models, develops a tool to facilitate discrete model selection in kinetic reaction networks, and uses integer programming to model and analyze neural networks with rectified linear units. Zach is interested in using algebraic optimization to build accurate models requiring minimal amounts of data that can be used to facilitate process design, decision making, and process analysis.