Tucker is a postdoctoral researcher in the University of Washington department of Chemical Engineering and an eScience Institute postdoctoral fellow. His research focuses on the application of machine learning to molecular simulations of enzymes, and specifically on the ability of neural networks to extract information about key sequence/function relationships from simulation data in order to guide enzyme engineering. Before coming to the University of Washington, Tucker completed his Ph.D. in Chemical Engineering at the University of Michigan, where he developed a software package called ATESA in order to automate and improve the unbiased discovery of rare event mechanisms with transition path sampling. This research was supported by two fellowships with the Molecular Sciences Software Institute (MolSSI). Prior to the University of Michigan, Tucker completed a B.S. and M.S. in Biomedical Engineering at the University of Rochester.