“You May Not Have Noticed, but Your Neural Network Did: Machine Learning from Simulated Enzyme Variants”
Biography: Tucker Burgin is a postdoctoral researcher in the Department of Chemical Engineering at the University of Washington, as well as a postdoctoral fellow with the eScience Institute. His work focuses on applying machine learning and data science to molecular simulations in order to obtain insights about and intelligently navigate within extremely high-dimensional enzyme design spaces. He holds a Ph.D. in Chemical Engineering from the University of Michigan.
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 the the University of Michigan, Tucker completed a B.S. and M.S. in Biomedical Engineering at the University of Rochester.
In the long term, Tucker hopes that his research will expand the reach of enzyme engineering in order to enhance extant technologies and enable new ones in renewable energy, biopharmaceuticals, and green manufacturing.
