Thomas Butruille
Thomas Butrille is a PhD candidate in mechanical engineering whose research interests are focused on architected materials and machine learning. Specifically, Thomas studies fabrication processes for complex micro-fabricated structures and how these structures interact with their environments and then develops machine-learning models to predict the behavior of these structures. As a MathWorks Fellowship, Thomas will pursue several research threads, including investigations of ultralight truss-based architected materials fabricated at the microscale using two-photon lithography. A second area of his work is computational and analytical modeling and scanning electron microscopy to study how different truss-based suspended lattices behave under ultra-high strain rate particle impact. Third, Thomas will help explore how Bayesian optimization can improve the training of a convolutional machine-learning model of the stress- strain behavior of hyperelastic triply periodic minimal surface lattices. Thomas’s research, which relies significantly on MATLAB, is offering valuable new insights and machine-learning methods in microengineering and has the potential to advance next-generation micro-fabricated structures in a wide variety of applications.