Nomi Yu

Nomi Yu is a graduate student in mechanical engineering whose research utilizes deep learning techniques to innovate the mechanical design process and to expand AI capabilities in design and manufacturing. As a MathWorks Fellow, Nomi will pursue two primary projects with the objective of creating models of additive manufacturing processes to predict the “manufacturability” of various geometries and incorporating these models in generative design tools. The first project aims to build a differentiable model for manufacturability-rule prediction, incorporating heuristic-based design rules to assess the printability of a given geometry. The second project seeks to produce an empirical model from scanned 3D prints to explore the potential of deep learning models to detect features associated with poor manufacturability that people may have a harder time noticing. Nomi makes extensive use of MATLAB and plans to make valuable contributions to the MATLAB community, including the implementation of 3D deep-learning networks and a centralized toolbox for geometric analysis. Their research could deliver novel techniques and insights with the potential to elevate design and manufacturing through AI and deep learning.