Chang Yang

Chang Yang possesses extensive experience in research and engineering, particularly in computational electromagnetics and machine learning. At Purdue University, significant contributions included the development of an O(NlogN) fast direct solver for dense matrix systems. Following this, tenure at Synopsys Inc involved roles as Staff R&D Engineer and Senior R&D Engineer, focusing on silicon aging and uncertainty quantification, with notable achievements such as achieving a 20X speed-up in library generation through machine learning. Currently, Chang Yang works as a Software Engineer in Machine Learning at Meta, specializing in Ads Ranking AI. Prior experience also includes an internship at Cadence Design Systems, where work on the Clarity 3D solver resulted in efficiency improvements in finite element method meshing. Educational qualifications include a Bachelor's degree in Electrical and Computer Engineering from Xi'an Jiaotong University and a PhD in Electrical and Computer Engineering from Purdue University.