Chang-Tsan Lu

Chang-Tsan Lu has a diverse work experience in various technical roles. Chang-Tsan started their career as a Graduate Research Assistant at National Taiwan University, where they conducted research on frequency selective surfaces and developed source codes in C++. Chang-Tsan then worked as a Graduate Research Assistant at Carnegie Mellon University, focusing on atomistic studies of twin boundaries in multifunctional materials.

After completing their studies, Chang-Tsan Lu served in the Republic of China Army as a Second Lieutenant Engineering Officer, collaborating on facility renewal projects. Chang-Tsan then joined the University of Massachusetts Amherst as a Postdoctoral Research Associate, where they conducted molecular simulations of polymer-based nanocomposites.

Following their time at the university, Chang-Tsan Lu worked at Third Wave Systems as a Computational Mechanics Engineer, developing and maintaining finite-element-based CAE software and CNC machining toolpath optimization packages. Chang-Tsan then joined InBrace as an Algorithms Engineer, where they enhanced orthodontic treatment CAD software using efficient algorithms.

Currently, Chang-Tsan Lu is working at Autodesk as a Principal Software Engineer. Chang-Tsan is focusing on the Variational Constraint Solver, a software component written in C++ that solves 2D and 3D geometric constraints. Their responsibilities include designing, implementing, testing, and maintaining features related to the geometric constraint solver, as well as researching technologies to enhance its functionality and robustness.

Chang-Tsan Lu has a strong educational background in the field of mechanics and engineering. They hold a Doctor of Philosophy (Ph.D.) degree in Computational Mechanics from Carnegie Mellon University. Prior to that, they earned a Master's degree in Applied Mechanics from National Taiwan University. Their Bachelor's degree is also from National Taiwan University, where they studied Civil Engineering. Additionally, Chang-Tsan Lu has acquired additional certifications in Machine Learning from Coursera, which they obtained in November 2022. They have also completed the NAFEMS Approved Training Course on Composite FEA from NAFEMS in November 2018.