My research expertise is in computational mechanics, materials modeling & design, and machine learning. During my master's study at NTU, I worked on problems in the area of solid mechanics, structural dynamics, and signal processing. During my Ph.D. study at MIT, I focused on understanding the mechanics of deformation and failure of materials at the atomic level. More specifically, I investigated the structure-property relationships of various nanomaterials and biomaterials using atomistic modeling tools including molecular dynamics (MD) and density functional theory (DFT). In 2016, I was deeply inspired by the success of Google DeepMind's AlphaGo program and started to develop machine learning approaches for solving computational mechanics problems. I have published 5 peer-reviewed papers on materials discovery and design using machine learning. I envision that rapid advances in machine learning and artificial intelligence will help solve inverse design problems as well as reduce the computational cost of multiscale modeling and overcome its time-scale and length-scale barriers.
I greatly appreciate the financial support that I have received for my study and research. During my Ph.D. study at MIT, I received financial support via a fellowship from the Taiwanese Government and funding from CRP Henri Tudor in the framework of the BioNanotechnology project. During my postdoctoral training at MIT, I received research funding from the Office of Naval Research (ONR) and the Multidisciplinary University Research Initiative (MURI). During my postdoctoral training at UC Berkeley, I am fully funded by the Department of Mechanical Engineering at UC Berkeley.