About

Che-Yuan Liang is a Research Assistant at Academia Sinica in Taiwan with 5.87 years of relevant experience in software engineering, data science, and integration. He holds a Master's degree in Emerging Media from Carnegie Mellon University and a Bachelor of Science in Physics from National Chiao Tung University. In his current role, Che-Yuan has developed a musical onset detection algorithm and improved its performance by 7%, which was published in IEEE Signal Processing Letters in 2015. He has also researched audio signal reconstruction error using constrained (sparsity/non-negativity) least squares. Che-Yuan's past experience includes working as an Engineering Intern at Hyperpilot, where he built an Analyzer backend service for VM instance recommendation and implemented the recommendation searching strategy. He also worked as a Software Engineer at Cloudera, where he worked on general backend development for on-prem Cloudera Data Science Workbench (CDSW) and integrated CDSW SSO with internal identity provider (SAML). Che-Yuan has expertise in a variety of tech stacks, including AWS, Kubernetes, Golang, Docker, Microservices, and Node.js. He is also proficient in front-end development, test, and ML. Che-Yuan's skills in software engineering, data science, and integration make him a valuable asset to any team.