Moshi Wei

Moshi Wei is a Ph.D. student specializing in machine learning applications on source code, focusing on code generation, API recommendation, and automated bug repair. As a Senior Machine Learning Engineer at LRQA, they developed an Agentic workflow that significantly improved the auditing process, achieving a 91% accuracy rate and saving 82% of processing time. They also have experience as a Generative AI Engineer, creating applications like a Tarot reading chatbot and a resume editing tool. Moshi's research contributions include developing a state-of-the-art API recommendation model and improving existing algorithms in notable projects. They are a co-founder of ISEK, a framework for decentralized agent networks, and they are currently pursuing a Master's degree in Computer Applications at the University of Waterloo.