Yichen (Jesse) Feng
About Yichen (Jesse) Feng
Yichen (Jesse) Feng is a Senior MLOps Engineer at Ada, with a strong background in chemical engineering and machine learning. He holds a Master's degree in Chemical Engineering & Applied Chemistry from the University of Toronto and has previously worked in various roles, including as a Machine Learning Engineer and Founder of an algorithmic trading firm.
Work at Ada
Currently, Yichen Feng serves as a Senior MLOps Engineer at Ada, a position he has held since 2022. In this role, he contributes to the development of machine learning operations infrastructure, focusing on enhancing the deployment and monitoring of machine learning models. His work involves integrating data engineering practices with machine learning workflows, which optimizes model performance and ensures efficient operations.
Education and Expertise
Yichen Feng has a solid educational background in data analytics and chemical engineering. He studied at the University of Toronto, where he earned a Master's degree in Management of Enterprise Data Analytics and a Master's degree in Chemical Engineering & Applied Chemistry. Additionally, he holds a Bachelor's degree in Applied Chemistry from China Agricultural University. This diverse educational foundation supports his expertise in machine learning and data engineering.
Background
Yichen Feng began his career as a Research Assistant at China Agricultural University from 2011 to 2012. He later transitioned to various roles in the machine learning field, including a position as a Machine Learning Teaching Assistant at WeCloudData in Toronto from 2016 to 2017. He founded Two Minus Technology, where he worked as an Algorithmic Trading Researcher from 2018 to 2019, before moving on to OPTA Information Intelligence as a Machine Learning Engineer from 2020 to 2022.
Achievements
Throughout his career, Yichen Feng has made significant contributions to machine learning projects, particularly in the development of operational infrastructure. His strong background in chemical engineering informs his unique approach to problem-solving, allowing him to integrate data engineering practices effectively with machine learning workflows. His contributions have led to optimized model performance in various projects.