Ye Bi
About Ye Bi
Ye Bi is a Data Scientist at Ping An Insurance Group in Shanghai, China, with eight years of experience in computational advertising and intelligence recommendation systems. He holds master's degrees in Electrical and Computer Engineering from Georgia Institute of Technology and in Electrical and Electronics Engineering from Shanghai Jiao Tong University.
Work at PING AN INSURANCE
Ye Bi has been employed as a Data Scientist at Ping An Insurance (Group) Company of China, Ltd. since 2016. Based in Shanghai, he has accumulated eight years of experience in this role. His work primarily focuses on developing advanced data-driven solutions, including a Big Data Artificial Intelligence Recommendation System utilizing Spark and Flink. His contributions have significantly enhanced the company's capabilities in computational advertising and intelligence recommendation systems.
Education and Expertise
Ye Bi holds multiple degrees in engineering. He earned a Master’s Degree in Electrical and Computer Engineering from Georgia Institute of Technology from 2011 to 2014. Prior to that, he completed a Master’s Degree in Electrical and Electronics Engineering at Shanghai Jiao Tong University from 2011 to 2014. He also obtained a Bachelor’s Degree in Electrical, Electronics and Communications Engineering from the same university from 2007 to 2011. His academic background supports his expertise in deep reinforcement learning and online learning, particularly in financial applications.
Background
Before joining Ping An Insurance, Ye Bi worked as a Machine Learning Engineer at China Telecom Corporation Limited from 2014 to 2016. During this period, he developed skills in applying machine learning techniques to real-world problems. Additionally, he interned at Microsoft for two separate summer sessions in 2012 and 2013, where he gained practical experience in the tech industry while working in Shanghai.
Achievements in Data Science
Ye Bi has made significant contributions to the fields of fraud detection and cyber security through the application of big data technologies. His work in these areas has advanced the understanding and implementation of data-driven solutions to mitigate risks and enhance security measures within financial domains.