Zhengxing Chen
Education
- BS, Beijing University of Posts and Telecommunications – China
Pronouns
he/him/his
Biography
Zhengxing Chen is a doctoral student at the Khoury College of Computer Sciences at Northeastern University. His research focuses on personalized recommendation systems for improved player engagement in video games. While Zhengxing’s research approaches largely revolve around machine learning, data mining and artificial intelligence techniques, he has broad interests in churn analysis, behavioral clustering, skill modeling, matchmaking, and virtual team composition.
During his doctoral studies, he has completed internships at Electronic Arts, eBay, Google, and Facebook, where he has used AI techniques to improve real-world user engagement problems. Before joining Northeastern University, Zhengxing earned his undergraduate degree at the Beijing University of Posts and Telecommunications in China in 2013. Within his research, he would like to design effective, efficient, and scalable recommendation systems that can provide players with large-scale in-game element recommendations in real-time.
Education
- BS, Beijing University of Posts and Telecommunications – China
Pronouns
he/him/his
Biography
Zhengxing Chen is a doctoral student at the Khoury College of Computer Sciences at Northeastern University. His research focuses on personalized recommendation systems for improved player engagement in video games. While Zhengxing’s research approaches largely revolve around machine learning, data mining and artificial intelligence techniques, he has broad interests in churn analysis, behavioral clustering, skill modeling, matchmaking, and virtual team composition.
During his doctoral studies, he has completed internships at Electronic Arts, eBay, Google, and Facebook, where he has used AI techniques to improve real-world user engagement problems. Before joining Northeastern University, Zhengxing earned his undergraduate degree at the Beijing University of Posts and Telecommunications in China in 2013. Within his research, he would like to design effective, efficient, and scalable recommendation systems that can provide players with large-scale in-game element recommendations in real-time.