Pengjun Pei

Title: Innovator Spotlight: Pengjun Pei

Introduction

Pengjun Pei is an accomplished inventor based in Santa Clara, CA, known for his significant contributions to the field of machine learning. With two patents to his name, Pei has made strides in online training and content optimization, impacting the technology landscape.

Latest Patents

Pengjun Pei's latest patents demonstrate his innovative approach to machine learning. One of his patents, titled "Efficient Online Training for Machine Learning," introduces a system with prediction workers, training workers, and a parameter server. The prediction workers maintain a local copy of a machine-learned model, operating exclusively in serving mode. In contrast, training workers store a local model and snapshot, using the snapshot to send delta updates back to the parameter server after training. This system enhances the efficiency of real-time updates, which occur much more frequently than full updates.

Another notable patent is "Multi-Stage Ranking Optimization for Selecting Content." This invention outlines a content evaluation pipeline that uses multiple stages to assess the likelihood of user interaction with various content items. By optimizing operational parameters based on compute time and content selection values, this innovation seeks to improve the effectiveness of online content presentation.

Career Highlights

Throughout his career, Pengjun Pei has worked for renowned companies such as Twitter, Inc. and Facebook, Inc. His experiences at these leading technology firms have allowed him to refine his skills in machine learning and content evaluation, further bolstering his expertise as an inventor.

Collaborations

Pei has collaborated with other talented professionals in the field, including Zhiyong Xie and Yue Lu. Their combined efforts in research and innovation have contributed to the advancement of machine learning technologies and applications.

Conclusion

Pengjun Pei is a notable inventor whose patents reflect a commitment to enhancing machine learning processes and content optimization. His work has the potential to influence online systems significantly, making him a key figure in the innovation landscape of technology. With a strong foundation in his career and collaborations, Pei's contributions continue to shape the future of machine learning.