Baoxu Shi

Title: Baoxu Shi: Innovator in Job Posting Technologies

Introduction

Baoxu Shi is a prominent inventor based in San Jose, California, known for his contributions to the field of job posting technologies. With a total of four patents to his name, he has made significant strides in utilizing advanced techniques to enhance the recruitment process.

Latest Patents

One of Baoxu Shi's latest patents is titled "Encoding a job posting as an embedding using a graph neural network." This invention describes techniques for using a graph neural network to encode online job postings as embeddings. The process begins with defining an input graph by processing rules to discover edges connecting nodes that represent job postings or standardized job attributes. The edges are determined by analyzing user activity logs directed at online job postings. A graph neural network (GNN) is then trained based on an edge prediction task, which ultimately derives node embeddings for the job postings in the input graph, including new postings not represented in the original graph.

Another notable patent is the "Machine learned model framework for screening question generation." This invention provides an online screening mechanism that automatically assesses job applicants. Job-specific questions are generated and posed to applicants, allowing for a more reliable measure of their skills based on their answers, which are more recent than the facts contained in their user profiles.

Career Highlights

Baoxu Shi is currently employed at Microsoft Technology Licensing, LLC, where he continues to innovate in the realm of job recruitment technologies. His work focuses on improving the efficiency and effectiveness of the hiring process through advanced machine learning techniques.

Collaborations

Baoxu has collaborated with notable colleagues, including Jaewon Yang and Feng Guo, who share his commitment to advancing technology in the recruitment sector.

Conclusion

Baoxu Shi's innovative work in job posting technologies exemplifies the impact of machine learning on recruitment processes. His patents reflect a deep understanding of both technology and the needs of modern hiring practices.