Beijing, China

Qi Wen

USPTO Granted Patents = 1 

Average Co-Inventor Count = 7.0

ph-index = 1


Company Filing History:


Years Active: 2023

where 'Filed Patents' based on already Granted Patents

1 patent (USPTO):

Title: The Innovative Contributions of Qi Wen

Introduction

Qi Wen is a notable inventor based in Beijing, China. He has made significant contributions to the field of computer science, particularly in the area of machine learning and optimization. His work focuses on enhancing the efficiency of ensemble decision tree models through innovative coding techniques.

Latest Patents

Qi Wen holds a patent for a groundbreaking invention titled "Generating native code with dynamic reoptimization for ensemble tree model prediction." This invention involves a computer-implemented method that receives an ensemble decision tree and generates native code from it. The method compiles the native code into machine language and evaluates its execution time. Furthermore, it dynamically reoptimizes portions of the native code that correspond to the most traversed sections of the ensemble decision tree. This patent showcases his expertise in improving computational efficiency and performance.

Career Highlights

Qi Wen is currently employed at International Business Machines Corporation, commonly known as IBM. His role at IBM allows him to work on cutting-edge technologies and contribute to advancements in artificial intelligence and machine learning. His innovative approach has positioned him as a valuable asset in the tech industry.

Collaborations

Throughout his career, Qi Wen has collaborated with esteemed colleagues such as Jean-François Puget and Ke Wei Wei. These collaborations have fostered a creative environment that encourages the exchange of ideas and the development of innovative solutions.

Conclusion

In summary, Qi Wen is a distinguished inventor whose work in generating native code for ensemble decision tree models has made a significant impact in the field of computer science. His contributions continue to influence advancements in machine learning and optimization techniques.

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