San Francisco, CA, United States of America

Ying Li

USPTO Granted Patents = 1 

Average Co-Inventor Count = 10.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Ying Li - Innovator in Content Selection Frameworks

Introduction

Ying Li is a notable inventor based in San Francisco, CA. He has made significant contributions to the field of content selection frameworks, showcasing his innovative spirit through his patent work. His expertise and dedication to advancing technology are evident in his latest patent.

Latest Patents

Ying Li holds a patent titled "Counterfactual policy evaluation of model performance." This invention describes an online system that utilizes an offline iterative clustering process to evaluate the performance of various content selection frameworks. The system clusters testing example data into a set of clusters and computes framework scores for each cluster. It also calculates improvement scores based on the performance of these clusters. The system determines whether to perform additional iterations by computing an aggregated improvement score. If this score does not meet a specified threshold, the system continues the iterative process. Upon completion, the system outputs the improvement scores from the most recent iteration. Ying Li's innovative approach enhances the evaluation of model performance in content selection.

Career Highlights

Ying Li is currently employed at Maplebear Inc., where he continues to develop and refine his innovative ideas. His work at the company reflects his commitment to advancing technology in the field of content selection.

Collaborations

Ying Li collaborates with talented individuals such as Jonathan Gu and Bo Xiao. Their combined expertise fosters a creative environment that drives innovation and enhances project outcomes.

Conclusion

Ying Li is a distinguished inventor whose work in content selection frameworks has made a significant impact in the field. His patent demonstrates his innovative thinking and dedication to improving model performance evaluation.

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