College Station, TX, United States of America

Mingrui Liu

USPTO Granted Patents = 2 

Average Co-Inventor Count = 6.0

ph-index = 1

Forward Citations = 3(Granted Patents)


Company Filing History:


Years Active: 2023

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2 patents (USPTO):Explore Patents

Title: Innovations by Inventor Mingrui Liu

Introduction

Mingrui Liu is a notable inventor based in College Station, TX (US). He has made significant contributions to the field of machine learning, particularly in decentralized distributed training. With a total of 2 patents, Liu's work is paving the way for advancements in statistical set updating.

Latest Patents

Mingrui Liu's latest patents focus on the updating of statistical sets for decentralized distributed training of a machine learning model. These patents describe systems, computer-implemented methods, and computer program products designed to facilitate the updating process, including averaging and training of statistical sets. The system comprises a memory that stores computer executable components and a processor that executes these components. One key component averages a statistical set with an additional compatible statistical set to compute an averaged statistical set. This additional statistical set is obtained from a selected additional system among a plurality of systems, with the selection process guided by a randomization pattern.

Career Highlights

Mingrui Liu 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 innovative solutions in the tech industry.

Collaborations

Some of Mingrui Liu's coworkers include Xiaodong Cui and Wei Zhang. Their collaboration fosters a creative environment that enhances the development of innovative technologies.

Conclusion

Mingrui Liu's contributions to machine learning and decentralized training systems highlight his role as a significant inventor in the tech industry. His patents reflect a commitment to advancing technology and improving processes in machine learning.

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