Edinburgh, United Kingdom

Niraj Kumar

This inventor holds 1 USPTO granted patent and 2 published patent applications. Top assignee: Paypal, Inc.. Active years: 2026.

USPTO Granted Patents = 1 

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Innovations of Niraj Kumar

Introduction

Niraj Kumar is an accomplished inventor based in Edinburgh, GB. He has made significant contributions to the field of feature selection in machine learning. His innovative approach utilizes feedback-assisted optimization models to enhance the efficiency of feature selection processes.

Latest Patents

Niraj Kumar holds a patent for "Feature selection using feedback-assisted optimization models." This patent discloses techniques related to feature selection based on feedback-assisted optimization models. The disclosed techniques involve accessing a training dataset that includes a plurality of data samples with corresponding labels. The computer system performs feature-selection operations to select a subset of features from the plurality of features to include in a reduced feature set. The optimization model utilized in this process incorporates performance feedback information from machine learning models trained on candidate feature sets. Ultimately, the system generates an output value indicating the subset of features to include in the reduced feature set. He has 1 patent to his name.

Career Highlights

Niraj Kumar is currently employed at PayPal, Inc., where he applies his expertise in machine learning and optimization. His work focuses on developing innovative solutions that enhance the performance of data-driven applications.

Collaborations

Niraj collaborates with Nitin S Sharma, leveraging their combined expertise to drive advancements in their field.

Conclusion

Niraj Kumar's contributions to feature selection and optimization models demonstrate his commitment to innovation in technology. His work continues to influence the development of efficient machine learning applications.

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