Hackensack, NY, United States of America

Yuva Manhendran

USPTO Granted Patents = 1 

Average Co-Inventor Count = 3.0

ph-index = 1

Forward Citations = 9(Granted Patents)


Company Filing History:


Years Active: 2015

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1 patent (USPTO):Explore Patents

Title: Yuva Manhendran: Innovator in Ensemble Learning Technologies

Introduction

Yuva Manhendran is a notable inventor based in Hackensack, NY (US). He has made significant contributions to the field of machine learning, particularly in the area of ensemble learning. His innovative approach has the potential to enhance the efficiency and effectiveness of data modeling processes.

Latest Patents

Yuva holds a patent titled "Methods and system for providing simultaneous multi-task ensemble learning." This patent describes a complete end-to-end modeling system that encompasses data sampling, feature engineering, action labeling, and model learning. The system is designed to operate with minimal human intervention, utilizing an automatic mechanism to streamline the modeling process. The patent also discloses a processor-readable medium that stores executable instructions for an automated data sampling and prediction structure training component. This component is configured to automatically collect user event data samples and train multiple prediction structures in parallel.

Career Highlights

Yuva Manhendran is currently employed at Dstillery, Inc., where he applies his expertise in machine learning and data analysis. His work focuses on developing advanced modeling techniques that can significantly improve predictive analytics.

Collaborations

Yuva collaborates with talented professionals in his field, including Brian Dalessandro and Rodney Alan Hook. These collaborations enhance the innovative environment at Dstillery, Inc. and contribute to the development of cutting-edge technologies.

Conclusion

Yuva Manhendran is a pioneering inventor whose work in ensemble learning technologies is shaping the future of data modeling. His contributions are vital to advancing machine learning applications and improving predictive capabilities across various industries.

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