Seattle, WA, United States of America

Hareesh Lakshmi Narayanan


Average Co-Inventor Count = 4.0

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2022

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1 patent (USPTO):Explore Patents

Title: Innovations by Hareesh Lakshmi Narayanan

Introduction

Hareesh Lakshmi Narayanan is an accomplished inventor based in Seattle, WA. He has made significant contributions to the field of machine learning through his innovative patent. His work focuses on enhancing the efficiency of data labeling processes, which is crucial for developing high-quality datasets for machine learning applications.

Latest Patents

Hareesh holds a patent titled "Custom labeling workflows in an active learning-based data labeling service." This patent describes techniques for an active learning-based data labeling service that allows users to build and manage large, high-accuracy datasets. The service automates the annotation and management of datasets, significantly increasing the efficiency of labeling tasks and reducing the time required for manual labeling. By utilizing active learning techniques, the service minimizes the amount of data that needs to be manually labeled. As subsets of the dataset are labeled, the labeled data is used to train a model that can identify additional objects without manual intervention. This iterative process continues until the model converges, allowing for efficient dataset labeling.

Career Highlights

Hareesh is currently employed at Amazon Technologies, Inc., where he applies his expertise in machine learning and data management. His innovative approach to data labeling has positioned him as a valuable asset in the tech industry.

Collaborations

Hareesh has collaborated with notable colleagues, including Rahul Sharma and Arvind Jayasundar, who contribute to the dynamic environment of innovation at Amazon Technologies.

Conclusion

Hareesh Lakshmi Narayanan's contributions to active learning-based data labeling services exemplify the intersection of innovation and technology. His work not only enhances the efficiency of machine learning systems but also sets a precedent for future advancements in the field.

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