Knoxville, TN, United States of America

Nicholas Cernek

USPTO Granted Patents = 1 

Average Co-Inventor Count = 9.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Innovations of Nicholas Cernek

Introduction

Nicholas Cernek is an accomplished inventor based in Knoxville, TN (US). He has made significant contributions to the field of data management and storage through his innovative patent. His work focuses on enhancing the efficiency and flexibility of handling high-volume object data, particularly in the realm of machine learning.

Latest Patents

Nicholas Cernek holds a patent titled "Cross-platform flexible data model for dynamic storage, management, and retrieval of high-volume object data." This patent describes systems, non-transitory computer-readable media, and methods that implement a cross-platform flexible data model. The invention aims to facilitate the dynamic storage, management, and retrieval of high-volume object data, including multi-modal machine learning datasets. The disclosed systems operate across various non-tabular media and multiple digital repository platforms, allowing for efficient management of machine learning datasets through a centralized cross-platform metadata database.

Career Highlights

Nicholas Cernek is currently employed at Recursion Pharmaceuticals, Inc., where he applies his expertise in data management to advance the company's research and development efforts. His innovative approach to data handling has positioned him as a valuable asset in the field of pharmaceuticals and machine learning.

Collaborations

Some of his notable coworkers include Jill Theresa Vandenbosch and Conrad Banneker Owen. Their collaborative efforts contribute to the innovative environment at Recursion Pharmaceuticals, Inc.

Conclusion

Nicholas Cernek's contributions to the field of data management through his patent and work at Recursion Pharmaceuticals, Inc. highlight his role as a forward-thinking inventor. His innovative solutions are paving the way for advancements in the management of machine learning datasets.

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