Littleton, MA, United States of America

Nikolaus Bates-Haus

USPTO Granted Patents = 8 

Average Co-Inventor Count = 4.4

ph-index = 2

Forward Citations = 21(Granted Patents)


Company Filing History:


Years Active: 2017-2025

where 'Filed Patents' based on already Granted Patents

8 patents (USPTO):

Title: The Innovative Mind of Nikolaus Bates-Haus

Introduction

Nikolaus Bates-Haus is a prominent inventor based in Littleton, MA (US). He has made significant contributions to the field of data science and machine learning, holding a total of 8 patents. His work focuses on improving record clustering techniques, which are essential for managing big data effectively.

Latest Patents

One of his latest patents is titled "Method of using clusters to train supervised entity resolution in big data." This innovative method involves performing record clustering by learning from verified clusters, which serve as the source of training data in a deduplication workflow utilizing supervised machine learning. Another notable patent is "Methods and computer program products for clustering records using imperfect rules." This patent outlines a process for record clustering that employs training rules, training-rule labels, and a pair-wise classifier, among other components, to enhance the accuracy of clustering algorithms.

Career Highlights

Nikolaus Bates-Haus is currently employed at Tamr, Inc., where he continues to develop cutting-edge solutions for data management. His expertise in machine learning and data clustering has positioned him as a valuable asset in the tech industry. His innovative approaches have garnered attention and respect from peers and industry leaders alike.

Collaborations

Nikolaus has collaborated with notable colleagues such as Ihab Francis Ilyas and George Beskales. These partnerships have further enriched his work and contributed to the advancement of technologies in data science.

Conclusion

Nikolaus Bates-Haus exemplifies the spirit of innovation in the realm of data science. His contributions through patents and collaborations continue to shape the future of machine learning and big data management.

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