Pittsburgh, PA, United States of America

Benjamin Solnik

USPTO Granted Patents = 2 

Average Co-Inventor Count = 4.4

ph-index = 1

Forward Citations = 1(Granted Patents)


Company Filing History:


Years Active: 2024

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2 patents (USPTO):Explore Patents

Title: The Innovations of Benjamin Solnik

Introduction

Benjamin Solnik is an accomplished inventor based in Pittsburgh, PA. He has made significant contributions to the field of optimization through his innovative patents. With a total of two patents to his name, Solnik's work focuses on enhancing the performance of systems and machine learning models.

Latest Patents

Solnik's latest patents include "Optimization of parameters of a system, product, or process" and "Optimization of parameter values for machine-learned models." The first patent describes a computer-implemented method for optimizing parameters that affect the performance of a system, product, or process. This method involves establishing an optimization procedure and utilizing prior evaluations to suggest improved variants. The second patent outlines a similar approach for machine learning models, where an optimization algorithm generates suggested variants based on previous performance evaluations and adjustable parameter values.

Career Highlights

Benjamin Solnik is currently employed at Google Inc., where he applies his expertise in optimization to various projects. His work at Google has allowed him to explore innovative solutions that enhance the efficiency and effectiveness of technology.

Collaborations

Throughout his career, Solnik has collaborated with notable colleagues, including Daniel Reuben Golovin and Subhodeep Moitra. These collaborations have contributed to the advancement of his research and the successful development of his patents.

Conclusion

Benjamin Solnik's contributions to the field of optimization through his patents demonstrate his innovative spirit and commitment to advancing technology. His work continues to influence the development of systems and machine learning models, showcasing the importance of optimization in modern applications.

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