Phil Gross

Title: Phil Gross: Innovator in Machine Learning for Power Grids

Introduction

Phil Gross is a notable inventor based in Brooklyn, NY. He has made significant contributions to the field of machine learning, particularly in its application to electrical grids. His innovative approach aims to enhance the reliability and efficiency of power systems.

Latest Patents

Phil Gross holds a patent for a machine learning system designed for ranking a collection of filtered propensity to failure metrics of similar components within an electrical grid. This system includes a raw data assembly that provides representative raw data of like components, a data processor that converts this raw data into more uniform data, and a database for storing the processed data. Additionally, it features a machine learning engine that generates propensity to failure metrics, an evaluation engine that filters non-compliant metrics, and a decision support application that displays a ranking of these metrics.

Career Highlights

Throughout his career, Phil Gross has worked with prestigious organizations such as Columbia University and Consolidated Edison Company of New York, Inc. His experience in these institutions has allowed him to develop and refine his innovative ideas in the field of machine learning and electrical engineering.

Collaborations

Phil has collaborated with notable professionals in his field, including Roger N. Anderson and Albert Boulanger. These partnerships have contributed to the advancement of his research and the successful implementation of his inventions.

Conclusion

Phil Gross is a pioneering inventor whose work in machine learning for power grids has the potential to transform the industry. His innovative patent and collaborations highlight his commitment to improving electrical systems and ensuring their reliability.