Company Filing History:
Years Active: 2022
Title: Jared Bowns: Innovator in Machine Learning Model Interpretation
Introduction
Jared Bowns is an accomplished inventor based in Newburyport, MA (US). He has made significant contributions to the field of machine learning, particularly in the area of model interpretation. His innovative work has led to the development of a patent that enhances the understanding of machine learning predictions.
Latest Patents
Jared holds a patent titled "Systems and methods for machine learning model interpretation." This patent describes systems and methods for interpreting machine learning model predictions. An example method includes providing a machine learning model configured to receive a plurality of features as input and provide a prediction as output. The plurality of features includes an engineered feature that combines two or more parent features. The method also involves calculating a Shapley value for each feature in the plurality of features and allocating a respective portion of the Shapley value for the engineered feature to each of the two or more parent features. He has 1 patent to his name.
Career Highlights
Jared is currently employed at DataRobot, Inc., where he applies his expertise in machine learning and data science. His work focuses on developing advanced algorithms that improve the interpretability of machine learning models. This role allows him to contribute to cutting-edge technology that has a significant impact on various industries.
Collaborations
Throughout his career, Jared has collaborated with notable professionals in the field, including Mark Benjamin Romanowsky and Thomas Whitehead. These collaborations have fostered an environment of innovation and have led to the advancement of machine learning technologies.
Conclusion
Jared Bowns is a prominent figure in the realm of machine learning, with a focus on model interpretation. His patent and work at DataRobot, Inc. exemplify his commitment to advancing technology in this critical area. His contributions are paving the way for a deeper understanding of machine learning models and their predictions.