Riley Charles Ennis

Title: Innovations of Riley Charles Ennis

Introduction

Riley Charles Ennis is a notable inventor based in San Francisco, CA. He has made significant contributions to the field of machine learning, particularly in the context of genetic data. With a total of 2 patents, his work is paving the way for advancements in this innovative area.

Latest Patents

One of his latest patents focuses on generating machine learning models using genetic data. This patent outlines systems, methods, and apparatuses for creating and utilizing machine learning models that leverage genetic information. A set of input features for training the machine learning model can be identified and used to train the model based on training samples, for which one or more labels are known. The input features can include aligned variables, derived from sequences aligned to a population level or individual references, and non-aligned variables, such as sequence content. The features can be classified into different groups based on the underlying genetic data or intermediate values resulting from processing the genetic data. Features can be selected from a feature space to create a feature vector for training a model. The selection and creation of feature vectors can be performed iteratively to train many models as part of a search for optimal features and an optimal model.

Career Highlights

Riley is currently employed at Freenome Holdings, Inc., where he continues to innovate in the field of machine learning and genetics. His work is instrumental in developing technologies that can potentially revolutionize healthcare and personalized medicine.

Collaborations

Riley collaborates with talented individuals such as Gabriel Otte and Charles Roberts, contributing to a dynamic and innovative work environment.

Conclusion

Riley Charles Ennis is a prominent figure in the realm of machine learning and genetic data, with a focus on creating impactful technologies. His contributions are shaping the future of healthcare and data science.