Benjamin John Mabey

Title: Benjamin John Mabey: Innovator in Generative Machine Learning

Introduction

Benjamin John Mabey, an accomplished inventor based in Millcreek, UT, has made significant contributions to the field of machine learning. With a total of two patents to his name, he remains at the forefront of innovation in utilizing advanced technologies for image analysis.

Latest Patents

Mabey's latest patents focus on the utilization of masked autoencoder generative models to extract microscopy representation embeddings. His work presents systems, non-transitory computer-readable media, and methods for training and utilizing generative machine learning models to generate embeddings from phenomic images. The patented systems are capable of training a masked autoencoder generative model to produce reconstructed phenomic images from masked versions of ground truth training images. Notably, the systems incorporate a momentum-tracking optimizer to enhance training efficiency on large-scale image batches. Furthermore, Fourier transformation losses with multi-stage weighting are employed to improve model accuracy during training, leading to the generation of accurate phenomic embeddings for various comparisons.

Career Highlights

Currently, Mabey is associated with Recursion Pharmaceuticals, Inc., where he continues to explore innovative applications of machine learning in the biotechnology sector. His work at Recursion showcases a blend of computational advancements and practical applications, emphasizing the importance of generative models in scientific research.

Collaborations

Throughout his career, Mabey has collaborated with notable colleagues such as Oren Zeev Kraus and Kian Runnels Kenyon-Dean. These partnerships contribute to a robust research environment that fosters innovation and enhances the potential for breakthrough discoveries in the field of machine learning.

Conclusion

Benjamin John Mabey exemplifies the qualities of a modern inventor, leveraging technology to push the boundaries of knowledge in generative machine learning. His contributions not only reflect his expertise but also underscore the importance of collaboration in the pursuit of innovation, positioning him as a significant figure in the realm of scientific advancement.