Ayla Yasmin Khan

Title: Ayla Yasmin Khan: Innovator in Generative Machine Learning

Introduction

Ayla Yasmin Khan is a prominent inventor based in Salt Lake City, UT (US). She has made significant contributions to the field of generative machine learning, particularly in the area of microscopy representation. With a total of 2 patents, her work is paving the way for advancements in phenomic image analysis.

Latest Patents

Ayla's latest patents focus on utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings. The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing generative machine learning models to generate embeddings from phenomic images or other microscopy representations. The disclosed systems can train a generative machine learning model, such as a masked autoencoder generative model, to generate predicted or reconstructed phenomic images from masked versions of ground truth training phenomic images. In some cases, the systems utilize a momentum-tracking optimizer while reducing the loss of the generative machine learning model to enable efficient training on large-scale training image batches. Furthermore, the systems can utilize Fourier transformation losses with multi-stage weighting to improve the accuracy of the generative machine learning model on the phenomic images during training. The trained generative machine learning model can generate phenomic embeddings from input phenomic images for various phenomic comparisons.

Career Highlights

Ayla Yasmin Khan is currently employed at Recursion Pharmaceuticals, Inc., where she continues to innovate in her field. Her work is instrumental in enhancing the capabilities of machine learning models in analyzing complex biological data.

Collaborations

Ayla collaborates with notable colleagues, including Oren Zeev Kraus and Kian Runnels Kenyon-Dean, contributing to a dynamic and innovative work environment.

Conclusion

Ayla Yasmin Khan is a trailblazer in the realm of generative machine learning, with her patents reflecting her commitment to advancing technology in microscopy representation. Her contributions are set to have a lasting impact on the field.