Dundas, Canada

Michael Joseph Uloth

USPTO Granted Patents = 1 

Average Co-Inventor Count = 26.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Michael Joseph Uloth: Innovator in Machine Learning and Perturbation Data Synthesis

Introduction

Michael Joseph Uloth is a notable inventor based in Dundas, Canada. He has made significant contributions to the field of machine learning, particularly in the synthesis of perturbation data. His innovative work has led to the development of advanced graphical user interfaces that enhance the understanding of cellular perturbations.

Latest Patents

Uloth holds a patent for a groundbreaking invention titled "Utilizing machine learning models to synthesize perturbation data to generate perturbation heatmap graphical user interfaces." This patent describes systems, non-transitory computer-readable media, and methods for embedding perturbation data via a machine learning model. The invention focuses on filtering, aligning, and aggregating embeddings to create a genome-wide perturbation database for real-time generation of perturbation heatmaps. The systems can process a variety of perturbation images portraying cells from multiple wells, generating well-level image embeddings and aligning them for further analysis.

Career Highlights

Michael Joseph Uloth is currently employed at Recursion Pharmaceuticals, Inc., where he applies his expertise in machine learning to advance the company's research initiatives. His work is pivotal in developing technologies that facilitate the understanding of complex biological systems through data analysis.

Collaborations

Uloth collaborates with talented individuals such as Marta Marie Fay and August Orvis Allen, contributing to a dynamic research environment that fosters innovation and discovery.

Conclusion

Michael Joseph Uloth's contributions to machine learning and perturbation data synthesis exemplify the impact of innovative thinking in the field of biotechnology. His work continues to influence advancements in understanding cellular behaviors and enhancing research methodologies.

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