Salt Lake City, UT, United States of America

Chi Cheng

USPTO Granted Patents = 2 

Average Co-Inventor Count = 17.0

ph-index = 1

Forward Citations = 3(Granted Patents)


Company Filing History:


Years Active: 2024

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2 patents (USPTO):Explore Patents

Title: Inventor Spotlight: Chi Cheng from Salt Lake City, UT

Introduction

Chi Cheng is an innovative inventor based in Salt Lake City, UT, known for his contributions to the field of machine learning and microscopy. With a total of two patents, Cheng is dedicated to advancing technologies that enhance the interpretation and analysis of phenomic images.

Latest Patents

Chi Cheng's latest patent focuses on utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings. This invention encapsulates systems, methods, and non-transitory computer-readable media designed to train generative machine learning models. These models are capable of generating embeddings from phenomic images or other microscopy representations. By leveraging techniques such as momentum-tracking optimization and Fourier transformation losses, Cheng's invention significantly improves the accuracy and efficiency of training on large-scale image batches.

Career Highlights

Chi Cheng is currently associated with Recursion Pharmaceuticals, Inc., where he applies his expertise in generative machine learning to enhance biopharmaceutical research. His work demonstrates a strong commitment to leveraging technology to elevate scientific understanding and innovation in healthcare.

Collaborations

Cheng has collaborated with notable coworkers including Oren Zeev Kraus and Kian Runnels Kenyon-Dean, who contribute to the same innovative environment at Recursion Pharmaceuticals. Through teamwork and shared knowledge, they strive to push the boundaries of what is possible in the intersection of machine learning and biological research.

Conclusion

Chi Cheng continues to be a significant figure in the realm of innovation, particularly in the application of generative models for microscopy. His work not only advances machine learning techniques but also has the potential to impact the way researchers interpret phenomic data in the future. As technology continues to evolve, inventors like Chi Cheng play a crucial role in shaping the landscape of scientific discoveries.

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