Mountain View, CA, United States of America

Dehao Chen


Average Co-Inventor Count = 3.0

ph-index = 4

Forward Citations = 39(Granted Patents)


Location History:

  • Beijing, CN (2013)
  • Mountain View, CA (US) (2015 - 2016)
  • Fremont, CA (US) (2022)

Company Filing History:


Years Active: 2013-2022

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5 patents (USPTO):

Title: The Innovations of Dehao Chen

Introduction

Dehao Chen is a prominent inventor based in Mountain View, CA. He has made significant contributions to the field of artificial intelligence and neural networks. With a total of 5 patents, his work has been influential in advancing technology.

Latest Patents

One of Dehao Chen's latest patents is titled "Training giant neural networks using pipeline parallelism." This invention includes methods, systems, and apparatus for training large neural networks. The process involves obtaining data that specifies a partitioning of the neural network into N composite layers. Each composite layer consists of a distinct plurality of layers from the multiple network layers. Additionally, he has developed a method for "Continuous profiling for automatic feedback directed optimization." This system improves application performance by collecting production profile data while applications execute, converting it into symbolized profiles, and aggregating these profiles for future compilations.

Career Highlights

Dehao Chen is currently employed at Google Inc., where he continues to innovate and contribute to cutting-edge technology. His work focuses on enhancing the efficiency and performance of neural networks, which is crucial for various applications in artificial intelligence.

Collaborations

Some of his notable coworkers include Vinodha Ramasamy and Xinliang David Li. Their collaboration has likely fostered an environment of innovation and creativity within their projects.

Conclusion

Dehao Chen's contributions to the field of artificial intelligence and neural networks are noteworthy. His patents reflect a commitment to advancing technology and improving application performance.

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