Yangsibo Huang

Title: Innovations of Yangsibo Huang in Secure Distributed Deep Learning

Introduction

Yangsibo Huang is an accomplished inventor based in Princeton, NJ (US). He has made significant contributions to the field of deep learning, particularly in the area of secure and robust distributed systems. His innovative approach to encrypting data for neural networks showcases his expertise and forward-thinking mindset.

Latest Patents

Yangsibo Huang holds a patent titled "System and method for secure and robust distributed deep learning." This patent describes a method for encrypting image data for a neural network, which includes mixing the image data with other datapoints to form mixed data. Additionally, it applies a pixel-wise random mask to the mixed data to create encrypted data. The patent also outlines a method for encrypting text data for natural language processing, which involves encoding each text datapoint via a pretrained text encoder, mixing the encoded datapoints, and applying a random mask to form encrypted data. This innovative approach enhances the security and robustness of deep learning systems.

Career Highlights

Yangsibo Huang is affiliated with Princeton University, where he continues to advance research in deep learning and data security. His work has garnered attention for its practical applications and potential to improve the safety of neural networks.

Collaborations

Yangsibo Huang has collaborated with notable colleagues, including Sanjeev Arora and Kai Yi Li. These partnerships have contributed to the development of cutting-edge technologies in the field of artificial intelligence.

Conclusion

Yangsibo Huang's contributions to secure distributed deep learning exemplify the innovative spirit of modern inventors. His patent work not only enhances data security but also paves the way for future advancements in neural network technology.