Beibei Wang

Title: Beibei Wang: Innovator in Deep Learning for Online User Activity Detection

Introduction

Beibei Wang is a prominent inventor based in Santa Clara, CA (US). She has made significant contributions to the field of deep learning, particularly in detecting abusive user activity in online networks. With a total of 2 patents, her work is paving the way for more secure online environments.

Latest Patents

Wang's latest patents include innovative algorithms that utilize deep learning techniques. The first patent focuses on "Deep learning using activity graph to detect abusive user activity in online networks." This invention introduces a deep learning algorithm that operates on a transition matrix formed from user activities. The transition matrix records the frequencies of transitions between different paths of user activity, allowing for a more nuanced understanding of user behavior.

The second patent, titled "Deep learning to detect abusive sequences of user activity in online network," enhances the detection of adversarial attacks. This algorithm operates directly on raw sequences of user activity, eliminating the need for human curation of features. By translating specific request paths into standardized tokens, the system can efficiently leverage hidden signals in the data, improving the detection of abusive activities.

Career Highlights

Beibei Wang is currently employed at Microsoft Technology Licensing, LLC, where she continues to innovate in the field of technology. Her work is instrumental in developing systems that enhance user safety in online platforms.

Collaborations

Wang collaborates with notable colleagues, including James R Verbus and Yi Wu. These partnerships contribute to the advancement of her research and the successful implementation of her inventions.

Conclusion

Beibei Wang's contributions to deep learning and user activity detection are significant in enhancing online security. Her innovative patents reflect her commitment to creating safer digital environments.