Haibo Wang

Title: Haibo Wang - Innovator in Automated Threat Detection

Introduction

Haibo Wang is a notable inventor based in Laredo, Texas, recognized for her contributions to the field of machine learning and automated threat detection. With a focus on enhancing supervised learning through unsupervised modeling, she has made significant strides in the technology sector.

Latest Patents

Haibo Wang holds a patent titled "Retraining supervised learning through unsupervised modeling." This innovative system and method are designed for automated threat detection. The system receives various types of unlabeled data and utilizes an unsupervised machine learning model to determine appropriate labels for the data. These labels are then provided to a supervised machine learning model during an initial training process. When new data is received, the supervised model is executed to cluster the new data according to the labels determined by the unsupervised model. Additionally, a label audit process may be implemented to update the output of the supervised model, ensuring continuous improvement in threat detection capabilities.

Career Highlights

Haibo Wang is currently employed at Entanglement, Inc., where she applies her expertise in machine learning to develop advanced systems for threat detection. Her work has positioned her as a key player in the intersection of technology and security.

Collaborations

Some of her notable coworkers include Richard T Hennig and John Lister, with whom she collaborates to further enhance the capabilities of their projects.

Conclusion

Haibo Wang's innovative approach to combining unsupervised and supervised machine learning models has paved the way for advancements in automated threat detection. Her contributions continue to impact the field significantly.