Sunnyvale, CA, United States of America

Lan Wang

This inventor holds 3 USPTO granted patents and 6 published patent applications. Top assignee: Visa International Service Association. Active years: 2024-2026.

USPTO Granted Patents = 3 

% Patents Active = 100.0

Average Co-Inventor Count = 6.5

ph-index = 1


Company Filing History:


Years Active: 2024-2026

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3 patents (USPTO):Explore Patents

Title: Innovations by Lan Wang in Anomaly Detection

Introduction

Lan Wang is an accomplished inventor based in Sunnyvale, California. He has made significant contributions to the field of anomaly detection through his innovative patent. His work focuses on enhancing supervised anomaly detection methods, which are crucial in various applications.

Latest Patents

Lan Wang holds a patent titled "Method, system, and computer program product for synthetic oversampling for boosting supervised anomaly detection." This patent outlines methods, systems, and computer program products that formulate an iterative data mix-up problem into a Markov decision process (MDP). The tailored reward signal guides the learning process effectively. To solve the MDP, a deep deterministic actor-critic framework is modified to adapt a discrete-continuous decision space for training a data augmentation policy. This innovative approach has the potential to significantly improve the accuracy of anomaly detection systems.

Career Highlights

Lan Wang is currently employed at Visa International Service Association, where he applies his expertise in data science and machine learning. His work at Visa involves developing advanced technologies that enhance the security and efficiency of financial transactions.

Collaborations

Lan has collaborated with notable colleagues, including Kwei-Herng Lai and Huiyuan Chen. Their combined efforts contribute to the advancement of technology in their respective fields.

Conclusion

Lan Wang's innovative work in anomaly detection showcases his commitment to advancing technology. His contributions are vital in improving the effectiveness of supervised anomaly detection methods.

Profile summary based on public USPTO records.
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