Toronto, Canada

Giuseppe Marcello Antonio Castiglione

USPTO Granted Patents = 1 

Average Co-Inventor Count = 5.0

ph-index = 1


Company Filing History:


Years Active: 2024

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1 patent (USPTO):Explore Patents

Title: Giuseppe Marcello Antonio Castiglione: Innovator in Machine Learning Security

Introduction

Giuseppe Marcello Antonio Castiglione is a notable inventor based in Toronto, Canada. He has made significant contributions to the field of machine learning, particularly in enhancing cybersecurity measures. His innovative approach focuses on identifying vulnerabilities in machine learning models, which is crucial in today's technology-driven world.

Latest Patents

Castiglione holds a patent for a "System and method for adversarial vulnerability testing of machine learning models." This patent proposes a system that receives a representation of a non-differentiable machine learning model. It transforms the input model into a smoothed version and conducts an adversarial search against this smoothed model. The outcome is an output data value that indicates potential vulnerabilities to adversarial examples. The patent also includes various embodiments aimed at improving computational efficiency and accuracy, such as noise injection and hyperparameter control. Flagged vulnerabilities can prompt models to be re-validated, re-trained, or removed, thereby addressing increased cybersecurity risks.

Career Highlights

Giuseppe Castiglione is currently employed at the Royal Bank of Canada, where he applies his expertise in machine learning and cybersecurity. His work is instrumental in developing secure systems that protect sensitive data and enhance the bank's technological infrastructure.

Collaborations

Castiglione collaborates with talented professionals in his field, including Weiguang Ding and Sayedmasoud Hashemi Amroabadi. These collaborations foster innovation and contribute to the advancement of machine learning security.

Conclusion

Giuseppe Marcello Antonio Castiglione is a pioneering inventor whose work in adversarial vulnerability testing is shaping the future of machine learning security. His contributions are vital in ensuring the integrity and safety of machine learning applications.

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