Ben Kopec

Ben Kopec is an American academic and researcher specializing in the field of Computer Science, particularly focusing on cybersecurity, privacy, and data science. He is known for his work on topics such as adversarial machine learning, data privacy mechanisms like differential privacy, and security vulnerabilities in machine learning systems.

Kopec's research often explores the intersection of machine learning and security, investigating how machine learning models can be attacked, defended, and leveraged for malicious purposes. His work contributes to a better understanding of the security and privacy risks associated with the increasing deployment of machine learning technologies in various applications. He often publishes his work in leading academic conferences and journals in the fields of computer security and machine learning.

Kopec's work often involves the development of new methods and algorithms for enhancing the privacy and security of machine learning systems, including techniques for protecting data from inference attacks and mitigating the impact of adversarial examples. He has also contributed to the development of tools and frameworks for evaluating the security and privacy properties of machine learning models.

His research is often supported by grants from government agencies and foundations. He is actively involved in the computer science research community.