Jason is dedicated to advancing the state of the art in secure and robust AI. With a bachelor’s degree in computer science from San Diego State University, he is focused on ensuring trust, security, privacy, bias, and robustness of AI/ML models. Jason has led the development efforts of a commercial solution for the detection and repair of vulnerabilities in deep learning systems, and the co-author of multiple patents related to the cybersecurity of systems including AI/ML, embedded devices, supply chain, and others. His passion for improving the field has driven him to push the boundaries of what is possible and make a meaningful impact in the fields of AI and cybersecurity.

Presentations

23x

Beyond Static Analysis: Applying Symbolic Execution to Embedded Linux

Static analysis tools are fast, scalable, and widely used in modern software workflows, but they struggle to reason about runtime behaviors in complex embedded systems. This talk focuses on how symbolic execution can be used as a complementary technique to explore deeper execution paths and uncover subtle bugs that traditional static analysis often misses. We will explain the core concepts, key challenges like path explosion, practical mitigation strategies, and real-world case studies involving embedded Linux applications.

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23x

Poison Once, Compromise Many: How Model Reuse Amplifies AI Vulnerabilities

AI models are rarely built from scratch. Through model reuse and transfer learning, organizations inherit risks they may never see. This session explores how backdoors, poisoning attacks, and evasion techniques survive across generations of models, exposing downstream systems to compromise.

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