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My research lies in the intersection of machine learning and security, and focuses on addressing two related questions: what are the security and privacy implications of learning in adversarial environments, and how can we learn effectively in such environments? In pursuing these questions I follow an application-driven methodology: given a domain, I identify threat models governing adversaries ’ goals and capabilities, I consider attacks on learners in the domain and countermeasures in some cases, and in others I consider game-theoretic approaches to learning. This methodology leads to theorems about the fundamental limits of learning, practical learningbased solutions for real-world problems, or where possible both. My work is highly inter-disciplinary, tying together diverse topics from machine learning & statistics, theory, privacy & security, and systems measurement. I enjoy the many benefits of collaborating widely on my research. I have been fortunate to work with m

Year: 2012
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