Dawn Song details challenges and exciting new opportunities at the intersection of AI and security and explains how AI and deep learning can enable better security and how security can enable better AI. You’ll learn about secure deep learning and approaches to ensure the integrity of decisions made by deep learning.
Dawn also offers an overview of the challenges of privacy-preserving machine learning and outlines new techniques to enable it. She then discusses a recent project on privacy-preserving smart contracts and working toward democratization of AI and shares future directions at the intersection of AI and security.
Dawn Song is a professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning, security, and the blockchain. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, distributed systems security, applied cryptography, the blockchain, and smart contracts to the intersection of machine learning and security. She is also a serial entrepreneur. Previously, she was a faculty member at Carnegie Mellon University. She is the recipient of various awards, including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, the Faculty Research Award from IBM, Google and other major tech companies, and best paper awards from top conferences in computer security and deep learning. She is ranked the most cited scholar in computer security (AMiner Award). Dawn holds a PhD from UC Berkeley.
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