Dr. Song will first present recent results in the area of secure deep learning, in particular, adversarial deep learning---how deep learning systems could be easily fooled and what we need to do to address the issues. She will also talk about how AI and deep learning can help enable new capabilities in security applications. Finally, I will conclude with key challenges and future directions at the intersection of AI and Security: how AI and deep learning can enable better security, and how Security can enable better AI.
Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning and security. 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, blockchain and smart contracts, to the intersection of machine learning and security. 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 obtained her Ph.D. degree from UC Berkeley. Prior to joining UC Berkeley as a faculty, she was a faculty at Carnegie Mellon University from 2002 to 2007.