Dawn Song

Dawn Song is a Professor in Computer Science at UC Berkeley and Co-Director of Berkeley Center for Responsible Decentralized Intelligence. Her research interest lies in AI safety and security, Agentic AI, deep learning, security and privacy, and decentralization technology. She is the recipient of numerous 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, ACM SIGSAC Outstanding Innovation Award, and more than 10 Test-of-Time Awards and Best Paper Awards from top conferences in Computer Security and Deep Learning. She has been recognized as Most Influential Scholar (AMiner Award), for being the most cited scholar in computer security. She is an ACM Fellow and an IEEE Fellow, and an Elected Member of American Academy of Arts and Sciences. She obtained her Ph.D. degree from UC Berkeley. She is also a serial entrepreneur and has been named on the Female Founder 100 List by Inc. and Wired25 List of Innovators.

AI2050 Project

AI is rapidly changing how software is built, with systems now writing code and acting as autonomous agents. While powerful, these advances also create new security risks—flaws in AI-generated code or agent behavior can be exploited at unprecedented scale and speed. Song’s project tackles this by developing AI tools that not only write code, but also produce formal security specifications and mathematical proofs that the code is correct and secure. This “provably secure” approach aims to eliminate entire classes of vulnerabilities before deployment, making AI-powered systems safer, more trustworthy, and better suited for critical applications that impact society worldwide.