As machine learning becomes more powerful and pervasive, it has an impact on the many software systems that rely on it and on the underlying software and hardware platforms. The resulting questions should concern virtually all aspects of the theory and practice of software, as they relate to software engineering, security and trust, programming, tools, foundations, and more. This talk will discuss some of those questions. In particular, it will present research on adversarial machine learning and on differentiable programming languages suitable for machine learning by gradient descent.

Martín Abadi is a research scientist at Google. He is also a Professor Emeritus at the University of California at Santa Cruz, where was a Professor in the Computer Science Department till 2013. He has held an annual Chair at the Collège de France, and has worked at Digital’s System Research Center, Microsoft Research Silicon Valley, and other industrial research labs. He received his Ph.D. at Stanford University in 1987. His research is mainly on computer and network security, programming languages and systems, and specification and verification methods. It has been recognized with the Outstanding Innovation Award of the ACM Special Interest Group on Security, Audit and Control and with the Hall of Fame Award of the ACM Special Interest Group on Operating Systems, among other awards. He is a Fellow of the Association for Computing Machinery (ACM) and of the American Association for the Advancement of Science (AAAS), and a member of the National Academy of Engineering (NAE). He holds a doctorate honoris causa from École normale supérieure Paris-Saclay.