Machine Learning and Security: The Good, the Bad, and the Hopeful

ABSTRACT

Machine learning has made a tremendous progress over the last decade. In fact, many believe now that ML techniques are a “silver bullet”, capable of making progress on any real-world problem they are applied to.

But is that really so?

In this talk, I will discuss a major challenge in the real-world deployment of ML: making our ML solutions be robust, reliable and secure. In particular, I will survey the widespread vulnerabilities of state-of-the-art ML models to various forms of adversarial noise and then outline promising approaches to alleviating these deficiencies.

-

Aleksander Madry is the NBX Associate Professor of Computer Science in the MIT EECS Department and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 2011 and, prior to joining the MIT faculty, he spent some time at Microsoft Research New England and on the faculty of EPFL. Aleksander's research interests span algorithms, continuous optimization, science of deep learning and understanding machine learning from a robustness perspective. His work has been recognized with a number of awards, including an NSF CAREER Award, an Alfred P. Sloan Research Fellowship, an ACM Doctoral Dissertation Award Honorable Mention, and 2018 Presburger Award.