Bio: Una-May O'Reilly is the leader of ALFA Group at MIT-CSAIL. An AI and machine learning researcher for 20+ years, she is broadly interested in artificial adversarial intelligence -- the notion that competition has complex dynamics due to learning and adaptation signaled by experiential feedback. This interest directs her to the topic of security where she has developed machine learning algorithms that variously consider the arms races of malware, network and model attacks and the uses of adversarial inputs on deep learning models. Her passions are evolutionary computation and programming. This frequently leads her to investigate Genetic Programming. As well, it draws her to investigations of coevolutionary dynamics between populations of cooperative agents or adversaries, in settings as general as cybersecurity and machine learning.

Talk: Adversarial Intelligence Supported by Machine Learning

Abstract: My interest is in computationally replicating the behavior of adversaries who target algorithms/code/scripts at vulnerable targets and the defenders who try to stop the threats. I typically consider networks as targets but let's consider the most recent ML models - foundation models. How do goals blur in the current context where the community is trying to simultaneously address their safety and security?