Today’s most exciting technologies—self-driving cars, air taxis, space transportation, and
healthcare breakthroughs—rely on advanced systems that combine physics, computing, and
data. However, the inherent uncertainty in ensuring their safety, reliability, and performance is
challenging. Traditional methods often fall short, and while AI techniques like machine learning
offer significant potential, they lack the necessary safeguards and formal guarantees needed for
real-world applications. This talk will focus on how recent advances in hybrid systems and
control are addressing these challenges. By combining certifiable algorithms with modern AI,
hybrid systems enable the design of algorithms that meet safety and performance standards
under uncertainty, paving the way for safer autonomous vehicles, more reliable space
technologies, and robust AI tools.

In-Person Reception: 5:30 p.m.

Lecture: 6-7 p.m.