Cyrus Neary
PhD Candidate, Computational Science, Engineering, and Math, UT Austin
RESCHEDULED TO: Friday, April 5 | 9:30 a.m. | AERO 111
Abstract: How can we transform artificial intelligence (AI) and machine learning capabilities into engineering systems? That is, how can we engineer AI systems within budget constraints, certify them with respect to stakeholder requirements, and ensure that they meet the needs of the end user? To answer these questions, my research creates engineering methodologies for AI systems, as well as AI algorithms that leverage the unique characteristics of engineering problems. In this talk, I will begin by presenting compositional approaches to AI system design, which enable independent development and testing of separate learning-enabled modules, and ultimately facilitate the process of reliably deploying their compositions in practice. Then, I will present control-oriented learning algorithms that integrate data with prior physics knowledge, yielding learning-enabled systems that effectively control hardware after mere minutes of data collection and training. Experiments on robotic hardware, ranging from ground vehicles to hexacopters, demonstrate the important role that these algorithms play in the fast and reliable transfer of simulation-and-data-driven AI algorithms to their target, real-world operating environments.
Bio: Cyrus Neary is a Ph.D. candidate in Computational Science, Engineering, and Mathematics at the University of Texas at Austin. His research develops theory and algorithms that enable scalable, generalizable, and modular artificial intelligence systems for purposes of autonomy, robotics, dynamics modeling, and control. Prior to his Ph.D., he earned a B.A.Sc. in Engineering Physics from the University of British Columbia.