Rene Mai is a postdoctoral researcher. Her work answers the central question of how should humans work together with autonomous systems? Her work focuses on developing data-driven and control-theoretic frameworks for real-time collaboration between humans and robots and artificial intelligence agents to exceed both human and autonomous systems’ capabilities, while promoting team interdependence, situational awareness, and trust. This work specifically focuses on how humans perceive the world and themselves, and how those perceptions are turned into actions. In this framework, human decisions are described at three levels: (1) high-level strategy, such as choosing a route or method of addressing a problem; (2) behavioral modeling at the tactical level, which includes more granular decisions like when to turn and look at a ball or when to change lanes to avoid an obstacle; and (3) dynamical-system modeling for actions currently being enacted, such as the steering angle needed to change lanes. Classical data science and modern artificial intelligence methods such as control theory, system identification, and stochastic modeling are used to capture these layered sets of behavior.
While Rene’s research focuses on the most efficient methods for teams to accomplish joint goals, her professional path has been less direct. She holds an undergraduate degree in Physics from Texas A&M University and a juris doctor from the University of Texas at Austin School of Law. Before obtaining her PhD in Mechanical Engineering from Rensselaer Polytechnic Institute, she practiced as a patent litigation attorney at Weil, Gotshal & Manges LLP and Pillsbury Winthrop Shaw Pittman LLP. Her work analyzing complex technologies, from solar cells to the Internet and cellular standards, led her to discover her love of research and leave her legal career to pursue a PhD and, eventually, a career as a professor.