Human-in-the-loop control systems
Vehicle dynamics and control
I study how humans and automated systems can work together to complete physical tasks. Often these tasks are expressed as optimization problems. The humans interact with the automated system by defining the objective function to be optimized, by providing information about the structure of the problem, and sometimes by approving and implementing the actions recommended by the automated system. By combining the ability of humans to learn structure from experience and the ability of automated algorithms to carry out computation and process data, I develop systems that achieve higher performance than either humans or automation could achieve on their own.
Models of human search behavior
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Search is the process of making rational decisions under uncertainty. In [?] we developed a model of human behavior in a spatial search task. Notably, we found that some people achieved very high performance on such tasks, including significant numbers of people who achieved performance better than an otherwise “optimal” algorithm. Our model allowed us to show that this high performance is consistent with the humans having good priors, i.e., the high-performing people have good intuitions about the task. In [?], we considered the problem of learning these intuitions from data and showed that this could be achieved using our model. In [?], we have considered other, more sophisticated models of human choice behavior based on the concept of satisficing, where the decision maker cares only about achieving performance above a given threshold, rather than achieving maximum possible performance.
Reactive decision making