Professor Hubert’s research is motivated by interactions and incentives. These terms are related in the context of her work focusing on the behavior of economic actors: interaction between consumers, workers, agents through their decisions, their actions, the price they pay for a service, or even through an external parameter; incentives refer to Contract Theory, specifically Principal-Agents problems. Prof. Hubert also studies Nash equilibria and Mean-Field Games, using recently introduced and state of the art tools in stochastic control, with applications in Energy, Epidemiology and Finance. Prof. Rebrova’s interests lie in high-dimensional probability. They involve interactions with random matrix theory, numerical linear algebra, and mathematical data science. An overarching theme of Prof. Rebrova’s research is to study the structure of large high-dimensional objects in the presence of randomness and use this understanding to develop randomized algorithms that efficiently process complex data. Specifically, Prof. Rebrova’s recent work focuses on low-rank tensor methods and their applications to interpretable machine learning, as well as on developing robust and provable randomized iterative methods for solving linear systems.