Research Statement

Machine learning algorithms now play a major role in all kinds of decision-making scenarios, such as college admissions, credit approval, and resume screening. When the stakes are high, self-interested agents --- about whom decisions are being made --- are increasingly tempted to manipulate the machine learning algorithm, in order to better fulfill their own goals, which are generally different from the decision maker's. This highlights the importance of making machine learning algorithms robust against manipulation. The main focus of my research is on designing and analyzing machine learning algorithms that are robust against strategic manipulation, which is different from the relatively well-studied notion of adversarial robustness.

My research sets the foundations for several key problems in machine learning in the presence of strategic behavior: