My research interests lie in econometrics and statistics with a focus on identification, estimation, and inference in nonparametric settings. My earlier work has primarily focused on models with endogenous regressors, such as panel data model and instrumental variable models, which play an important role in economics and where identifying and estimating objects of interest nonparametrically is particularly challenging. My current theoretical research investigates the use of shape restrictions in nonparametric estimation and inference as a middle ground between parametric and nonparametric methods. These restrictions are often reasonable assumptions in applications, they can drastically decrease confidence sets, and thereby yield much more precise conclusions. I also worked on empirical applications of nonparametric methods, such as estimating first price auctions or predicting stock returns using a large set of firm characteristics.