Jonathan Hill received his Ph.D. in economics from the University of Colorado at Boulder in 2001. His early research concerned tests of functional form motivated by interests in perfect tests asymptotically and tests that exploit revealing structure on non-standard spaces; and tail parameter and tail dependence estimation and inference under weak assumptions motivated by new weak limit theory for tail arrays of dependent and non-stationary data. His research then evolved toward robust estimation problems for heavy tailed data, including M-estimation, GMM, and Empirical Likelihood; and robust inference problems including tests of functional form, moment conditions and dependence, with recent extensions to volatility spillover, Expected Shortfall, and Variance Targeting. His current research involves inference in the presence of many regressors, inference in the presence of nuisance parameters under the alternative hypothesis, and multivariate quantile regression.