In this talk I will discuss inference for high dimensional quantile regression, and present two new tests based on maximum-type statistics to detect the presence of significant predictors associated with the quantiles of a scalar response. The first test is based on the t-statistic associated with the chosen most informative predictor at the quantiles of interest. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behavior due to a weak identifiability issue. The second test is based on the maximum of score-type statistics. We show that for diverging dimensions, the test statistic converges to the extreme value distribution of Type I under the null hypothesis under some regularity conditions. For finite samples, we also introduce a convenient multiplier bootstrap method to construct critical values. The proposed tests are more flexible than existing methods based on mean regression, and have the added advantage of being robust against outliers in the response.