Sure Independence Screening (SIS) is a method for dimensionality reduction in high-dimensional statistical modeling. It uses correlation learning to identify important predictors by ranking them based on their marginal correlation with the response variable. SIS reduces the dimensionality from a high scale to a moderate scale below the sample size, making variable selection more efficient and accurate. This method is particularly useful when dealing with ultra-high dimensional data, where traditional methods like the Dantzig selector face challenges due to computational cost and the logarithmic factor in risk estimation. SIS ensures that all important variables are retained with high probability, enabling the use of well-established methods like SCAD, Lasso, or Dantzig selector for further variable selection. Theoretical analysis shows that SIS maintains the oracle property, ensuring accurate estimation and selection. Numerical studies demonstrate that SIS significantly improves the performance of variable selection methods in high-dimensional settings, reducing computational burden and enhancing model accuracy. SIS is applicable to various models, including classification tasks, and has been shown to be effective in both simulated and real data scenarios.Sure Independence Screening (SIS) is a method for dimensionality reduction in high-dimensional statistical modeling. It uses correlation learning to identify important predictors by ranking them based on their marginal correlation with the response variable. SIS reduces the dimensionality from a high scale to a moderate scale below the sample size, making variable selection more efficient and accurate. This method is particularly useful when dealing with ultra-high dimensional data, where traditional methods like the Dantzig selector face challenges due to computational cost and the logarithmic factor in risk estimation. SIS ensures that all important variables are retained with high probability, enabling the use of well-established methods like SCAD, Lasso, or Dantzig selector for further variable selection. Theoretical analysis shows that SIS maintains the oracle property, ensuring accurate estimation and selection. Numerical studies demonstrate that SIS significantly improves the performance of variable selection methods in high-dimensional settings, reducing computational burden and enhancing model accuracy. SIS is applicable to various models, including classification tasks, and has been shown to be effective in both simulated and real data scenarios.