The lasso-type models have been widely used for model selection in data mining, machine learning, and high-dimensional statistics. However, due to the ultrahigh-dimensional, large-scale data sets collected in many real-world applications, it remains challenging to solve the lasso-type problems even with state-of-the-art algorithms. My research aims to address the Big Data challenges by developing more scalable and efficient algorithms and software packages to fit lasso-type models with big data.