In recent years, in parallel to sparse models, low-rank models also develop significantly, in particular in subspace recovery problems. Besides robustness, low-rank subspace recovery models may have closed form solutions. This property further makes low-rank models distinct from sparse models. The results are both surprising and elegant. Moreover, they also open doors to better solutions, faster numerical algorithms, and interesting applications. In this talk, I will present my recent work in this line.