MZMi Zhang, Assistant Professor of Electrical and Computer Engineering, and his students recently has developed a novel Neural Architecture Search (NAS) approach that is nominated for the Best Paper Award of the 2019 International Conference on Computer Vision (ICCV) Neural Architects Workshop. In their work, they present an efficient NAS approach, named HM-NAS, that generalizes existing weight sharing based NAS approaches. Compared to state-of-the-arts, HM-NAS is able to achieve better architecture search performance and competitive model evaluation accuracy. Without the constraint imposed by the hand designed heuristics, the searched networks contain more flexible and meaningful architectures that existing weight sharing based NAS approaches are not able to discover.