Dani Ushizima is a computer scientist who investigates computational approaches based on machine learning to interface data-driven models to materials characterization toward self-driving labs. Main expertise is on computer vision applied to high-resolution data for measuring 2D and 3D structure across spatial and temporal scales. Her research work has advanced the design of new materials imaged using instruments reliant on x-ray, electron, confocal, and other light-matter interactions. Current focus is on deep learning algorithms for improved quality control and decision making applied to samples, such as lithium metal batteries and biofuel plants. Dr. Ushizima is also a faculty affiliated to the Berkeley Institute of Data Science, at UC Berkeley, and the Bakar Institute, at UC San Francisco.