Dani Ushizima, Ph.D. is a Senior Scientist who investigates computational approaches based on Machine Learning to interface data-driven models to materials characterization. Main expertise is in Computer Vision applied to multimodal imaging for measuring 2D and 3D structure across spatial scales, which is key to advance research and design of new materials imaged using instruments reliant on x-ray, electron, confocal, and other light-matter interactions. The current focus is on lithium metal batteries and biofuel, as well as biomedical data analysis. She is also a Faculty affiliated with the Bakar Institute at UC San Francisco, consulting on biomedical imaging projects, and BIDS at UC Berkeley. 

At LBNL since 2007, her research in image analysis and pattern recognition has impacted a broad array of scientific investigations that depend upon images. In 2015, Ushizima received the U.S. Department of Energy Early Career Research award to focus on pattern recognition applied to diverse scientific domains - images range from biomedical to new materials science samples. She has also led the ML team for the Center for Advanced Mathematics for Energy Related Applications (CAMERA). Other initiatives she co-leads are CRIC (cancer), ImageXD (multimodal imaging) and new initiatives in biofuel and energy storage. Proud to be part of the Math for Experimental Data Analysis Group (MEDA).