WiDS Tech Talk: Applications & Implications of Women in Data Science

2020 Women in Data Science (WiDS) Conference


March 2, 2020
10:15am to 11:45am
Banatao Auditorium, Sutardja Dai Hall, UC Berkeley
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Daniela Ushizima is the Computer Vision Area Leader for the Center of Advanced Mathematics for Energy Research Applications (CAMERA) at Berkeley Lab, and a Data Scientist for the Berkeley Institute for Data Science (BIDS) at UC Berkeley. In 2014, Ushizima received the Moore-Sloan Foundation Data Science Fellowship and the U.S. Department of Energy Early Career Award in 2015 to focus on the research project: Image across Domains, Experiments, Algorithms and learning (IDEAL). She is one of the founders of ImageXD, the first cross-domain initiative at BIDS, which is now a yearly conference focused on image analysis across domains. Together with colleagues at UCB, UCSF, and LBNL, Ushizima has been working on algorithms with applications ranging from biomedical image analysis to quality control for the design of new materials. At WIDS Berkeley, she will discuss advancements & challenges in medical imaging of breast cancer, cervical cancer, Alzheimer's brain cytology and Neuro-oncology. More info about projects at http://bit.ly/idealdatascience.

BIDS Data Scientist Dani Ushizima in a group photo at WiDS Berkeley 2020.



Daniela Ushizima

Staff Scientist, Applied Mathematics and Computational Research Division, Berkeley Lab

BIDS Faculty Affiliate Dani Ushizima is a Staff Scientist in the Machine Learning and Analytics Group in the Computational Research Division at Berkeley Lab, where she leads the Image Processing/Machine Vision team at CAMERA, and an Affiliate Faculty of the Bakar Computational Health Sciences Institute (BCHSI) at the University of California, San Francisco. She also leads the Center for Recognition and Inspection of Cells (CRIC), where her research focuses on imaging cancer cells for early-stage disease diagnosis. With 20 years of research and development experience in Computer Vision, Dani has focused primarily on quantitative microscopy and microstructure classification, from materials science to biomedical imaging.