Speaker Profile

Ph.D., Staff Scientist, Lawrence Berkeley National Laboratory / Data Scientist, UC Berkeley
Biography

Dani Ushizima aims to aggregate value to scientific data by constructing models, algorithms and software that leverage unlabeled massive datasets and curations by scientists, embedding prior knowledge of specific science areas. Knowledge has been prospected in three ways: (a) inclusion of domain experts for a fully immersive collaboration; (b) mining of massive datasets, including image and text; (c) exploration of advanced algorithms in machine learning, e.g. convolutional neural networks. Ushizima is the Image Processing Team Leader for the Center of Advanced Mathematics for Energy Research Applications (CAMERA) at the Lawrence Berkeley National Laboratory, and a data scientist at the Berkeley Institute for Data Sciences at UC Berkeley. She has twenty years of experience in Signal Processing and Computer Vision, being in charge of projects with applications ranging from quality control in the design of new materials to biomedical image analysis. Ushizima is the recipient of the U.S. Department of Energy Early Career award (2015) and the LBNL Director’s Award for Outreach (2017) for her work on data science and machine learning, and activities on scientific diplomacy with the U.S. Dept. of State TechWomen Program, respectively. She is also recipient of the Science without Borders Special Researcher award (CNPq/Brazil) for her work on machine learning applied to cytology, as part of a cancer research initiative focused on women’s healthcare.

Session Abstract – PMWC 2019 Silicon Valley

Session Synopsis: Imaging plays pivotal roles in the diagnosis, pathology staging, treatment response assessment, and the prognosis of many cancers. It is a crucial part of precision patient care and advancing at a rapid pace with innovative developments in imaging sciences, algorithm development, and an improved understanding of the complex biology of cancer. This session focus of how precision imaging may assist with optimal clinical decision-making and outcomes prediction.

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