Image across Domains, Experiments, Algorithms and Learning

U. S. Department of Energy Advanced Scientific Computing Research Early Career Research Project

2016 - 2020

Imagine inspecting 1 TB of images in 1 sec? Forget binge watching, this means 200 movies in a blink of the eye. This amount of images overwhelms scientists who depend on experimental data recorded in image files. But how to visually examine that many pictures? What we need is to construct computer programs that recognize relevant patterns from digital images. That is what I have done for the past 20 years: I use computer vision and machine learning techniques to enable people to use pictures in decision-making. I construct algorithms applied to material science, aiming to control the quality of compounds and, for example, improve durability of aircrafts. Another project is to design computational tools for biological structure description and interactions, for example, to enable neurologists to detect cells and functions in cellular communities.

I’m Dani Ushizima and my mission is to turn data into decisions. As a Staff Scientist at the Computational Research Division, Berkeley Lab, and a member of the CAMERA Center and the Data Analytics and Visualization Group, LBNL, my job is to design analytics-driven strategies to cope with data. I also investigate Data Science methods as a Data Scientist Staff at the Berkeley Institute for Data Science (BIDS), UC Berkeley in collaboration with UCSF and BioCAMERA. As part of scientific teams, my R&D focuses on computer vision applied to images across domains. Other initiatives I co-lead are ImageXD and CRIC.

This website contains work partially sponsored by the United States. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product,process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or theRegents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.