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Dani Ushizima

Deputy Group Leader of Analytics/Visualization, Staff Scientist at the Computational Research Division of LBNL. She is also the Image Analysis/Machine Vision consultant at the National Energy Research Scientific Computing Center (NERSC) . Ushizima is the Machine Vision co-P.I. for the Center of Applied Mathematics for Energy Related Applications (CAMERA). At the University of California, Berkeley, she is one of the selected BIDS Data Scientists fellows. Her work focuses on image analysis and pattern recognition applied to diverse scientific domains - images range from biomedical micrographies to geological materials and composites, e.g. micro-tomography of materials with applications to carbon sequestration. She has acted as Principal/Co- Investigator of several DOE projects related to image analysis, machine learning, and high performance computing. Interests include computer vision, machine learning, signal processing, quantitative microscopy, and high-performance computing. [Previous work]

Hover on these images for details:

Synchrotron-based X-ray micro-tomography for analysis of composites with applications to jet engine construction.

Micro-CT of glass beads in biogenic mixture, using microbe S.pasteurii for calcite precipitation in research about efficient carbon sequestration.

Identi cation of palladium faces and platinum core from electron tomography for precise control of catalytic reactions during material design.

Quantitative Structure Activity Relationship (QSAR) models for nanoparticles: quantification of chemical composites.

Segmentation of cervical cells as part of the Pap-smear analysis automation process: this work was awarded 1st place in ISBI'2014 Cervical Cell Recognition Challenge.

Team from LBL/UCSF/Oblong developed a gesture-based interface with network diagrams that show traffic patterns, combined with maps of brain structure.

Using time series of 3D confocal imagery to measure cell movement patterns and speed during mitosis of human mammary epithelial cells (in vivo).

Using high-resolution imagery from Auer's lab to search for sub-cellular structures, such as microtubules within human mammary epithelial cells.

Recent News:

  • 08/2014: Ushizima awarded BIDS fellowship to be a BIDS data scientist at UC Berkeley

  • 08/2014: Ushizima's PhD student, Kate Odziomek, is awarded the Americal Chemical Society for Scienfic Excellence

  • 05/29/2014: CAMERA Lecture Series at the ALS

  • 05/2014: Brazilian institution recognition of public service

  • 04/2014: Ushizima and team awarded 1st Place in Algorithm Challenge

  • 2014: The "Math Foundry" is real - meet our DOE center, a.k.a. CAMERA

  • 2014: Showing students the importance of work at LBNL Slides [HERE]

  • 2013: Future brain viewers in UX Magazine: exploratory visualization in medicine;

  • 2013: Exploratory analysis of the brain: video of collaboration between UCSF neurologists, LBNL computer scientists and Oblong industry experts;

  • 2013: Brain, precision-medicine and visualization: national laboratory is ready to play a role in the Brain Initiative related projects;

  • SAMSI 2012-2013: Program on Statistical and Computational Methodology for Massive Datasets

  • 2010: This manuscript is #20 among Top 25 Hottest Papers in Digital Signal Processing. More

    Previous work:

    Before joining LBNL in 2007, she was a Computer Science Professor at the Catholic University of Santos, Sao Paulo, Brazil. While professor, she was the Principal Investigator of Computer Vision in Leukemia Diagnosis (Young Researcher Award) and FAP-Books (FAP-IV), co-PI in Agriculture and Ceramics projects with industry (Small Business Incentive Grants), all sponsored by FAPESP science foundation, Sao Paulo, Brazil. Her PhD is from the University of Sao Paulo (USP) in Computational Physics (2004), where she developed a prototype for computer-aided leukemia diagnosis in collaboration with the Clinic Hospital FMRP-USP, and feature selection tools for general purpose data applications. As part of her PhD, she was also as a Visiting Researcher in the Electrical and Computer Engineering Department at UC Santa Barbara (2004).
  • Automating image analysis