Empowering Leadership: Computing Scholars of Tomorrow Alliance
Daniela Ushizima is a research scientist with the Analytics/Visualization and Mathematics groups in the Computational Research Division at Lawrence Berkeley National Laboratory (LBNL), and a consultant for the National Energy Research Scientific Computing Center. Her work focuses on the investigation of ground-breaking areas that require digital signal and/or images for pattern recognition - these data range from biomedical pictures to porous material micro-tomography for applications to carbon sequestration. She has acted as co-Principal Investigator of the projects: "Visualization and Analysis for Nanoscale Control of Geologic Carbon Dioxide" (Scidac-e) and "From images to models to computational input" (LDRD-DOE). Before joining LBNL in 2007, she was a computer science professor at the Catholic University of Santos, Brazil. While professor, she was Principal Investigator of "Computer Vision in Leukemia Diagnosis" (Young Researcher Grants) and FAP-Books (FAP-IV), co-Principal Investigator in "Classification in Agro-industry" (Small Business Incentive Grants), all sponsored by FAPESP science foundation, Sao Paulo, Brazil. She received her PhD from the University of Sao Paulo (USP) in Computational Physics (2004), where she developed a prototype for computer-aided leukemia diagnosis in collaboration with Hematology Lab (FMRP-USP) and feature selection tools for general purpose data application. Her master degree was in the same institute, with dissertation on biological signal processing. She was also a Visiting Researcher in the Electrical and Computer Engineering Department at UC Santa Barbara in 2004. Ushizima graduated from the Federal University of Sao Carlos, Brazil, with a degree in Computer Science in 1997, after completing a monograph about her work as software engineer on the automation of Intensive Therapy Unit procedures, in industry (Dixtal Biomedica). Her interests include computer vision, machine learning, signal processing, quantitative microscopy, and high-performance computing.