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Office of the Chief Information Officer (OCIO)

Deron Burba, Chief Information Officer

The Office of the Chief Information Officer (OCIO) supports the Smithsonian community in the innovative, efficient use of technology to advance the Institution’s mission. OCIO is responsible for providing the technology tools, services, and applications needed to ensure robust day-to-day operations across the organization and for delivering leading-edge solutions in areas of strategic importance. OCIO also plays a leading role in the development and delivery of the “Digital Smithsonian,” enabling millions of people around the world to experience our vast collections, research, and educational resources remotely. Working in collaboration with unit colleagues, other central staff, and outside partners, OCIO is advancing institutional priorities in collections digitization, sharing of digital assets online, and engaging the public in our work.

The OCIO Data Science Lab (https://datascience.si.edu) was formed in 2016 in response to the dramatic increase in all forms of digital data across the Smithsonian. Members of the Data Science Lab seek to build collaborations both across Smithsonian units, as well as universities and other institutions and work on a variety of data-intensive research topics including biodiversity genomics and machine learning applications of digitized museum collections and archives. The Data Science Lab hosts both postdoctoral and graduate fellows pursuing research projects in these areas. See a list of recent publications here: https://datascience.si.edu/data-science-lab-publications.

Research Staff

Dikow, Rebecca B., Research Data Scientist. B.S. (2003) Cornell University; Ph.D. (2012) University of Chicago. Specialties: Biodiversity genomics, phylogenetic systematics, applications of machine learning to museum collections and archives. Contact: DikowR@si.edu

Affiliated Research Staff

Trizna, Michael G., Data Scientist. B.S. (2006) Virginia Tech; M.S. (2007) Virginia Commonwealth University. Specialties: Biodiversity informatics, data visualization, applications of machine learning to museum collections and archives. Contact: TriznaM@si.edu

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