John S. Davis, II

john davis
Senior Information Scientist; Professor, Pardee RAND Graduate School; Co-Director, Center for Scalable Computing and Analysis
Washington Office

Education

B.S. in electrical engineering, Howard University; Ph.D. in electrical engineering, University of California, Berkeley

Overview

John Davis is an information scientist at the RAND Corporation. His research focuses on the impact of technology on society and includes projects involving cybersecurity, Big Data, and digital privacy. He has led research projects for the Department of Homeland Security (DHS) and the Office of the Secretary of Defense. Davis is also a core professor in the Pardee RAND Graduate School and co-teaches a course on Big Data for policy researchers, and he serves as co-director of RAND's Center for Scalable Computing and Analysis, where he helps shape the incorporation of Big Data techniques and practices into RAND's data science activities.

Prior to joining RAND, Davis worked at IBM Research where he led several projects in context-aware and pervasive computing that studied the design and architecture of systems for applying machine learning to user data. He has software development experience in production environments at WaPo Labs (an innovation lab within the Washington Post Company) and OMB (Executive Office of the President), and he also founded a startup.

Davis holds four patents and has published extensively in peer-reviewed ACM/IEEE workshops and conferences. He earned a BSEE at Howard University and a Ph.D. in EECS at the University of California, Berkeley, during which he studied communications theory, digital signal processing, and the electronic design process of signal processing systems.

Pardee RAND Graduate School Courses

Recent Projects

  • A Framework for Programming and Budgeting for Cybersecurity
  • Information Flow Between Acquisition and Intelligence
  • FVAP and the Road Ahead
  • A Dataset for Biometric Testing

Honors & Awards

  • 2014 Bronze Award, RAND
  • 2008 Best Paper Award, IEEE/IFIP International Symposium on Trust, Security and Privacy for Pervasive Applications

Commentary

  • Digital silhouettes of people

    Rethinking Data Privacy

    Society benefits from the exchange of large-scale data in many ways. Anonymization is the usual mechanism for addressing the privacy of data subjects. Unfortunately, anonymization is broken.

    Oct 5, 2016 Inside Sources

Publications