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 a senior information scientist at RAND where he studies the impact of technology on society via projects involving cybersecurity, big data and telecommunications. He has led research for the Department of Homeland Security, the Office of the Secretary of Defense and Microsoft, among others. Davis is a professor in the Pardee RAND Graduate School and co-teaches a course on big data for policy researchers. He also serves as co-director of RAND's Center for Scalable Computing and Analysis.

Davis's recent project work includes recommendations for an international organization tasked with cyber attribution, the development of an analysis plan to assist the FCC in assessing the transition to IP-based telecommunication options and an analysis of Google Analytics data related to the EngageNY education website. Research led by Davis has been covered by media outlets such as the Wall Street Journal, CyberScoop and Federal News Radio.

Prior to RAND, Davis worked at IBM (T.J. Watson Research Center) where he led projects in pervasive computing (Internet of Things) that studied systems for applying machine learning algorithms to user data. In addition, he has software development experience in production environments at WaPo Labs (an innovation lab at the Washington Post), OMB (Executive Office of the President) and as a startup founder. 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 UC Berkeley.

Pardee RAND Graduate School Courses

Recent Projects

  • A Framework for Programming and Budgeting for Cybersecurity
  • Use of Open Educational Resources in an Era of Common Standards
  • Stateless Attribution: Toward International Accountability in Cyberspace
  • Information Flow Between Acquisition and Intelligence

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