David K.A. Mordecai was Invited to Speak at the AFCEA 2018 Cybersecurity Technology Summit

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David K.A. Mordecai, President of Risk Economics and Adjunct Professor at New York University (NYU), was invited to speak at the Armed Forces Communications and Electronics Association (AFCEA) 2018 Cybersecurity Technology Summit on February 27, 2018 in Arlington, VA.

David Mordecai participated on a panel entitled Artificial Intelligence: The Next Line of Defense, where he highlighted  and discussed cyberphysical risk management considerations common to civilian commercial and industrial Internet of Things (IoT), as well as defense network settings.

The panel was moderated by Rick Hansen, Professor of Practice, Cybersecurity and Information Assurance at Capitol Technology University.

The other speakers on the panel were the following:

  • Dr. Alexander Kott, Chief Scientist, United States Army Research Lab
  • Adam Cardinal-Stakenas: Information Assurance DirectorateNational Security Agency (NSA)
  • Patrick Sullivan, Director, Security Technology and Strategy, Akamai


About Risk Economics
Risk Economics is a New York based advisory firm founded in 1999, providing advisory services at the intersection of commercial business-process engineering and risk engineering with a particular focus on coupling commercial reinsurance and financial technology, through the rigorous application of agent-based, demographic, and statistical methodologies to microeconomic and macroeconomic analytics.

About RiskEcon® Lab @ Courant Institute
The mission of RiskEcon® Lab for Decision Metrics @ Courant Institute of Mathematical Sciences NYU is the development of experimental testbeds and analytics that employ high-dimensional datasets from innovative sources by applying a range of computational and analytical methods to commercial and industrial sensor networks and edge computing embedded systems, focusing primarily on research and development (R&D) of remote and compressed sensing, anomaly detection, forensic analytics and statistical process control. By employing applied computational statistics within the context of robust and scalable data analytic solutions, the goal is robust integration of machine learning with signal processing for measurement and control, in order to conduct research fundamental to large-scale, real-world questions in risk and liability management.