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 Internet of Things (IoT) Technology Summit on April 24, 2018, at The National Press Club in Washington, D.C.
David Mordecai participated on a panel entitled IoT Procurement: Assembling the Pieces, where he highlighted and discussed technology ecosystem curation, and public-private transition partnerships as enablers for scalable, reliable, and secure IoT adoption.
The panel was moderated by Jason Miller, Executive Editor/Reporter, Federal News Radio. The other speakers on the panel were the following:
- Jonathan Mostowski, President, Agile Acquisitions and Acquisitions Strategist & Bureaucracy Hacker, U.S. Digital Service
- Bradley Smith, Branch Chief/Contracting Officer, Office of Acquisitions, U.S. Securities and Exchange Commissions (SEC)
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 and reliable 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.