David K.A. Mordecai, President of Risk Economics and Adjunct Professor at New York University (NYU), was invited to speak at the Joint Quantum Symposium held at NYU on April 5-6, 2018.
David Mordecai participated on a panel entitled Future of Quantum Information, where he highlighted and discussed prospective domain-specific RiskTech use-case applications for quantum devices and related nanotechnologies within Artificial Intelligence (AI), autonomous systems control, industrial Internet of Things (IoT) and remote sensing.
The panel was introduced by Paul Horn, Senior Vice Provost for Research at NYU and Senior Vice Dean for Strategic Initiatives and Entrepreneurship at the NYU Tandon School of Engineering.
The other speakers on the panel were the following:
- Charles Tahan, Technical Director, Laboratory for Physical Sciences, University of Maryland
- Valerie Feldmann, CEO, Palestrina Group
- Jay Gambetta, Manager, IBM
- Chris Monroe, University of Maryland and IonQ
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 for Decision Metrics
The mission of RiskEcon® Lab @ 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 in the public interest.
