RiskEcon Lab @ NYU CIMS

RiskEcon Lab @ NYU CIMS

RiskEcon® Lab for Decision Metrics at NYU Courant Institute

RiskEcon® Lab for Decision Metrics was established in 2011 at Courant Institute of Mathematical Sciences, an independent division of New York University, widely considered to be one of the world’s leading mathematics educational and scientific research centers, and ranked first in applied mathematical research. The focus of the lab is to facilitate the development of software, analytics tools, and semantic libraries that employ high-dimensional datasets to integrate conventional data with web-enabled demographic, biometric, psychometric and sociometric data from innovative sources.

Recent events demonstrate that many large-scale geopolitical and socioeconomic questions are particularly related to the implications and effects of interrelated changes in demographics, technology adoption, and lifestyle choices on the economy. It is crucial for decision-making in both industry and government to understand these patterns. The most critical of these patterns are the emerging effects of changes in technology and consumer behavior on finance, labor, and housing, and on trends in income and wealth distribution, immigration, aging, health and the environment.

RiskEcon® Lab applies a range of computational and analytical methods to commercial, consumer and population-related societal trends, primarily focused on research and development. The goal of the lab’s activities is to integrate web-enabled crowdsourcing with machine learning, data-mining, and text-mining, in order to promote research fundamental to large-scale, real world questions, employing applied computational statistics, and robust and scalable data analytic solutions, with three fundamental objectives:

  • To foster, promote, and coordinate public-private-academic research partnerships
  • To sponsor, fund, organize and manage big data libraries, and
  • To advance NYU’s competency within applied computational statistics

RiskEcon® Lab’s primary role is to enable, facilitate and coordinate academic research in the development of commercially-viable, analytic applications employing computational statistical tools in conjunction with innovative and non-traditional data structures. In addition, other activities of RiskEcon® Lab involve the advancement of the fields of applied mathematical statitics and computational economics, via interdisciplinary post-doctoral, postgraduate, graduate research and education in data sciences and social computing.

RiskEcon® Lab is the cornerstone of the Computational Economics and Algorithmic Data Analytics (CEcADA) cooperative at New York University, established concurrently in 2011.

 

Press releases on the establishment of RiskEcon® Lab:

NYU Press release
Courant Newsletter Article

Links to RiskEcon Lab:
RiskEcon Lab at NYU CIMS
Samantha Kappagoda @ RiskEcon Lab
David Mordecai @ RiskEcon Lab

News and Events

David Mordecai participated in FinTech Innovation Lab 2017 Demo Day

David Mordecai, participated in Demo Day 2917 for FinTech Innovation Lab (FTIL) on June 22nd 2017, as Scientist-in-Resident for FTIL. […]

Bankruptcy Court Approves Settlement of MF Global Litigation Trust Claims against Corzine et al.

David K.A. Mordecai, President of Risk Economics, filed two expert reports on behalf of the Litigation Trustee of the MF […]

Samantha Kappagoda was invited to speak on a panel for the Council for Economic Education

Samantha Kappagoda, Chief Economist of Risk Economics, was invited to speak on a panel sponsored by the Council for Economic […]

David Mordecai was invited to speak at the American Association of State Compensation Insurance Funds (AASCIF) Mid-Year CEO Conference

David Mordecai, President of Risk Economics Inc. was invited to speak at the American Association of State Compensation Insurance Funds […]

Samantha Kappagoda was a speaker on the Economics of Financial Decision Making

Samantha Kappagoda, Chief Economist and co-founder of Risk Economics was invited to speak on a panel jointly sponsored by 100 […]

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