David K.A. Mordecai
David K.A. Mordecai is lead investigator at the RiskEcon® Lab for Decision Metrics, established in 2011 in order to apply a range of computational and analytical methods to commercial, consumer and population-related societal trends, and Visiting Scholar at Courant Institute of Mathematical Sciences at New York University (NYU). He leads technical oversight for research activities exploring applications of agent-based computing and statistical inference, in conjunction with machine learning systems and methods, to a broad range of commercial and institutional contexts.
In 2010, David was invited to become a Fellow and a member of the Advisory Board of the Mathematical Finance program at Courant, subsequent to serving as a guest lecturer for the program since 2006. From 2012 to 2015, he held a joint appointment at Stern Graduate School of Business, as a Senior Research Scholar for Computational Economics of Commerce, Law and Geo-Politics. He has served as an adjunct instructor of applied mathematics at Courant, as well as an Adjunct Professor and an active member of the working group for the NYU Center for Data Science (NYUCDS) at its inception. He mentors students across various NYU divisions, including Courant, NYUCDS, Stern, Tisch ITP (Interactive Telecommunications Masters Program) and NYU Tandon School of Engineering. In 2014, he was appointed Course Director leading the NYUCDS Capstone graduate applied research program in its inaugural year. He is also associated with the NYU Social Media and Political Participation Lab (SMaPP), an interdisciplinary collaboration that researches the relationships between social media and political behavior.
He earned a Ph.D. with concentrations in Econometrics/ Mathematical Statistics and Economics/ Industrial Organization from the University of Chicago, and an M.B.A. in Finance from NYU Stern School of Business. His dissertation research applied principal components analysis to risk-based leverage estimation with a focus on empirical tests of the limts of arbitrage, and how market shocks trigger contagion via the financing of highly leveraged financial institutions during periods of extreme market volatility. In addition to studying financial economics and market microstructure, as well as the economics of law, regulation and industry structure, his doctoral education included the study of Bayesian decision theory, social network analysis and behavioral economics.
Since 2013, he has served as the first Scientist-in-Residence at FinTech Innovation Lab, an accelerator platform for early and growth stage technology firms, organized by The Partnership Fund for New York City in conjunction with Accenture and a consortium of venture capital firms and global financial institutions.
He is currently serving as Vice-Chair, Artificial Intelligence and Robotics for the American Bar Association Science & Technology Law Section. He has recently been appointed to the Uniform Law Commission Study Committee on Event Data Recorders in Cars, which is studying the need for and feasibility of uniform or model state legislation concerning event data recorders and generated vehicle data.
David is co-founder and co-managing member of Numerati Partners, which coordinates a data analytics and technology development ecosystem, with the mission of advancing and fostering the next generation of scalable data-intensive risk and liability management enterprises. The firm provides resources fundamental to advancing the development of nascent leading-edge inferential surveillance, monitoring, and predictive analytics technologies for deployment within the RiskTech domain: risk technologies associated with adaptive distributed, networked and embedded systems such as remote sensing, agent-oriented data analytics, computing and control systems. David is also co-founder of New York City based advisory firm, Risk Economics, founded in 1998, which specializes in the application of computational economics to the proprietary development and scalable implementation of robust modeling and data analytic frameworks for valuation, strategic and systemic risk analysis, and dynamic asset-liability management.
During his thirty year tenure in the financial services industry, David has served as a Managing Director at Swiss Re, where he led Relative-Value Market Strategies, a quantitative economics and financial engineering function with the global mandate to develop firm-wide and industry standards, benchmarks and frameworks for the valuation and trading of exposures underlying long-dated life, health, medical and pension liabilities as well as geopolitical risk. Prior to this, he served as Senior Advisor to the Head of Swiss Re Financial Services.
Previously, he was Managing Director of Structured Products, responsible for five billion of CDO assets, at a multi-strategy hedge fund with ten billion of assets under management. Prior to his role as a hedge fund manager, he was Vice President of Financial Engineering/Principal Finance at AIG, and a Director at the rating agency Fitch. During the first decade of his career, he specialized in credit analysis and the origination, structuring, and trading of leveraged loans for non-recourse project finance and highly leveraged transactions involving corporations and financial institutions.
David has served as an advisor on systemic risk issues to the Federal Reserve, the International Monetary Fund, the US Treasury, and the Commodities and Futures Trading Commission. He has also served as an advisor on hedge fund valuation issues to the International Organization of Securities Commissions. He coauthored the second working paper published by the Treasury Department’s Office of Financial Research, entitled Forging Best Practices in Risk Management. He has also been a member of the Investment Advisory Committee of the New York Mercantile Exchange. He is the founding Co-Chair of the International Association of Financial Engineers’ Liquidity Risk Committee, and has actively served on the Steering Committee of the IAFE Investor Risk Working Group on hedge fund and CTA disclosure issues, as well as the Advisory Board.
David was the founding Editor-in-Chief of the Journal of Risk Finance, a quarterly peer-reviewed research periodical, which addresses topics in financial risk intermediation. He remains a senior member of the journal’s Advisory Board subsequent to its sale by the original publishers Euromoney Institutional Investor to Emerald Publications. He has published numerous articles on topics including hedge fund strategies, structured credit, and weather and insurance derivatives. He has also served on the advisory committee for Chartered Alternative Investment Analysts Association, and on the editorial board of the Journal of Alternative Investments. In addition, he has been a guest lecturer at Columbia University, at the Graduate Business School, the Engineering/Operations Research Division, as well as the School for International and Public Affairs.
In 2016, David was elected to the board of governors of New York Academy of Sciences, a membership organization founded in 1817 with over 20,000 members, including research scientists at universities and industry, as well as representatives of business, government, and policy organizations, in 100 countries. He is also a member of the leadership council of Black Rock Forest Consortium, a 4,000-acre natural living laboratory for field-based scientific research and education, operated by a consortium of twenty-three colleges and universities, public and independent schools, and scientific and cultural institutions. From 2009 to 2015, he served as a member of the board of directors of Scenic Hudson, one of the nation’s three largest conservation organizations, and during his second term, co-chaired their Science Committee. In addition, he served on the board of directors of Hudson Highlands Land Trust from 2007 to 2016.
His biography has been published in the Marquis publications Who’s Who in the World, Who’s Who in America, and Who’s Who in Finance and Business
- Adaptive learning processes, activity-recognition, dynamic resource allocation and social computing applications of statistical inference, signal processing and stochastic control mechanisms to agent-based cyberphysical networks.
- Applications of generative agent-based modeling, spatio-temporal mapping and social computing to forensic geopolitical, socioeconomic, psychometric, sociometric, demographic, syndromic, and environmental surveillance, inference and analytics.
- Market-consistent enterprise risk and liability management applications of scalable, robust cyberphysical adaptive learning and pervasive, embedded multiagent computing systems.
- Institutional and industry configuration, market microstructure, and commercial process engineering applications of computational linguistics, law and economics to sociotechnical systems design.
- General methodological interests and experience include: applications of high-dimensional computational and graphical statistics to Bayesian experimental design, simulation and statistical inference; applied principal components and dimension reduction methods; applied information geometry, graphical statistical modeling and network analysis; applied functional data analysis, function approximation, filtering, sparse and compressed signaling, remote sensing, adaptive and dynamic system modeling, nonparametric hierarchical mixture and kernel models; generalized additive models; latent variable analysis; applied generalized linear and logistic regression models and discrete choice methods; algorithmic natural and social computing frameworks; robust market-based predictive estimation, pricing and valuation applications of auctions and parimutuel exchange mechanisms; multiattribute, multiobjective stochastic optimization and control systems.