Dr. David Rushing Dewhurst joined DARPA as a program manager in April 2024 to design, execute, and transition programs at the nexus of technology and economic strategy. His research interests include financial intelligence, the design of economic mechanisms and financial assets, capital markets, and payment systems.
Before joining DARPA as a program manager, Dewhurst served as a technical advisor for government research and development programs at the agency and was a Capstone Fellow at Yale University. Dewhurst also worked as a senior scientist at a defense research and development firm, a risk management data scientist in the financial industry, and a computer scientist at a federally funded research and development center.
Dewhurst earned a Bachelor of Arts in economics, mathematics, and political science, an M.S. in mathematics, and a doctorate in complex systems and data science, all from the University of Vermont. He also attended the Séminaire de Mathématiques Supérieures at the University of Montreal.
programs
Resilient Supply-and-Demand Networks (RSDN)
The Department of Defense (DoD) has a critical need to secure its sources of materiel against both intentional—including adversarial—and unintentional disruptions. Extensive global networks of private-sector vendors, commonly called “supply chains,” collaborate to provide these key resources, including precursor components and materials. The Resilient Supply-and-Demand Networks (RSDN) program adopts the phrase “supply-and-demand network” (SDN) in lieu of “supply chain” to emphasize that the strategic challenges are more extensive than the logistic challenges of delivering (“supplying”) materiel.
Anticipatory and Adaptive Anti-Money Laundering (A3ML)
Money laundering directly harms American citizens and global interests. Half of North Korea's nuclear program is funded by laundered funds, according to statements by the White House, while a federal indictment alleges that money launderers tied to Chinese underground banking are a primary source of financial services for Mexico's Sinaloa cartel. Despite recent anti-money laundering efforts, the United States (U.S.) still faces challenges in countering money laundering effectively for several reasons. According to Congressional research, money laundering schemes often evade detection and disruption, as anti-money laundering (AML) efforts today rely on manual analysis of large amounts of data and are limited by finite resources and human cognitive processing speed. To address these challenges, DARPA seeks to revolutionize the practice of anti-money laundering through its A3ML program. A3ML aims to develop algorithms to sift through financial transactions graphs for suspicious patterns, learn new patterns to anticipate future activities, and develop techniques to represent patterns of illicit financial behavior in a concise, machine-readable format that is also easily understood by human analysts. The program's success hinges on algorithms' ability to learn a precise representation of how bad actors move money around the world without sharing sensitive data.