Decision Science and Control (DeSCon) Lab

We study the fundamental mathematics of interactive decision-making, applied to a variety of contexts including infrastructure optimization, epidemiology, and the distributed control of multiagent systems.


Current Projects

  • Socially-Networked Autonomy: How Should Machines Interact With Society? This NSF-funded project studies decision design methodologies for autonomous agents that are networked with and interacting among human beings in societal systems. The core question is this: how should a system planner design the routing policies of autonomous vehicles to have the greatest positive impact on overall network traffic congestion, even if human drivers react in a self-interested way to the behavior of the autonomous vehicles? Supported by NSF ECCS 2013779.
  • Optimizing the Life-Cycle Impacts of COVID-19 Policy Interventions. This project asks how leaders can make public policy decisions regarding the COVID-19 pandemic in a scientific way that is locally appropriate and properly accounts for both near-term and longer-term costs of policy interventions. This project combines rigorous mathematical modeling, innovative approaches to data collection, and input from policymakers, to develop a decision aid framework that weighs the costs and benefits of various policy interventions at a local level and tailors interventions to the locale considering the effects of specific indicators such as urbanization, economic distress, and availability of regional healthcare. Supported by NSF DEB 2032465.
  • Value-based Access Control System using Path Security (with PI Dr. Gedare Bloom). This NSA-funded project investigates novel theories, policies, and mechanisms for access control that presumes client credentials are inherently risky. Our approach relies on state-of-the-art advances being made in path-based security for Internet routing. The focus of path-based mechanisms is to enable path selection by senders and path validation back to the sender (source) by intermediate routers and the destination. Our objective is to design, implement, and enforce higher-level access control decisions built upon this baseline to protect digital assets within the (destination) network from malicious source nodes that can pass traditional user authentication mechanisms due to stolen credentials.

Current Graduate Students

  • Joshua Seaton 2019-Present
  • Pam Russell 2019-Present
  • Brandon Collins 2020-Present
  • Colton Hill 2021-Present
  • Will Wesley 2022-Present

Current Undergraduate Researchers

  • Brendan Gould, 2021-Present
  • Sonia Karsanbhai, 2021-Present

Past Masters Thesis Students

  • Ryan Young, 2020, First employment: The MITRE Corporation
  • Joshua Seaton, 2021. First employment: DeSCon Lab

Past Undergraduate Researchers

  • Brandon Collins, 2018-2020. First employment: DeSCon Lab
  • Joseph Mazzocco, 2019. First employment: Raytheon