I am currently completing a PhD on information sharing for natural resources management. Research is based on case studies on oyster farming, in the Thau Basin, France and in New South Wales, Australia

Economics, Resources management, and Planification.

My research interests include policy informatics and decision making, modeling in policy analysis and management decisions, public health management and policy, and the role of public value in policy development. I am particularly interested in less mainstream approaches to modeling that account for learning, feedback, and other systems dynamics. I include Bayesian inference, agent-based models, and behavioral assumptions in both my research and teaching.
In my dissertation research, I conceptualize state Medicaid programs as complex adaptive systems characterized by diverse actors, behaviors, relationships, and objectives. These systems reproduce themselves through both strategic and emergent mechanisms of program management. I focus on the mechanism by which citizens are sorted into or out of the system: program enrollment. Using Bayesian regression and agent-based models, I explore the role of administrative practices (such as presumptive eligibility and longer continuous eligibility periods) in increasing enrollment of eligible citizens into Medicaid programs.

I study small- and large-scale sustainable resource management using a variety of techniques including mathematical modeling, agent-based simulation, and Statistical Inference

Modelling of socio-ecological systems and management of common property resources in artisanal fisheries. Population dynamics of coastal marine invertebrates exploited by artisanal fisheries.

Doing research on how the flood insurance system in the UK should be structured in the future to make it resilient for environmental change.

Flood Risk Management, Coupled Human-Natural System Modelling, Socio-hydrological Modelling, Agent-Based Modelling, Human Behaviour Modelling, Agent-Based Social Simulation, Hydrological and Hydraulic Modeling, Geographic Information Systems (GIS), Mapping, Risk Modelling and Risk Visualization, Disaster Risk Reduction

• Applied data science (machine/deep learning applications) and computational modeling (agent-based
  modeling) in U.S. Government
• Geographic Information Systems and analysis of dense urban environments and complex terrain
• Complexity theory and computational organizational design of distributed enterprise teams.
• Human Capital Management and Talent Management policy development

Community and Infrastructure Resilience
Disaster Recovery
Complex Systems Modeling
Agent-Based Modeling
System Dynamics
Machine Learning
Pattern Recognition
Data Mining
Spatial Analysis and Modeling
Construction Management
Building Information Modeling