Research focuses on the coupled dynamics of human and natural systems, specifically in the context of forest dynamics. I utilize a variety of modeling and analysis techniques, including agent-based modeling, cellular automata, machine learning and various spatial statistics and GIS-related methods. I am currently involved in projects that investigate the anthropogenic and biological drivers behind native and invasive forest pathogens and insects.

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.

• Social learning and cultural evolution
• Hunter-gatherers
• Evolutionary medicine

I work in the field of complex adaptive systems, specializing in multi-agent systems, simulation, machine learning, collective intelligence, self-organization, and self-adaptation. I am interested in contributing to innovative projects and research in these domains.

My experience spans across multiple large-scale international research projects in areas such as green urban logistics, blockchain for nuclear applications, autonomous robotics systems and simulation of biological neural networks.

As publically funded science has become increasingly complex, the policy and management literature has begun to focus more attention on how science is structured and organized. My research interests reside at the nexus of science and technology policy, organizational theory, and complexity theory—I am interested in how the management and organization of S&T research influences the implementation of policies and the emergence of organizational strategies and innovation. Although my research involves the use of multiple qualitative and quantitative methods, I rely heavily on agent based modeling and system dynamics approaches in addressing my research questions.

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

I am interested in the study of small-group decision-making using agent-based simulation of models grounded in sociological social psychology. I am also interested in a particular kind of small-group decision-making: peer review.

My interests include model componentization, statistics, data analysis and improving model development and resuability practices.

I specialize in creating agent-based models of biological systems for research and education in genetics, evolution, demography, ecology, and behavior.

I am interested in using agent-based model to understand the behavior of financial markets