Research Work

I develop mathematical and computational models of complex systems to understand how individual-level behaviors and evolutionary processes generate population-level outcomes. My research examines how targeted interventions—genetic technologies, chemical controls, or policy changes—interact with ecological and evolutionary constraints to inform decision-making and management.

Conceptual overview of my research program illustrating how targeted interventions act on evolving individual-level states within structured populations to generate emergent population-level dynamics, and how inference under imperfect monitoring feeds back into adap- tive intervention design. The framework treats interventions as evolutionary processes embed- ded in complex ecological and social systems.

A central scientific goal of my research is: to understand how strong targetted interventions reshape evolutionary and ecological dynamics in structured/unstructured populations, and to identify the conditions under which such interventions succeed, fail, or generate unintended outcomes. To address this goal, my research program integrates three closely connected axes decribed below.

Genetic Biocontrol & Population Genetics

I model the dynamics of synthetic gene drive systems as deliberate interventions. My work focuses on how biological realism—including mating complexity, spatial heterogeneity, and stochasticity—fundamentally reshapes the efficacy and risk profiles of vector control and conservation efforts.

Applications: Malaria suppression in mosquitoes and invasive rodent management on islands.
Monitoring: Developing "Target Product Profiles" to optimize surveillance under imperfect information.

Evolutionary Game Theory & Strategic Adaptation

I use evolutionary game theory (specifically asymmetric games) to analyze how distinct classes of agents respond to interventions/uncertainity.

Social Dynamics: Modeling "harassment bribery" on interdependent networks to identify why certain policy interventions succeed or fail based on population structure.
Eco-Evolutionary Feedback: Analyzing strategic responses to chemical interventions, such as the evolution of fungicide resistance in agricultural landscapes.

Microbial Antagonism & Coexistence

I investigate the fundamental rules governing diversity and persistence in systems under intense selective pressure.

Stability: Identifying structural constraints and interaction topologies that allow antagonistic microbial communities to coexist where classical theory predicts collapse.
Complexity: Using microbial systems as tractable analogues for broader intervention-driven dynamics.

Decision Theory & Policy Engagement

Across my research program, a unifying goal is to generate decision-relevant insight into the limits and robustness of interventions in evolving systems. I bridge the gap between theoretical modeling and management by integrating population dynamics with optimal control and value-of-information (VoI) frameworks.

Robustness: Evaluating intervention strategies for their resilience to uncertainty and feedback.
Impact: Contributing quantitative risk assessments to government-commissioned reports on emerging genetic technologies.

Open-Source Tools

Open-source tools developed for research, education, and regulatory engagement:

DrMxR (Drive Mixer): An interactive web-based platform for exploring and communicating population-genetic models of gene drive systems; developed for researchers, educators, and regulators. https://pverma.shinyapps.io/DrMxR/.
TPP Explorer: Web-based application for evaluating diagnostic performance and sam- pling designs for monitoring gene drive presence and frequency in mosquito populations. https://pverma.shinyapps.io/tpp_explorer/.

Creative Commons attribution on the image "Gene Drive inheritence". (Mariuswalter, 2017)

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