Research

My research is motivated by several domain-specific themes, including:

Preferential cooperation and resource redistribution within families and extended genealogical networks
The relationship between resource stress, innovation, and persistence in human populations
The role of peripheral or low-connectivity agents, outsiders, and other perturbations on normative group behavior
Multiscale responses of cooperative networks under stress, from individual and familial units to communities and nations

These phenomena are only partially understood and likely reflect a combination of biological constraints and culturally learned behavior.

My research focuses on developing mathematical models relevant to these questions, with applications to related problems in ecology, anthropology, and sociology. I am particularly interested in:

Applications of measure-theoretic probability and stochastic processes
Interacting particle systems and formal frameworks for agent-based models
Characterizing dynamic network structure, transport, and interaction processes on networks
Scaling limits relevant to spatiotemporal population dynamics
Statistical inference for complex and partially observed systems

Across these areas, my goal is to identify minimal, interpretable models that connect local interaction rules to large-scale population-level behavior.

Selected Preprints and Work in Progress

Threshold Redistribution on a Network (in prep for Journal of Theoretical Biology). A stochastic model of threshold-based resource redistribution on dynamic networks, studied through a two-dimensional stepping-stone population model with lineage-dependent cooperation. Focuses on the relationship between dynamic network topology, resource density, and population persistence.
Statistical Inference for a Rare-Event Process with High-Dimensional Observational Data (thesis section, work in progress). Develops inference methods for rare-event stochastic processes under partial observability, motivated by point-source and diffuse analyses in high-energy astroparticle physics.
Machine Learning Processes for Observational Data (thesis section, work in progress). Examines classification and generative modeling through a probabilistic lens, with emphasis on uncertainty, identifiability, and the role of modeling assumptions in partially observed systems.