I develop and analyze mathematical models of biological and physical systems — turning differential equations, optimization, and data into insight about how living systems behave in space and time.
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Research Areas
Spatial Metabolic ModelingFBA × reaction–diffusion
I built an equation-based framework that couples genome-scale flux balance analysis with reaction–diffusion PDEs to simulate how microbial communities grow and interact across space and time.
Disease Transmission ModelingODE & PDE epidemics
I build ODE and PDE models of infectious-disease dynamics — including reaction–diffusion and chemotaxis models of schistosomiasis transmission with 2D spatial simulation, and compartmental epidemic models such as SVEIR with vaccination.
Predictive Modeling for HealthChronic kidney disease
Working with virtual-care data, I applied predictive modeling to classify chronic kidney disease patients by their probability of stage degeneration from longitudinal eGFR trajectories. This co-authored work (Ashenafi et al.) was presented at the SIAM Mathematical Problems in Industry workshop.
Mathematical NeuroscienceRetinal modeling
As a co-author, I contributed to modeling the spatial dependence of rods and cones in the retina — using mathematical models to study the organization of photoreceptors and what it implies for vision.
Bayesian Inference & Complex SystemsUncertainty & collective behavior
I use Bayesian hierarchical modeling and MCMC to quantify uncertainty in experimental data (including pupillometry / psycholinguistics), and I study complex systems through agent-based models.
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