My research focuses on the development of mathematical and computational models to understand complex dynamical systems, particularly in biological and epidemiological contexts. I work at the intersection of mathematical biology, stochastic processes, and network based modeling, combining theoretical analysis with computational simulation to investigate disease dynamics and intervention strategies. My broader research interests also extend to graph theory, combinatorics, and cryptography, reflecting a strong interdisciplinary foundation in applied mathematics.
Future Directions
I am interested in extending my research toward multiscale epidemic modeling, data driven inference, and interdisciplinary applications that connect mathematics, biology, and computation to address real world challenges in agriculture and public health.
My current research investigates the spread and management of Frogeye Leaf Spot (FLS) in soybean systems using spatially explicit stochastic models. This work involves the development of a network based SEIRB compartmental framework that incorporates both direct plant to plant transmission and environmental pathways.
Key research directions include:
Construction of spatial contact networks from field scale plant layouts
Development of stochastic compartmental epidemic models
Parameter inference using approximate Bayesian computation (ABC)
Analysis of intervention strategies such as tillage practices and disease management approaches
This project integrates mathematical modeling, computational simulation, and biological insight to support data driven decision making in agricultural systems.
Beyond mathematical biology, my research background includes several projects in graph theory and cryptography, including work on labeling problems, multipartite graph structures, and novel encryption algorithms. These studies have resulted in peer reviewed conference publications and journal contributions, highlighting my interdisciplinary research trajectory.