Embedded Files
Research Interest
Applied Mathematics
Mathematical Ecology
Mathematical Modelling
Chaos Control and Synchronization
Epidemiology
Numerical Simulation-MATLAB, MATCONT, GRIND, Python
Skills
Chaos theory: chaos control and synchronization
Quantitative analysis of chaos: Lyapunov exponents, instantaneous phases, sensitivity to initial conditions (SDIC)
Bifurcation analysis: numerical simulations with GRIND and MATCONT
Chaos control methods: analytical, adaptive, and active control techniques
Coupled systems: achieving complete and phase synchrony via feasible coupling schemes and robust controls
Mathematical ecology: ecological modelling under seasonal perturbations; fear, Allee and refugia effects; local dispersal and global migration
Autonomous and non-autonomous dynamical systems
Mathematical modelling and simulation, Population, ecological, and epidemic models
Numerical solutions of autonomous and non-autonomous ODEs
Non-linear dynamics: stability, bifurcation, chaos control and synchronization
Application of Research Work
When an ecological model shows chaos, it means the numbers for an ecosystem start jumping around wildly—like a weather forecast swinging from sunshine to storm every few minutes. Those wild swings tell ecologists the system is on the edge of a big, sudden change, so they need to step in fast to stop a crash.
Another clue is synchrony, where many different species go up or down together, almost like dancers moving in the same beat. That shared rise-and-fall can mean everyone is chasing the same limited food or space, making the whole community fragile. Spotting either warning sign early—chaos or synchrony—gives scientists time to adjust conditions, spread out the pressure on resources, and keep the ecosystem steady and healthy.
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