Astrophysics
// Imperial College London · PhD 2019–2023
My foundational research investigated the atmospheric evolution and habitability of close-in exoplanets orbiting M-dwarf stars. Using 3D Magnetohydrodynamic (MHD) simulations, I modelled the complex plasma interactions between magnetized stellar winds and planetary atmospheric outflows — demonstrating that stellar winds play a significant, often dominant, role in driving atmospheric escape beyond classic photoevaporation models. During my PhD, I then developed a hydrodynamic (HD) model of photoevaporative amtospheric escape to explore whether water-rich atmospheres experience escape in a simialr or different way to primordial H/He atmsopheres.
Water-Rich & Complex Atmospheric Chemistry
Developed novel 1D multifluid hydrodynamic models moving beyond simple hydrogen/helium compositions. Tracked how heavier elements — specifically water and oxygen — alter evaporative mass loss rates, challenging conventional single-fluid analytic models used for atmospheric escape estimation.
Stellar Winds Drive Evaporative Outflow Variations
Demonstrated through 3D MHD simulations that magnetized stellar winds create strong, time-variable distortions to exoplanet atmospheric outflows. Showed how the morphology of the planetary magnetosphere directly modulates transit absorption signatures, challenging models that treat evaporation as purely radiatively-driven.
Agent-Based Modeling & Migration
// Brunel University London · Current
I now apply the same computational modeling rigour to humanitarian crises. Using Agent-Based Models (ABMs), I simulate how individual human decisions — shaped by economic status, weather awareness, and social observation — aggregate into population-level displacement patterns. My focus is transparent, rule-based models that NGOs and policymakers can audit and trust, rather than black-box machine learning.
DFlee — Flood-Induced Population Displacement
Contributor to DFlee - which is a modification to the conflict-driven Flee ABM. It integrates flood levels with agent behavioral algorithms. Successfully reproduces key dynamics: preference for nearest safe shelter, heterogeneous evacuation timing based on social observation, and rapid population return once floodwaters recede.
Forecasting refugee return to Ukraine amid ongoing war and uncertainty
Analysed the uncertainty in a predictive ABM prototype for the UNHCR forecasting potential return pathways of Ukrainian refugees. The model maps push factors (conflict, infrastructure damage) and pull factors (safety, economic opportunity, family ties) to simulate return trajectories under multiple recovery scenarios.
Coping Mechanisms in Bangladesh Flooding
Developed conceptual agent models mapping how individuals in flood-prone Bangladesh respond to climate disasters. Proactive behaviors (food/water storage, property fortification) and reactive behaviors (cyclone shelter evacuation) are modeled as functions of economic status, flood severity history, and weather forecast awareness.
Qualitative Uncertainty Quantification in Models
Researching how to rigorously assess and communicate uncertainty in assumptions. Develops frameworks for evaluating confidence in structural model assumptions, parameters, mechanisms/rules, and data sources — so we understand the reliability boundaries of simulation results.