PhD student in Aerospace Engineering at Georgia Tech’s Aerospace Systems Design Laboratory (ASDL), working across sustainable transportation systems, scientific machine learning, uncertainty quantification, and mobility resilience.
My research connects engineering-scale modeling with transportation questions: how systems become cleaner, more resilient, and more useful for people and communities. I am especially interested in models that are not only predictive, but interpretable, uncertainty-aware, and useful for decision-making.
- Sustainable transportation systems
- Mobility resilience and accessibility
- Scientific machine learning
- Surrogate modeling and multifidelity methods
- Uncertainty quantification
- Network disruption and digital twins
- Emissions and lifecycle modeling
Independent prototype for modeling road-network disruption scenarios in Atlanta and comparing travel-time and accessibility outcomes. Current work focuses on baseline network resilience, stress-test scenarios, and future equity/accessibility layers for essential services.
Mesh-free NURBS-based boundary element solver for solenoid magnetic field computation. Generates multifidelity electromagnetic datasets for surrogate modeling, POD feasibility analysis, and operator-learning exploration.
Systematic study of surrogate methods from first principles, including Gaussian process regression, response surface methods, radial basis functions, uncertainty behavior, and failure modes of approximation.
I am building a research profile at the intersection of transportation systems, scientific machine learning, and policy-relevant modeling. My goal is to develop models that can support sustainable and resilient transportation decision-making while remaining interpretable, uncertainty-aware, and grounded in real system behavior.
Python · NumPy · SciPy · scikit-learn · PyTorch · NetworkX · GeoPandas · OSMnx · Jupyter · Git · MATLAB