Hi! I'm Buse

Geophysicist blending inversion theory, physics, and machine learning.

I work on physics-informed machine learning pipelines that infer latent physical fields from sparse observations and images. Right now I’m a postdoctoral researcher at CSIRO, developing hybrid ML–physics workflows for complex systems.

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Current role
Postdoctoral Research Fellow, CSIRO Data61
Research pillars
Inverse problems, physics-informed ML, latent field estimation
Previously
Ph.D. in Seismology & Mathematical Geophysics, ANU

What I care about

Interpretable models for complex Earth systems.

My work sits at the intersection of inversion theory, geophysical imaging, and machine learning. I like turning noisy, incomplete data into structured constraints that help us understand and predict how our planet behaves.

Physics-Informed ML

Neural networks guided by physical laws, priors, and PDE constraints instead of pure curve fitting.

Hybrid Inversion Pipelines

Estimating latent physical fields from sparse measurements and images using differentiable forward models.

Scientific Software

Reusable, well-documented research code that other scientists can build on, not just one-off scripts.