Differentiable Gaussian processes
Energy-conserving models, gradient observations, physical prior means, and uncertainty-aware prediction.
My work combines computational chemistry, machine learning, and scientific software engineering. I am interested in models that preserve physical structure while reducing the cost of molecular calculations.
I work across methodology and implementation: from differentiable Gaussian processes and physical prior means to software used in practical atomistic workflows.