My two preferred languages for scientific computing are Julia and Python, especially its JAX ecosystem. For three years I supervised practical sessions on scientific computing in Python for master students.

I use Julia as my go-to language, both for building complex educational animations using Makie and in my research to solve non-linear control problems in the optimize-then-discretize paradigm.

I use JAX to implement proofs of concept by leveraging its automatic differentiation capabilities to solve control problems in the discretize-then-optimize paradigm.

Optimization, the intuitive way

Repository
Iterative algorithms require proper initializations. Does initialization matter? Find out by choosing all initializations at once!

Bloch ball for noisy qubits

Repository
The Bloch ball is a fundamental tool which provides a geometrical interpretation for the dynamics and control of quantum systems.

Paths of eigenvalues

Track the collisions of eigenvalues as we vary the weight of a convex combination of matrices.

Singularity of the value function

Either one or exactly two time-minimal controls for a bilinear control system.

Rejection sampling

Sample from probability distributions and measure volumes.

Modal perspective on transport equations

Expore the effect of numerical diffusion and the Courant number on eigenvectors of circulant finite-differences matrices.