yurii liudomyrskyi

University of Cambridge MPhil in Scientific Computing graduate

Corpus Christi College member & alumni

My work sits at the intersection of scientific computing and machine learning. I build systems where mathematical rigour meets real-world constraints, focusing on performance, reliability, and reproducibility.

At Cambridge, I worked on large-scale sparse linear solvers for computational fluid dynamics, developing distributed implementations with MPI and analysing convergence and conditioning in high-dimensional settings. I also built GPU-accelerated numerical methods, focusing on performance and stability at scale.

Previously, I worked on deep learning for scientific imaging, designing models and training pipelines that remain robust under noisy, imperfect data.

I care about understanding systems end-to-end, from mathematical formulation to efficient implementation.