I'm a computer scientist (PhD) moving from research into machine learning and data science. My work sits where advanced mathematics meets real engineering: I take a hard, high-dimensional problem, reduce it to its core, design experiments that build understanding, and deliver correct, reproducible software.
Over seven years in research (PhD plus postdoc) I've built scientific software end to end — analysis pipelines, open-source packages, and the infrastructure to run them — first primarily in Julia and now primarily in Python. I set up and administer a High-Performance Computing cluster, and I re-architected our core computation pipelines for a 10–15× speed-up over the original implementation.
I'm the author of eight publications, including a first-authored article in PLOS Computational Biology that made the journal's cover and was covered by Scientific American — work where I owned the complete statistical analysis.
Making high-dimensional ideas legible. Turning a hard, abstract result into a figure a non-expert can read and an expert can trust — see the visualization showcase.
Engineering rigour on research code. Pure functions, tests, caching, and reproducible pipelines — including a 10–15× speed-up of the core computation over the original implementation.
Depth across ML, statistics, and reinforcement learning. Topological data analysis, graph and network analysis, information theory, and experimental design, applied to real data.
PhD, Computer Science — University of Hertfordshire (Biocomputation group). Thesis: Topological insight into neuroscience.
MSc, Electronics & Computer Science in Medicine — Warsaw University of Technology, including an ERASMUS exchange at KU Leuven.
BSc, Biomedical Engineering (Distinction) — Warsaw University of Technology.
Based in London. For the full record see Publications, or grab the CV (PDF). Get in touch: email · LinkedIn · GitHub.