Research

During my Ph.D., I most importantly studied reproducible research, and the main reasons why we can lose reproducibility when using computers. It goes from scientific method to the full software stack. I have also shortly studied quantum computing, a new computing paradigm that might revolutionize a part of High Performance Computing. In my last year of thesis, I also focused on machine learning and LLMs, and why we can lose reproducibility when using top machine learning frameworks. I also included a bit of energy consumption in my research, as it has become an important concern for high performance computing.

My research focuses on the evolving domain of reproducibility in computational science, a fundamental pillar of scientific rigor. Reproducibility in research, especially in high-performance computing (HPC), is an essential component that impacts diverse scientific disciplines. My work spans across several domains, including stochastic simulations, quantum computing, and machine learning, with the overarching aim of addressing why reproducibility can be lost and how to prevent it, particularly in computational environments.

Publication list here.

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Research social media links

• Google Scholar
• ORCID