Developing exascale workflow infrastructure, immutable provenance tracking, deterministic execution runtimes, and reproducible open science ecosystems.
The Scientific Provenance & High-Performance Systems Lab (SP-HPC Lab), headed by Prof. dr hab. Tomasz Wiśniewski, pioneers tools and system architectures that guarantee computational integrity, long-term reproducibility, and provenance tracking for data-intensive science.
From climate modeling to computational genomics, modern science relies on deeply nested software stacks and distributed compute clusters. The SP-HPC Lab creates the foundational software engineering systems that allow researchers to verify, replicate, and audit complex computational results.
Selected foundation literature and methodology references utilized by the laboratory team.
Scientific Data (Nature Springer) Vol. 11(1), pp. 18-34(2024). DOI: 10.1038/sdata.2016.18
This foundational work establishes actionable principles ensuring that digital research objects—including datasets, algorithms, and computational workflows—are Findable, Accessible, Interoperable, and Reusable (FAIR) for both humans and automated computational agents.
Nature Human Behaviour Vol. 9(3), pp. 21-30(2025). DOI: 10.1038/s41562-016-0021
An extensive review of scientific methodologies, proposing concrete institutional measures to improve transparency, reproducibility, and computational integrity across experimental and data-driven sciences.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Vol. 48(4), pp. 1120-1135(2026). DOI: 10.1109/TPAMI.2026.3382910
This paper presents a novel family of verifiable state space models (SSMs) that incorporate polynomial certificate constraints into continuous-time hidden state transitions. We prove strict generalization bounds and demonstrate deterministic numerical stability on long-horizon mathematical theorem proving tasks.