Technical presentation on topological graph compression algorithms and their applications to distributed knowledge graphs and spatial transcriptomics datasets.
Internal research seminar presenting joint findings between the MCNC Lab and the Computational Biology Group.
On July 28, 2026, researchers from the BBQ Institute gathered for a joint technical seminar exploring Deterministic State Compression in Distributed Graph Computing.
Presented jointly by Dr Aleksander Zieliński (MCNC Lab) and Dr Mateusz Wójcik (CBMD Group), the seminar demonstrated how topological invariants derived from persistent homology can be used to dramatically reduce communication bottlenecks in distributed graph neural network training.
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.