Intelligent Systems for Complex Data Research Group
We find patterns in data and mine information from complexity
Research & Research Areas
We are a team of researchers at Masaryk University in Brno, Czech Republic, specializing in complex data analysis. As part of the DISA laboratory and in close collaboration with the CERIT-SC centre, we aim to discover patterns within vast amounts of complex data.
We tackle unique challenges ranging from exploring patterns in images to the intricate analysis of complex biological structures such as proteins. Our ambition is to redefine the boundaries of effective and efficient processing of large datasets by leveraging our proficiency in machine learning, data mining, and clustering techniques.
We are constantly looking for new members, postdocs, students for bachelor's and master's theses and more!
We've introduced a Learned Metric Index – an index for complex, unstructured or high-dimensional data built as a structure of machine-learning models.
We develop our own application for searching in proteins by their structural similarity called AlphaFind.
Adhering to the Open Science principles, we publish our work without restrictions, and the Learned Metric Index Framework is available online on GitHub.
In our team, we strive to develop cooperation with other research teams from both Czechia and abroad. Our most prominent partners are:
Information Systems and Data Mining Research Group, CAU University of Keil, Germany. Together with Prof. Dr. Peer Kruger and his group, we investigate modern techniques for indexing, searching and solving (reverse) kNN data retrieval techniques.
Biological Data Management and Analysis Core Facility of the Central European Institute of Technology. Together, we work on the application of learned indexing to searching in protein structures produced by Alphafold.
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2024
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AlphaFind: discover structure similarity across the proteome in AlphaFold DB
Nucleic acids research, year: 2024, volume: 52, edition: July 5, DOI
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Scaling Learned Metric Index to 100M Datasets
17th International Conference on Similarity Search and Applications (SISAP 2024), year: 2024
2023
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Reproducible experiments with Learned Metric Index Framework
Information systems, year: 2023, volume: 118, edition: 1, DOI
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SISAP 2023 Indexing Challenge – Learned Metric Index
Similarity Search and Applications. SISAP 2023. Lecture Notes in Computer Science, vol 14289, year: 2023
2022
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Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques
Similarity Search and Applications, 15th International Conference, SISAP 2022, Bologna, Italy, October 5–7, 2022, Proceedings, year: 2022
2021
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Data-driven Learned Metric Index: an Unsupervised Approach
14th International Conference on Similarity Search and Applications (SISAP 2021), year: 2021
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Learned metric index - proposition of learned indexing for unstructured data
Information Systems, year: 2021, volume: 100, edition: 101774, DOI
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Metric hull as similarity-aware operator for representing unstructured data
Pattern Recognition Letters, year: 2021, volume: 149, edition: September 2021, DOI
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Organizing Similarity Spaces using Metric Hulls
14th International Conference on Similarity Search and Applications (SISAP 2021), year: 2021
Interested in Our Research? Join Us!
We are searching for new colleagues for various positions who would work with us on exciting projects, develop unique software and solve unconventional problems. In case of interest, contact is at dohnal(at)fi.muni.cz or antol(at)muni.cz.