Our prizes, grants and other accomplishments.


for Students

See what we can offer you, as a Bachelor, Master of PhD level student.



Interested in our research? Please come in.



Come to our weekly meeting.



Take a quick tour through our team members.



Main research direction

We focus on various machine learning models and algorithms, with particular interest in hybrid models, connecting concepts from both classification and clustering methods.


every thursday, 14:00, room 1146

Group's seminar

Classification methods

We are investigating new models able to perform classification. Including hierarchical, support vector based and entropy based models.

Clustering methods

Our group develops new clustering algorithms, based mostly on information and compression theory.

Deep learning

GMUM is also actively working on deep learning paradigm (in the Hinton's sense), as we strongly believe that this is the future of machine learning.

Recent papers

Ociepka Michał, Przemysław Rola, Przemysław Spurek, Jacek Tabor, ICA based on asymmetry , PATTERN RECOGN (2017)
D Leśniak, S Jastrzębski, S Podlewska, WM Czarnecki, AJ Bojarski, Quo vadis G protein-coupled receptor ligands? A tool for analysis of the emergence of new groups of compounds over time, Bioorganic & Medicinal Chemistry Letters 27 (3), 626-631 (2017)
Krzysztof Byrski, Przemysław Spurek, Jacek Tabor, Active Function Cross-Entropy Clustering , EXPERT SYST APPL vol. 72 (2017), 49–66
Przemysław Spurek, General Split Gaussian Cross-Entropy Clustering , EXPERT SYST APPL vol. 68 (2017), 58–68
S Jastrzebski, D Leśniak, WM Czarnecki, How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks, arXiv preprint arXiv:1702.02170 (2017)
D Warszycki, M Śmieja, R Kafel, Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands, Molecular Diversity, 1-6 (2017)
Marek Śmieja, Magdalena Wiercioch, Constrained clustering with a complex cluster structure , ADV DATA ANAL CLASSI (2017), 26
P Spurek, J Tabor, P Rola, M Ociepka, ICA based on the data asymmetry, arXiv preprint arXiv:1701.09160 (2017)
S Podlewska, WM Czarnecki, R Kafel, AJ Bojarski, Creating the New from the Old–Combinatorial Libraries Generation with Machine Learning-Based Compound Structure Optimization, Journal of Chemical Information and Modeling (2017)
A Roman, K Zmitrowicz, Testowanie oprogramowania w praktyce: studium przypadkw, Warszawa: Wydawnictwo Naukowe PWN (2017)



Theory of missing data

prof. Jacek Tabor, dr Łukasz Struski, dr Marek Śmieja, dr Bartosz Zieliński
2016-2019, NCN OPUS 10

Clustering algorithm, which uses generalized Gaussian distribution and non-normal distributions

dr Przemysław Spurek
2016-2019, NCN SONATA 10

Detectors and descriptors of the key points based on the topological information

dr Bartosz Zieliński, dr Mateusz Juda
2016-2019, NCN SONATA 10

Algorithmical aspects of synchronization

dr Adam Roman
2016-2019, NCN OPUS 9

Application of neural networks in politology

dr hab. Igor Podolak (grant coordinated by dr hab. Łukasz Wordliczek)
2015-2018, NCN OPUS 8

Knowledge enriched sparse word embedding

lic. Stanisław Jastrzębski (under supervision of dr hab. Jacek Tabor)
2015-2018, MNiSW Diamentowy Grant 4

Minimal Memory Clustering Paradigm

dr hab. Jacek Tabor, dr hab. Igor Podolak, prof. dr hab. Andrzej Bojarski, mgr Przemysław Spurek, mgr Marek Śmieja, mgr Wojciech Czarnecki, mgr Sabina Smusz
2015-2017, NCN OPUS 7

Development of machine learning methods with applications to chemical compound activity prediction

mgr Marek Śmieja
2015-2017, NCN PRELUDIUM 7

A novel approach to de novo genome assembly problem based on UCT

mgr Ewa Matczyńska
2014-2017, NCN PRELUDIUM 6

The memory center

mgr Przemysław Spurek
2014-2017, NCN PRELUDIUM 5

Novel active learning querying strategy for the machine learning models

mgr Wojciech M. Czarnecki
2014-2017, NCN PRELUDIUM 5


Rector prize for scientific achievements

prof. Jacek Tabor, dr Wojciech Czarnecki, dr Przemysław Spurek
2015, UJ

Scholarship of Polish Minister Of Science

lic. Stanisław Jastrzębski
2015, MNiSW

Best paper award at CORES'15

mgr Wojciech Czarnecki and dr hab. Jacek Tabor

4th place in TRADESHIFT competition

lic. Stanisław Jastrzębski, Rafał Józefowicz (Google), mgr Wojciech Czarnecki

5th place in CONNECTOMICS competition

mgr Wojciech Czarnecki, Rafał Józefowicz (Google)

Second prize at the SMP competition

Stanisław Jastrzębski (under supervision of dr hab. Igor Podolak)

Best presentation at CISIM'13

mgr Przemysław Spurek



GMUM.r is an R package done in a close collaboration between team members and students. This is an unique occasion to make a first step into true science. We encourage all students to visit a section for students as well as contact Wojciech Czarnecki (project coordinator) in order to get more details.




Hierarchical Classifier with Overlaping Clusters is a novel ensemble model designed to work with problems with numerous output labels. It is being developed mainly by dr hab. Igor Podolak, polish speaking people may be interested in the new book describing this classifier in detail.



Cross Entropy Clustering is a novel clustering method developed by dr hab. Jacek Tabor and his PhD students. It finds a compromise between easy and efficient implementation (similar to the k-means) and strengths of the model based clustering (Expectation Maximization). We are currently working on the generalizations of this concept and applications in the classification.


Contact Us

Igor Podolak, PhD
Group of Machine Learning Research
Faculty of Mathematics and Computer Science
ul. Lojasiewicza 6
30-342, Cracow, Poland