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

Przemysław Spurek, General Split Gaussian Cross-Entropy Clustering , EXPERT SYST APPL vol. 68 (2017), 58–68
Marek Śmieja, Dawid Warszycki, Average Information Content Maximization - a new approach for fingerprint hybridization and reduction , PLOS ONE vol. 11/1 (2016), e0146666
Krzysztof Misztal, Michał Podgórski, Przemysław Spurek, Jacek Tabor, Arkadiusz Tomczyk , Detection of Elongated Structures with Hierarchical Active Partitions and CEC-Based Image Representation , LECTURE NOTES IN COMPUTER SCIENCE vol. 403 (2016), 159-168
Szymon Nakoneczny, Marek Śmieja, Natural language processing methods in biological activity prediction , Proceedings of ECML PKDD Workshop on Machine Learning in Life Sciences (2016), 12
Wiesław Pałka, Przemysław Spurek, Clustering of Gaussian distributions , 2016 International Joint Conference on Neural Networks (IJCNN) vol. IEEE (2016), 3346--3353
Jakub Hyła, Krzysztof Misztal, Jacek Tabor, Optimal Ellipse Based Algorithm as an Approximate and Robust Solution of Minimum Volume Covering Ellipse Problem , LECTURE NOTES IN COMPUTER SCIENCE vol. 9842 (2016), 240-250
Przemysław Spurek, Jacek Tabor, Maximum Likelihood Estimation and Optimal Coordinates , International Conference on Systems Science, 2016 vol. Springer (2016), 3-13
Szymon Nakoneczny, Marek Śmieja, Jacek Tabor, Fast entropy clustering of sparse high dimensional binary data , Proceednigs of IEEE International Joint Conference on Neural Networks (IJCNN 2016) (2016), 2397-2404
Wojciech Czarnecki, Stanisław Jastrzębski, Damian Leśniak, Learning to SMILE(S) , International Conference on Learning Representations 2016 workshop (2016)
Konrad Kamieniecki, Krzysztof Misztal, Marek Śmieja, Przemysław Spurek, Jacek Tabor, , NEUROCOMPUTING (2016), 7



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