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Tomáš Kliegr, Ph.D. is an associate professor in the Faculty of Informatics and Statistics of the Prague University of Economics and Business, where he works in the Data Mining and Knowledge Discovery group. 

University homepage | Office hours | Bio |DBLP| ResearchGate | Google Scholar| email: tomas(dot)kliegr(at)vse(dot)cz

Teaching (Winter 2023/2024)

• Information and knowledge processing (Machine learning 1)

• Programming for data science in Python

RuleML Webinars

I organize monthly RuleML Webinars. 

Last webinar: 

• 2023-08-01. Understanding Earth’s Ecosystems with Machine Learning. Marcin Joachimiak (LBL Berkeley USA), KBASE  (paper, system), KG-Microbe (paper, KG-Hub, github)

News 

• Sep 21, 2023. "Can variants, reinfection, symptoms and test types affect COVID-19 diagnostic performance? A large-scale retrospective study of AG-RDTs during circulation of Delta and Omicron variants, Czechia, December 2021 to February 2022" published in the ECDC Eurosurveillance journal.

• I co-organize 3rd International Workshop on Explainable and Interpretable Machine Learning (XI-ML), colocated in ECAI 2023 in Krakow

• April 22, 2023. Our paper QCBA: improving rule classifiers learned from quantitative data by recovering information lost by discretisation was published in Applied Intelligence. 

• Sep 28, 2022. We will present at the ILP/AAIP session of the 2nd International Conference on Learning and Reasoning our SWJ paper "Rdfrules: Making RDF rule mining easier and even more efficient".

• Sep 28, 2022. We will present at RuleML Challenge@DeclarativeAI 2022 our paper "High-Utility Action Rules Mining" 

• Sep 21, 2022. Interpretable Machine Learning (XI-ML) co-located with KI 2022, Sept. 21, 2022, Trier, Germany took place. http://www.cslab.cc/xi-ml-2022/ 

• Sep 7, 2022. Our paper on antigen testing published in Eurosurveillance was presented at the ECDC/WHO Europe Lab network meeting. 

• Aug 18, 2022. "Role of population and test characteristics in antigen-based SARS-CoV-2 diagnosis, Czechia, August to November 2021" published in the ECDC Eurosurveillance journal.

• July 15, 2022. "Explainability of Text Clustering Visualizations—Twitter Disinformation Case Study"  published in IEEE Computer Graphics and Applications ( Volume: 42, Issue: 4, 01 July-Aug. 2022)  

• July 17, 2022. Extended submission deadline for Interpretable Machine Learning (XI-ML) co-located with KI 2022, Sept. 21, 2022, Trier, Germany. http://www.cslab.cc/xi-ml-2022/ 

• June 2022. Our paper on Explainability of Text Clustering Visualizations was accepted in the IEEE Computer Graphics and Applications. 

• April 2022. Our paper "Why was this cited? Explainable machine learning applied to COVID-19 research literature"  resulting from cooperation with Dr. Joachimiak from Lawrence Berkeley National Laboratory is on-line first in Scientometrics.

• August 2021. I will present our work on review cognitive biases in machine learning at the International Joint Conference on Artificial Intelligence (IJCAI-21)

• August 2021. I will serve as a program committee member of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI'22)

• 17 June 2021. Our paper on rule mining from graphs appears in an issue of the  Semantic Web Journal. 

Václav Zeman, Tomáš Kliegr and Vojtěch Svátek. RDFRules: Making RDF Rule Mining Easier and Even More Efficient. Semantic Web, vol. 12, no. 4, pp. 569-602, 2021 http://www.semantic-web-journal.net/system/files/swj2398.pdf 

• I serve on the program committee of RuleML+RR 2021 (DeclarativeAI 2021). 

• February 17 2021. I was invited to present at the First international school on teaching and learning Machine Learning in Business Schools.

• February 10, 2021. The Artificial Intelligence journal published the on-line first version of our article: Tomáš Kliegr, Štěpán Bahník, Johannes Fürnkranz. "A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models".  The article is open access.

• Submission deadline: March 31, 2021. You are invited to submit to the Special Issue on Explainable and Interpretable Machine Learning and Data Mining of the Data Mining and Knowledge Discovery journal (DMKD, Springer). Guest editors:  Martin Atzmueller, Johannes Fürnkranz (editor-in-chief),  Tomáš Kliegr, Ute Schmid. 

• 8 September 2020. Proceedings or RuleML+RR are now on-line. https://www.springer.com/978-3-030-57976-0

• 21 August 2020. Our paper Editable Machine Learning Models? A rule-based framework for user studies of explainability will soon appear in Journal of Advances in Data Analysis and Classification (Springer) Preprint: https://nb.vse.cz/~klit01/papers/RuleEditor.pdf Update: paper is now on-line.

• Paper presenting Lukas Sykora's master thesis "Action Rules: Counterfactual Explanations in Python"  is the winner of the 14th Rule Challenge 2020 competition. Paper is freely available at http://ceur-ws.org/Vol-2644/. Lukáš joins our department as a PhD student.

• 1 February 2020. Our paper "Advances in machine learning for the behavioral sciences" appears in a printed issue of American Behavioral Scientist.  Preprint: https://arxiv.org/abs/1911.03249 

• 27 Dec 2019. Paper "Associative Classification in R: arc, arulesCBA, and rCBA" featuring my arc package was published in the R Journal. This is a joint paper with Michael Hahsler (Southern Metodist University) and Ian Johnson (Google),  authors of arulesCBA, and Jaroslav Kuchař (Czech Technical University), author of rCBA.

• 24 Dec 2019. Our paper "On Cognitive Preferences and the Interpretability of Rule-based Models." is on-line first in Machine Learning (Springer) - Open Access.

• I serve as program co-chair of RuleML+RR 2020@DeclarativeAI conference in Oslo.

• 22 Sep 2019. Our paper "Tuning Hyperparameters of Classification Based on Associations (CBA)" was presented at ITAT 2019 in Donovaly

• 19 Sep 2019 Our paper "PyIDS–Python Implementation of Interpretable Decision Sets Algorithm by Lakkaraju et al, 2016". received the best RuleML Challenge Award at RuleML+RR 2019 in Bolzano.