Theory of Distributed and Computing Systems
Group members
- Wojciech Jamroga, Ph.D., Professor - Head of the Group
- Andrzej Blikle, Ph.D., Professor (on leave)
- Włodzimierz Drabent, Ph.D., Professor
- Mateusz Kamiński, M.Sc.
- Michał Knapik, Ph.D. (on leave)
- Damian Kurpiewski, M.Sc.
- Marta Kwiatkowska, Ph.D., Professor
- Łukasz Maśko, Ph.D.
- Antoni Mazurkiewicz , Ph.D., Professor (on leave)
- Artur Męski, Ph.D. (on leave)
- Wojciech Penczek, Ph.D., Professor
- Teofil Sidoruk, Ph.D.
WWW: https://ztsrio.ipipan.waw.pl/
RESEARCH DOMAIN 
THEORY OF DISTRIBUTED AND COMPUTING SYSTEMS
Activities and interests of the group members center around the following topics:
- models of distributed and concurrent systems,
- temporal logics for concurrent systems,
- modelling of knowledge and belief,
- model checking of real time systems ann multi-agent systems,
- design and analysis of cryptographic algorithms,
- formal modeling of multiagent systems: paraconsistent models of multiagent systems including, modeling beliefs, reasoning and communication,
- local computations in graphs: problems of election, enumeration and negotiation.
The leading researchers working on the above subjects: W. Jamroga, W. Penczek.
PROJECTS:
- Social Explainable Artificial Intelligence (SAI; CHIRT-ERA)
- Socio-Technical Verification of Information Security and Trust in Voting Systems (STV; PolLux)
- Verification of Voter-Verifiable Voting Protocols (VoteVerif; PolLux)
- Verification of Parametric Time constrained Strategic Abilities of Agents acting under Incomplete Information (współpraca PAN-CNRS)
Cryptography
Group members
- Paweł Morawiecki, Ph.D., Associate Professor, Head of the Group
- Józef Pieprzyk, Ph.D., Professor
also Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
PhD student at TIBPAN
- Andrii Krutsylo, M.Sc.
Research Domain
Cryptography
Activities and interests of the group members center around the following topics:
- security and reliability of deep neural networks,
- cryptanalysis of block and stream ciphers, hash functions,
- symmetric cryptography algorithms design,
- authenticated encryption scheme.
Foundations of Artificial Intelligence
Group members
- Mieczysław Kłopotek, Ph.D., Professor, Head of the Group
- Piotr Borkowski, Ph.D.
- Szymon Chojnacki, Ph.D. (on leave)
- Krzysztof Ciesielski, Ph.D. (on leave)
- Dariusz Czerski, Ph.D.
- Eryk Laskowski, Ph.D. (on leave)
- Zbigniew Michalewicz, Ph.D., Professor (on leave)
also School of Computer Science, The University of Adelaide, Australia - Bartłomiej Starosta, Ph.D.
- Sławomir Wierzchoń, Ph.D., Professor (on leave)
WWW: https://zpsi.ipipan.waw.pl/
Research Domain
Foundations of Artificial Intelligence
Our team at the Artificial Intelligence Fundamental Research Laboratory has been conducting intensive research on the leading challenges of Artificial Intelligence (also called Computational Intelligence) for four decades. Artificial Intelligence (AI) is a branch of computer science that deals with solving problems for which there are no algorithmic solutions or they are computationally too complex. In this spirit, the Team participated in the development of a system for analyzing data on the health effects of the Chernobyl disaster, a system supporting the diagnosis of hand injuries, a system for distributed knowledge extraction from medical data, a system for pro-ecological optimization of the power supply of Polish power plant network, a system for assessing candidates for the pilot profession, the first Polish large-scale semantic internet search engine, consumer price development evaluation system and many others.
Research on specific applications of AI was coupled with the development of inference and learning theories for uncertain and incomplete information (including Bayesian networks and Dempster-Shafer theory), the development of optimization methods inspired by nature (including immune networks, herd, genetic and extreme optimization algorithms), methods of extracting knowledge from numerical data, text and hypertext (new algorithms for cluster analysis and classification, including in the field of graph spectral analysis, new methods for extracting relationships of hierarchical concepts and simple relationships from natural language texts) and others. Currently, the Team has undertaken the hottest and most important challenge of developing Explainable Artificial Intelligence (XAI) methods. XAI is a response to industry objections that artificial intelligence methods such as deep neural networks, evolutionary algorithms and other operate on the principle of a "black box", while only transparent methods are trusted by business. Our Team took on a particularly difficult challenge, i.e. achieving explainability in the field of cluster analysis of text documents, especially those clustered using spectral methods. The basic difficulty lies in the lack of a coherent axiomatic system for cluster analysis. What is more, grievvant, spectral methods detach the representation of clusters from the textual content of documents. Our achievements in this area include:
- An non-cotradictory axiomatic system, including, among others, k-means algorithm, which is the basis of spectral methods,
- Classification method based on Laplacian spectra of document sets,
- Incremental clustering method based on the above-mentioned spectra,
- Clustering method based on kernelization of the similarity matrix,
- A method of explaining hashtags by hashtags based on the above-mentioned spectra,
- Method of assigning text labels to groups from spectral clustering,
- Explaining the nature of the kernel k-means clustering results for non-Euclidean spaces,
- Deepen understanding and selection of non-dominated solutions in nature-inspired optimization systems,
- And other.
Statistical Analysis And Modelling
Group members
- Szymon Jaroszewicz, Ph.D., Professor, Head of the Group
- Stanisław Bylka, Ph.D., Associate Professor (on leave)
- Łukasz Dębowski, Ph.D., Associate Professor
- Dariusz Kalociński, Ph.D.
- Małgorzata Łazęcka, Ph.D. (on leave)
- Stanisław Matwin, Ph.D., Professor,
also Institute for Big Data Analytics, Dalhousie University, Canada - Jan Mielniczuk, Ph.D., Professor
- Krzysztof Rudaś, Ph.D.
- Paweł Teisseyre, Ph.D., Associate Professor
PhD student at TIBPAN
- Tomasz Klonecki, M.Sc.
- Adam Wawrzeńczyk, M.Sc.
WWW: {External link (open in new window): } https://zams.ipipan.waw.pl/
Research Domain
Statistical Analysis and Modelling
The members of the group conduct research on generalizations of well-established methods of machine learning to the case of uplift modelling which concerns modelling of causal influence of a given action (e.g. marketing campaign, medical therapy) at the level of an individual by taking into account control group not subjected to the action. The theory of of linear models for the uplift case is also being developed.
The domains researched by the group include information theoretic and probabilistic modelling of a natural language. Objects of a special interest here are discrete stochastic processes with strong dependence which is measured by the rate of increase of a block entropy and a length of a maximal repetition. Such processes exhibit certain statistical properties which are close to those found in natural language productions, e.g. related to fulfilling Hilberg hypothesis. Their construction is studied as well as statistical inference for them with applications in computational linguistics.
Subsequent research direction concerns classification methods for multivariate response variables. An intensively studied special case is so-called multi-label classification when the response is multivariate variable with binary coordinates. Of a particular interest is construction of effective methods for high-dimensional data when high-dimensionality refers to large number of potential predictors as well as to dimensionality of the response. The aim of the research is development of algorithms (as well as theoretical analysis of their performance) for variable selection and prediction in this set-up.
Variable selection is also studied for high-dimensional generalized linear and additive models. Here, we study two- and multi-step procedures in which selection is executed based on information criteria after performing preliminary screening and/or ranking of the variables pertaining to values of their importance measures. The measures are constructed based on large number of small models with randomly chosen predictors. The main results concern selection consistency when assumed model for data at hand is correctly specified. The analogous problem is also studied for the misspecification case with the concept of selection consistency suitably modified.
Research concerning modelling stochastic dependence using copula-based approach is also being pursued by the group.
For more information, please visit: http://zams.ipipan.waw.pl/.
Computational Biology
Group members
- Michał J. Dąbrowski, Ph.D., Head of the Group
- Michał Dramiński, Ph.D.
- Marta Jardanowska, M.Sc.
- Henryk Jan Komorowski, Ph.D., Professor
also Bioinformatics Laboratory, Department of Cell and Molecular Biology, Uppsala University, Professor - Małgorzata Perycz, Ph.D.
PhD student at SMM Med.Univ. of Lodz
- Aleksandra G. Bilska, M.Sc.
WWW: {External link (open new window): }https://zbo.ipipan.waw.pl
Research Domain
Computational Biology
Motto:
What mathematics did to physics, computer science will do to biology
(Stanisław Ulam, 1975)
Computational Biology Group (CBG) is a unit in the Department of Artificial Intelligence.We focus on the functions of non-coding DNA regions that may lead to detecting regulatory disorders that disrupt biological pathways. To better understand the development of various diseases, we study variation in the genomic, epigenomic, proteomic and other -omic layers of regulation of gene expression. We employ multidisciplinary knowledge, including statistics, mathematical modeling, machine learning, programming, Big Data analysis, parallel computing, biochemistry, ecology, evolution, and molecular biology, to discover the mechanistic structure of a wide range of biological issues. In CBG, we combine the achievements of a leading computer science institute with the recent advances of biotechnologies applied to Life Sciences. We offer an interdisciplinary agora for biologists, statisticians, linguists, oncologists and computer scientists.
Our research interests include:
- Developing methods for analyzing and modeling large data sets generated by modern biotechnologies,
- Analysis of biological data, both own and publicly available,
- Epigenetics, with focus on DNA methylation in cancer and civilization diseases.
We developed a system for selecting and ranking features in classification tasks using decision trees and a Monte Carlo method (MCFS-ID) - rmcfs; a system for constructing classifiers (ROSETTA) based on Pawlak’s rough sets - R.ROSETTA, and a DNA methylation analysis toolkit CytoMeth. Our further work on methods focuses on finding interdependencies between significant features (implemented in the MCFS-ID system) and developing methodologies for rule networks generated from rough set models. In the field of bio-data analysis CBG has made several significant contributions to modeling the pathogenicity of the bird flu virus and in research on mutations in regulatory regions of the genome and their correlation with carcinogenesis.
The results of our work on the regulation of gene expression in glioma is an atlas of regulatory regions in the human brain (transcription regions, transcription factor binding sites, enhancers, chromatin structure and histone modifications) ["Mapping chromatin accessibility and active regulatory elements reveals pathological mechanisms in human gliomas" - article in Natural Communications, 2021], constructed in cooperation with the Nencki Institute of the Polish Academy of Sciences and the Institute of Informatics of the University of Warsaw, financed by the National Science Center Symfonia 3 grant. We continue research on transcriptional regulation in glioma, focusing on the role of the REST and KAISO transcription factors.
As part of our research on carcinogenesis, we identify possible regulatory networks composed of epigenetic features important in predicting the formation and development of breast cancer. Other projects carried out in CBG include the analysis of population and molecular biology data, such as signaling pathways related to carcinogenesis and DNA methylation in Rheumatoid Arthritis.
CBG leads Bioinformatics course at the Doctoral School of Information and Biomedical Technologies of the Polish Academy of Sciences (TIB PAN)
For more information, please visit: http://zbo.ipipan.waw.pl/.
Formal Linguistics
Group members
- Adam Przepiórkowski, Ph.D., Professor, Head of the Group
- Diego Feinmann, Ph.D.
- Agnieszka Patejuk, Ph.D.
- Sebastian Zawada, M.Sc.
Research Domain
Formal Linguistics
The Formal Linguistics Group (FLG) studies the syntactic and semantic structure of natural languages, employing corpus-based, computational, experimental and formal methods.
FLG members have carried out work that has resulted in the creation of important linguistic resources for Polish, including the National Corpus of Polish (NKJP), the Walenty valence dictionary, and one of the Polish corpora within the Universal Dependencies initiative (UD-LFG). More recent computational work concerns syntactic and semantic processing of texts based on the theoretical assumptions of Lexical Functional Grammar and Glue Semantics.
An important area of the team's activity is theoretical work on the phenomenon of coordination. Within this thread, it has been shown that conjoined constituents may differ in syntactic categories, in grammatical cases, and in grammatical functions. Work in this area is based on advanced use of linguistic corpora and on experimental methods. Formal syntactic analyses of coordinate structures are carried out within the frameworks of Lexical Functional Grammar, Head-driven Phrase Structure Grammar, Minimalist Program, and within dependency approaches, while semantic analyses are based on the concepts of polyadic quantification and event semantics.
Formal semantic and pragmatic methods are also employed in work on various aspects of quantification in natural languages and on the phenomena of vagueness, homogeneity and oddness in language.
FLG members have also worked on the argument structure (so-called, valence) of predicates. A practical result of this thread is the Polish valence dictionary Walenty – developed together with members of the Linguistic Engineering Group – which contains detailed syntactic, semantic and phraseological information about the argument structure of verbal, nominal, adjectival and adverbial lexemes. Theoretical work on the argument–adjunct dichotomy is also related to this thread.
Currently, intensive work is being carried out on the multifunctional Polish word to. The aim of this work is to classify various uses of this word in texts and to delimit a set of lexemes with the lemma TO. Individual lexemes TO are subject to formal morphological, syntactic, semantic and pragmatic analysis.
FLG participates in international cooperation, including cooperation with scientists from the University of Oxford (Great Britain), the University of Konstanz (Germany) and from MIT (Massachusetts Institute of Technology, USA).
ZIL has been and is active in multiple national and international projects.
More information about the research activities of the team members can be found at: http://zil.ipipan.waw.pl/
Linguistic Engineering
Group members
- Łukasz Kobyliński, Ph.D., Head of the Group
- Anna Andrzejczuk, Ph.D. (on leave)
- Monika Borys, M.Sc.
- Anna Bulińska, Ph.D.
- Wiktoria Gumienna, M.Sc.
- Elżbieta Hajnicz, Ph.D., Associate Professor
- Witold Kieraś, Ph.D.
- Dorota Komosińska, M.Sc.
- Katarzyna Krasnowska-Kieraś, M.Sc.
- Martyna Lewandowska, M.Sc.
- Marek Łaziński, Ph.D., Professor
- Mikołaj Łąkowski, M.Sc. Eng.
- Małgorzata Marciniak, Ph.D., Associate Professor
- Dawid Mastalerz, M.Sc.
- Agnieszka Mykowiecka, Ph.D., Associate Professor
- Maciej Ogrodniczuk, Ph.D., Associate Professor
- Angelika Peljak-Łapińska, Ph.D.
- Sandra Penno, M.Sc.
- Piotr Przybyła, Ph.D. (on leave)
- Zuzanna Przybysz, M.Sc.
- Michał Rudolf, Ph.D.
- Piotr Rychlik, Ph.D.
- Aleksandra Tomaszewska, M.Sc.
- Aleksander Wawer, Ph.D.
- Marcin Woliński, Ph.D., Associate Professor
- Joanna Wołoszyn, Ph.D.
- Alina Wróblewska, Ph.D.
- Natalia Zawadzka-Paluektau, Ph.D.
- Aleksandra Zwierzchowska, M.Sc.
- Bartłomiej Żuk, M.Sc.
WWW: {Externnal link in new window: }http://zil.ipipan.waw.pl/
Research Domain
Linguistic Engineering
The Linguistic Engineering Group (Pol. Zespół Inżynierii Lingwistycznej; ZIL) deals with multiple aspects of Natural Language Processing.
Information extraction is a key area of the Group's research: numerous publications have addressed the automatic extraction of structured data from domain texts, named entity recognition, and shallow parsing in general. As a result of the Group's work, TermoPL was developed for extracting terminology from Polish texts. Additionally, a multilingual version, TermoUD, was created to process texts in any language with an available UD dependency parser. Related work includes the automatic acquisition of linguistic knowledge from corpus data, including valence frames and contributions to the Walenty dictionary.
The Group also works on text processing at the semantic level, focusing on word sense disambiguation, coreference resolution, discourse analysis, as well as sentiment and emotion analysis in text. More application-oriented research in this area involves automatic summarization and text categorization.
ZIL is also active in the area of corpus linguistics. The Group coordinated the development of the 1.5-billion-word National Corpus of Polish (Narodowy Korpus Języka Polskiego; NKJP), based to some extent on the earlier IPI PAN Corpus. In the process, the Group created various tools for manual and automatic corpus annotation at multiple linguistic levels, an XML schema for corpus annotation, and a manually annotated 1-million-word subcorpus. This subcorpus serves as the empirical basis for the Składnica and Polish Dependency Treebank. The latter is commonly used to train dependency parsing models for Polish (e.g.,COMBO, Trankit, Stanza, UDPipe). Moreover, both resources are used by the hybrid syntax-dependency parser Hydra.
Recent corpus work is related to the Corpus of Contemporary Polish, the Polish Parliamentary Corpus, and corpora of historical Polish. Notably, ZIL has collaborated on tagging and publishing the Electronic Corpus of 17th and 18th century Polish Texts (KorBa).
Many of the tools created by the Group are publicly available under open source licenses. These include LAMBO system for sentence and token segmentation, COMBO language preprocessing system, Morfeusz morphosyntactic analyzer, and Korpusomat web application for creating and analyzing text corpora.
ZIL also continues to develop the Grammatical Dictionary of the Polish Language, the basis of many taggers and other tools for processing Polish.
ZIL is an active participant in the CLARIN-PL and DARIAH-PL research infrastructure consortia and COST actions (also as a Grant Holder, currently in the UniDive action). The Group conducts numerous national and international projects. National funding sources include the National Science Center, the National Center for Research and Development, the Foundation for Polish Science, the National Agency for Academic Exchange and the National Program for the Development of Humanities. International projects are supported by programs such as CEF, Horizon 2020, and DIGITAL.
For more information, please visit: http://zil.ipipan.waw.pl/.