Research Areas

The Department of Computer Science at the Faculty of Mathematics and Computer Science of Nicolaus Copernicus University in Toruń conducts research in theoretical and applied computer science, as well as in computational methods supporting data analysis and the solution of problems in contemporary science and technology. The Department’s research profile combines fundamental issues with areas of high application potential, developed through interdisciplinary collaboration and with due consideration of current technological trends. The scope of research at the Faculty includes, among others, logical aspects of computer science, computer modeling, parallel and distributed computing, distributed and parallel programming, and digital image processing. The educational offer of the Computer Science program also indicates a strong presence of artificial intelligence, cryptography, and data mining.

The Department’s main research areas include, in particular:

• logical and theoretical foundations of computer science, Petri nets, formal modeling
• multi-agent systems, strategic logic, formal verification
• software engineering, repository analysis, security
• parallel and distributed computing
• algorithms and data structures
• artificial intelligence and machine learning
• data analysis, signal processing, and medical applications
• databases and time series analysis
• computer science education and modern technologies in teaching

Research conducted in the Department is both fundamental and application-oriented. Its results are applied in the analysis of large data sets, intelligent systems, secure communication, information processing, and the design of modern software and computational solutions.

Publications included in the Computer Science in Education conference proceedings document the Department’s activity in the area of computer science education, covering issues related to computer science teaching, teaching methodology, digital competences, and the promotion of modern IT tools and content at various stages of education.

Current publications related to the research areas

The research results of the Department’s staff are published in international journals and in peer-reviewed proceedings of prestigious computer science conferences, including those indexed in the DBLP database. The publication output of the staff of the Department of Computer Science at the Faculty of Mathematics and Computer Science of Nicolaus Copernicus University in Toruń over the last ten years has focused on several main research areas. The strongest representation can be seen in formal methods and Petri nets, multi-agent systems and formal verification, software engineering and security, as well as parallel and distributed computing.

Logical and theoretical foundations of computer science, Petri nets, formal modeling

In this area, the work of Kamila Barylska, Łukasz Mikulski, Marcin Piątkowski, and Anna Gogolińska is particularly prominent, focusing on concurrency, reversibility of computation, and modeling using Petri nets.

• M. Koutny, Ł. Mikulski, M. Pietkiewicz-Koutny, An extension of the taxonomy of persistent and nonviolent steps.
• J. Fernandes, M. Koutny, Ł. Mikulski, M. Pietkiewicz-Koutny, D. Sokolov, A. Yakovlev, Persistent and Nonviolent Steps and the Design of GALS Systems.
• R. Janicki, J. Kleijn, M. Koutny, Ł. Mikulski, Relational structures for concurrent behaviours.
• R. Janicki, J. Kleijn, M. Koutny, Ł. Mikulski, Classifying invariant structures of step traces.
• R. Janicki, J. Kleijn, M. Koutny, Ł. Mikulski, Paradigms of concurrency: observations, behaviours, and systems: a Petri Net view.
• R. Janicki, J. Kleijn, M. Koutny, Ł. Mikulski, Relational structures for interval order semantics of concurrent systems.
• R. Janicki, M. Koutny, Ł. Mikulski, Interval traces with mutex relation.
• D. de Frutos Escrig, M. Koutny, Ł. Mikulski, Investigating reversibility of steps in Petri Nets.
• K. Barylska, A. Gogolińska, Acyclic and cyclic reversing computations in Petri Nets.
• E. Erofeev, K. Barylska, Ł. Mikulski, M. Piątkowski, Generating all minimal Petri net unsolvable binary words.
• J. Kleijn, M. Koutny, Ł. Mikulski, Reaction systems and enabling equivalence.
• R. Janicki, Ł. Mikulski, Algebraic structure of step traces and interval traces.
• D. de Frutos Escrig, M. Koutny, Ł. Mikulski, Reversing steps in Petri nets.
• D. de Frutos Escrig, M. Koutny, Ł. Mikulski, An efficient characterization of Petri net solvable binary words.
• A. Gogolińska, Ł. Mikulski, M. Piątkowski, GPU computations and memory access model based on Petri nets.
• M. Koutny, Ł. Mikulski, Encoding reaction systems in Petri nets.
• A. Męski, M. Koutny, Ł. Mikulski, I. Petre, W. Penczek, M. Piątkowski, Model checking for distributed reaction systems with temporal-epistemic properties.
• M. Kaniecki, Ł. Mikulski, On categorical approach to reaction systems.
• A. Męski, M. Koutny, Ł. Mikulski, W. Penczek, Reaction mining for reaction systems.
• K. Barylska, A. Gogolińska, Ł. Mikulski, A. Philippou, M. Piątkowski, K. Psara, Formal translation from reversing Petri nets to coloured Petri nets.
• A. Gogolińska, W. Nowak, Bipartite Graphs – Petri Nets in Biology Modeling.
• B. Aman et al., Foundations of reversible computation.
• K. Barylska, M. Koutny, Ł. Mikulski, M. Piątkowski, Reversible computation vs. reversibility in Petri nets.
• J. Kleijn, M. Koutny, Ł. Mikulski, G. Rozenberg, Reaction systems, transition systems and equivalences.
• K. Barylska, E. Erofeev, M. Koutny, Ł. Mikulski, M. Piątkowski, Reversing transitions in bounded Petri nets.
• K. Barylska, E. Best, E. Erofeev, Ł. Mikulski, M. Piątkowski, Conditions for Petri net solvable binary words.
• R. Janicki, J. Kleijn, M. Koutny, Ł. Mikulski, Step traces.
• K. Barylska, A. Gogolińska, R. Jakubowski, W. Nowak, Petri nets formalism facilitates analysis of complex biomolecular structural data.
• K. Barylska, F. Delaplace, A. Gogolińska, E. Pańkowska, Glucagon and insulin production in pancreatic cells modelled using Petri nets and Boolean networks.
• M. Kaniecki, M. Piątkowski, A modular Petri net model for the interlocking post control system.
• K. Barylska, A. Gogolińska, Ł. Mikulski, A. Philippou, M. Piątkowski, K. Psara, Reversing computations modelled by Coloured Petri Nets.
• A. Gogolińska, W. Nowak, OPOA: a Petri net generation algorithm for molecular dynamics analysis.
• K. Barylska, E. Best, U. Schlachter, V. Spreckels, Properties of plain, pure, and safe Petri nets.
• K. Barylska, E. Best, Properties of plain, pure, and safe Petri nets – with some applications to Petri net synthesis.

Multi-agent systems, strategic logic, formal verification

This research stream is strongly associated with the work of Wojciech Jamroga, Damian Kurpiewski, Mateusz Kamiński, Łukasz Mikulski, and their co-authors. It includes model checking, strategic logics, the analysis of multi-agent systems, and the formal study of electronic voting protocols.

• D. Kurpiewski, W. Jamroga, Y. Kim, Approximate verification of strategic abilities under imperfect information using local models.
• M. Kamiński, D. Kurpiewski, W. Jamroga, NatSTV: towards verification of natural strategic ability.
• W. Jamroga, M. T. Godziszewski, A. Murano, Strategies, credences, and Shannon entropy: reasoning about strategic uncertainty in stochastic environments.
• M. Kamiński, D. Kurpiewski, W. Jamroga, STV+KH: towards practical verification of strategic ability for knowledge and information flow.
• W. Jamroga, M. Knapik, D. Kurpiewski, Ł. Mikulski, Approximate verification of strategic abilities under imperfect information.
• D. Kurpiewski, M. Kamiński, W. Jamroga, STV+FLY: on-the-fly model checking of strategic ability in multi-agent systems.
• W. Jamroga, Ł. Maśko, Ł. Mikulski, W. Pazderski, W. Penczek, T. Sidoruk, D. Kurpiewski, Verification of multi-agent properties in electronic voting: a case study.
• W. Jamroga, Y. Kim, D. Kurpiewski, Scalable verification of social explainable AI by variable abstraction.
• Ł. Mikulski, W. Jamroga, D. Kurpiewski, Assume-guarantee verification of strategic ability.
• F. Adobbati, Ł. Mikulski, Analysing multi-agent systems using 1-safe Petri Nets.

Software engineering, repository analysis, security

In recent years, the output of Piotr Przymus, Jakub Narębski, Krzysztof Rykaczewski, and their co-authors has become especially visible in the areas of software quality, change analysis, vulnerabilities, and supply chain security.

• P. Przymus et al., HaPy-Bug: human annotated Python Bug resolution dataset.
• P. Przymus, M. Fejzer, J. Narębski, K. Rykaczewski, K. Stencel, Out of sight, still at risk: the lifecycle of transitive vulnerabilities in Maven.
• J. Narębski, M. Fejzer, K. Stencel, P. Przymus, PatchScope: a modular tool for annotating and analyzing contributions.
• P. Przymus, T. Durieux, Wolves in the repository: a software engineering analysis of the XZ Utils supply chain attack.
• P. Przymus, M. Fejzer, J. Narębski, K. Stencel, How I learned to stop worrying and love ChatGPT.
• P. Przymus, M. Fejzer, J. Narębski, K. Stencel, The Secret Life of CVEs.
• M. Fejzer, J. Narębski, P. Przymus, K. Stencel, Tracking buggy files: new efficient adaptive bug localization algorithm.
• K. Kaczmarski, J. Narębski, S. Piotrowski, P. Przymus, Fast JSON parser using metaprogramming on GPU.
• K. Kaczmarski, P. Przymus, Fixed length lightweight compression for GPU revised.
• J. Narębski, Mastering Git: attain expert-level proficiency with Git for enhanced productivity and efficient collaboration by mastering advanced distributed version control features.
• P. Przymus, K. Rykaczewski, J. Zieliński, Ł. Mikulski, Segmentation and process assignment of semi-structured event logs.

Parallel and distributed computing, HPC, Big Data

This area is represented primarily by Marek Nowicki and Łukasz Górski. The publications concern scalable computing in Java, the PCJ library, the PGAS model, and the processing of large data sets.

• M. Nowicki, M. Mroczek, D. Mukhedkar, P. Bała, V. N. Pimenoff, L. S. Arroyo Mühr, HPV-KITE: sequence analysis software for rapid HPV genotype detection.
• M. Nowicki, Ł. Górski, P. Bała, PCJ Java library as a solution to integrate HPC, Big Data and Artificial Intelligence workloads.
• M. Nowicki, Ł. Górski, P. Bała, Performance evaluation of Java/PCJ implementation of parallel algorithms on the cloud.
• M. Nowicki, Comparison of sort algorithms in Hadoop and PCJ.
• M. Chlebiej et al., Quality improvement of OCT angiograms with elliptical directional filtering.
• M. Nowicki, Ł. Górski, P. Bała, Performance evaluation of Java/PCJ implementation of parallel algorithms on the cloud (extended version).
• M. Burzańska, P. Wiśniewski, K. Stencel, Recursive queries: twenty-five years after SQL:1999.
• M. Nowicki, Ł. Górski, P. Bała, PCJ – Java library for highly scalable HPC and Big Data processing.
• M. Nowicki, D. Bzhalava, P. Bała, Massively parallel sequence alignment with BLAST through work distribution implemented using PCJ library.
• Ł. Górski, P. Bała, F. Rakowski, A case study of software load balancing policies implemented with the PGAS programming model.
• Ł. Górski, F. Rakowski, P. Bała, Parallel differential evolution in the PGAS programming model implemented with PCJ Java library.

Algorithms and data structures

Although this stream is less numerous than formal methods or multi-agent systems, it remains an important part of the Department’s research profile. Marcin Piątkowski’s publication record is particularly visible in DBLP in this area.

• J. Kärkkäinen, M. Piątkowski, S. J. Puglisi, String inference from longest-common-prefix array.
• H. Bannai, J. Kärkkäinen, D. Köppl, M. Piątkowski, Constructing and indexing the bijective and extended Burrows–Wheeler transform.
• H. Bannai, J. Kärkkäinen, D. Köppl, M. Piątkowski, Indexing the bijective BWT.
• J. Kärkkäinen, D. Kempa, M. Piątkowski, Tighter bounds for the sum of irreducible LCP values.

Data analysis, machine learning

This area includes research on data analysis, machine learning, and the modeling and forecasting of time series, both from methodological and application-oriented perspectives. Publications in this stream concern, among others, multidimensional data mining, feature selection, and the application of data science methods to real-world problems.

• D. D’Elia et al., Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.
• L. J. Marcos-Zambrano et al., A toolbox of machine learning software to support microbiome analysis.
• K. Niedzielewski et al., Forecasting SARS-CoV-2 epidemic dynamic in Poland with the pDyn agent-based model.
• L. J. Marcos-Zambrano et al., Applications of machine learning in human microbiome studies: a review on feature selection, biomarker identification, disease prediction and treatment.
• I. Moreno-Indias et al., Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions.
• P. Przymus, Y. Hmamouche, A. Casali, L. Lakhal, Improving multivariate time series forecasting with random walks with restarts on causality graphs.

Data analysis, signal processing, and biomedical applications

The Department’s output also includes publications demonstrating the application of computer science methods to the analysis of biological structures, signals, and medical data.

• M. Komorowski et al., ToFFi – Toolbox for frequency-based fingerprinting of brain signals.
• M. Chlebiej, A. Żurada, J. Gielecki, M. A. Pawlak, M. Szkulmowski, Customizable tubular model for n-furcating blood vessels and its application to 3D reconstruction of the cerebrovascular system.
• E. G. Gebregeorgis, J. Boniecka, M. Piątkowski, I. Robertson, C. B. K. Rathgeber, SabaTracheid 1.0: a novel program for quantitative analysis of conifer wood anatomy: a demonstration on African Juniper from the Blue Nile basin.
• M. Chlebiej, A. Rutkowski, A. Żurada, J. Gielecki, K. Polak-Boroń, Interactive CT/MRI 3D fusion for cerebral system analysis and as a preoperative surgical strategy and educational tool.
• M. Chlebiej, A. Żurada, J. Gielecki, CT/MRI 3D fusion for cerebral system analysis.
• M. Mroczek, M. Nowicki, D. Mukhedkar, V. N. Pimenoff, L. S. Arroyo Mühr, Assessing targeted viral detection between different metagenomic tools.
• M. Nowicki, M. Mroczek, D. Mukhedkar, P. Bała, V. N. Pimenoff, L. S. Arroyo Mühr, HPV-KITE for high-performance metagenomics data analysis.
• K. Tołpa et al., Spectral fingerprinting reveals gradients of frequency activity across brain regions.
• M. Mroczek, M. Nowicki, Ł. Górski, S. Arroyo Mühr, A. Ormenisan, P. Bała, High performance AI based tool for mining and classification of viral genomes in human exposome metadata.
• E. G. Gebregeorgis, J. Boniecka, M. Piątkowski, SabaTracheid 1.0: software for quantitative conifer wood anatomy analysis.

Databases and time series analysis

This area is less numerous in DBLP than the Department’s main activity streams, but it remains visible in publications related to query languages, data management, and time series analysis.

• M. Burzańska, P. Wiśniewski, K. Stencel, Recursive queries: twenty-five years after SQL:1999.
• M. Zimniak, B. Franczyk, M. Burzańska, P. Wiśniewski, Heuristic algorithm for periodic patterns discovery in a database workload reconstruction.
• M. Burzańska, Data model for rich time series data and Chameleon query language.
• M. Zimniak, M. Burzańska, B. Franczyk, On some heuristic method for optimal database workload reconstruction.
• M. Burzańska, P. Wiśniewski, How poor is the “Poor Man’s Search Engine”?
• R. Bocian, D. Pawłowska, K. Stencel, P. Wiśniewski, OpenMP as an efficient method to parallelize code with dense synchronization.
• I. Szulc, K. Stencel, P. Wiśniewski, Using genetic algorithms to optimize redundant data.
• A. Boniewicz, P. Wiśniewski, K. Stencel, Estimating costs of materialization methods for SQL:1999 recursive queries.
• M. Chlebiej, P. Habela, A. Rutkowski, I. Szulc, P. Wiśniewski, K. Stencel, Architectural challenges of genotype-phenotype data management.
• M. Burzańska, P. Krukowski, P. Wiśniewski, Extending DQL with recursive facilities.