Systems, Decisions, Innovation
We offer solutions to the most complex challenges of contemporary science and the economy.
Across the departments and centers of SRI PAS, we develop methods in artificial intelligence and computational intelligence, systems analysis, optimization, system modeling, and decision support under risk and uncertainty. From stochastic systems and reliability problems to environmental protection and economic‑financial modeling, we combine mathematics with computer science to solve real‑world problems.
About the Institute
SRI PAS
We understand complex systems and help people make better decisions
The Systems Research Institute of the Polish Academy of Sciences (IBS PAN) was established in 1976. For fifty years, it has been developing systems analysis — the art of modeling, optimization, and decision‑making wherever problems are complex, multifaceted, and burdened with uncertainty. We work with systems that cannot be easily described such as energy networks, environmental processes, financial markets, supply chains, and information flows. We combine advanced mathematics with modern computer science to extract practically useful knowledge from data that are incomplete, imprecise, and affected by uncertainty.Artificial intelligence is at the heart of our research
Artificial intelligence and computational intelligence are today among the most important research directions at IBS PAN. We develop machine‑learning methods, neural networks, evolutionary algorithms, multi‑agent systems, and fuzzy logic — a field whose Polish school was shaped largely here, around the work of Professor Janusz Kacprzyk.A particular strength of the Institute is artificial intelligence operating under uncertainty and imprecise information — that is, wherever data are incomplete, fuzzy, or expressed in natural language. This is one of the most challenging and most practical areas of contemporary computer science.
Our work on artificial intelligence does not end with theory. We build intelligent systems for searching and analyzing numerical, textual, and multimedia data; decision‑support systems; solutions for the Internet of Things; and data‑mining methods used in engineering, management, econometrics, and medicine.
This focus also returns in education: IBS PAN co‑runs the Doctoral School of Information and Biomedical Technologies at the Institutes of the Polish Academy of Sciences (TIB PAN), where artificial intelligence plays a leading role.
Our Research Teams
We conduct research in five scientific departments and in a specialized data‑analysis center. Each of these teams contributes its own perspective, and the most interesting results usually emerge where their expertise intersects. Our teams are recognized not only in Poland but also internationally.Intelligent Systems Department
Head: Prof. Janusz Kacprzyk
This team integrates fuzzy logic, evolutionary computation, and neural networks into intelligent decision‑support and control systems. It develops machine‑learning methods, intelligent information retrieval and processing, and the analysis of numerical, textual, and multimedia data. Key areas include multi‑criteria and group decision support, consensus building, multi‑agent systems, and solutions for the Internet of Things.Computer Modeling Department
Head: Prof. Zbigniew Nahorski
The team builds mathematical and computer models of static and dynamic systems in domains that combine technical problems—especially in the broadly understood energy sector—with economic, medical, and environmental issues. It uses statistical and fuzzy methods, intelligent agent systems, and efficient computational techniques involving multi‑criteria optimization and machine learning. Particularly important are methods for practical challenges such as integrated modeling of air‑pollution dispersion, emission inventories and uncertainty analysis for greenhouse gases, effective use of renewable energy, and computer‑based decision support in environmental policy.Modeling and Optimization of Dynamic Systems Department
Head: Prof. Jan Sokołowski
The team focuses on the mathematical foundations of control and optimization. It studies stability and sensitivity of optimal‑control problems for nonlinear dynamic systems, models and optimizes nonlinear elastic mechanical structures, and develops theoretical and numerical aspects of shape optimization. Its interests also include nonlinear boundary problems of parabolic‑hyperbolic type and vector optimization in partially ordered topological spaces. These studies provide a solid theoretical basis for many practical applications.Stochastic Methods Department
Head: Assoc. Prof. Maciej Romaniuk
The team develops probabilistic and statistical methods for decision‑making under risk, especially when knowledge is incomplete. It works on statistical quality control and reliability theory, modeling complex reliability systems, and stochastic processes with applications in financial mathematics. It also develops methods supporting medical decision‑making. A distinctive feature of the team is statistical inference for imprecise and fuzzy information—statistics for real‑world data.Decision Support under Risk Department
Head: Assoc. Prof. Janusz Miroforidis
The team creates methods and tools that help individuals and organizations make better decisions. It combines multi‑criteria optimization with game theory, supporting the analysis of cooperative situations and conducting negotiations through interactive computer procedures. It models socio‑economic processes and applies data‑analysis methods and graph theory to network problems, especially in logistics, transportation, and local and regional development.Center for Computational Methods of Data Analysis
Head: Prof. Piotr Kulczycki
The Center conducts research, consulting, and training in statistical data analysis, data mining, and modern information technologies used in engineering, management, econometrics, and biomedicine. It develops statistical systems of computational intelligence, decision‑support and control systems, models real‑world systems under uncertainty, and creates non‑parametric estimation methods—including for fuzzy and interval data.