{"id":10154,"date":"2026-02-10T15:19:11","date_gmt":"2026-02-10T14:19:11","guid":{"rendered":"https:\/\/pk.edu.pl\/?p=10154"},"modified":"2026-10-23T10:20:02","modified_gmt":"2026-10-23T08:20:02","slug":"prestigious-horizon-europe-grant-for-pk-krakow-university-of-technology","status":"publish","type":"post","link":"https:\/\/pk.edu.pl\/en\/prestigious-horizon-europe-grant-for-pk-krakow-university-of-technology\/","title":{"rendered":"Prestigious Horizon Europe Grant for PK&nbsp;Krakow University of&nbsp;Technology"},"content":{"rendered":"\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/pk.edu.pl\/wp-content\/uploads\/MOstojaStarzewskiANiemczynowiczRKycia-1024x576.jpg\" alt=\"The grant team including Agnieszka Niemczynowicz, PhD, Rados\u0142aw Kycia, PhD, and Prof. Martin Ostoja-Starzewski, all from the Faculty of Computer Science and Mathematics.\" class=\"wp-image-10155\" srcset=\"https:\/\/pk.edu.pl\/wp-content\/uploads\/MOstojaStarzewskiANiemczynowiczRKycia-1024x576.jpg 1024w, https:\/\/pk.edu.pl\/wp-content\/uploads\/MOstojaStarzewskiANiemczynowiczRKycia-300x169.jpg 300w, https:\/\/pk.edu.pl\/wp-content\/uploads\/MOstojaStarzewskiANiemczynowiczRKycia-768x432.jpg 768w, https:\/\/pk.edu.pl\/wp-content\/uploads\/MOstojaStarzewskiANiemczynowiczRKycia-1536x864.jpg 1536w, https:\/\/pk.edu.pl\/wp-content\/uploads\/MOstojaStarzewskiANiemczynowiczRKycia.jpg 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<p class=\"wp-block-pk-typografia-lead pk-lead has-text-align-justify\"><strong>Researchers from PK&nbsp;KUT will lead an&nbsp;international consortium that has been awarded funding under the prestigious Horizon Europe programme, within the Marie Sk\u0142odowska-Curie Actions (MSCA) scheme. Scientists from four continents and nine countries will collaborate on&nbsp;the project Hypercomplex Explainable AI: Mathematical Foundations for Trustworthy Models (HyperXAI), focusing on&nbsp;the development of&nbsp;theoretical frameworks for explainable artificial intelligence (XAI).&nbsp;<\/strong><\/p>\n<\/div>\n<\/div>\n\n\n<nav class=\"pkng-toc\" aria-label=\"Contents\"><p class=\"pkng-toc__title\">Contents<\/p><ul class=\"pkng-toc__list\"><li class=\"pkng-toc__item pkng-toc__item--h3\"><a href=\"#h-introducing-a-new-paradigm-hypercomplex-neural-networks\"><span class=\"pkng-toc__nr\">1.<\/span> Introducing a&nbsp;new paradigm: Hypercomplex neural networks<\/a><\/li><li class=\"pkng-toc__item pkng-toc__item--h3\"><a href=\"#h-from-fundamental-research-to-real-world-impact\"><span class=\"pkng-toc__nr\">2.<\/span> From fundamental research to&nbsp;real-world impact<\/a><\/li><li class=\"pkng-toc__item pkng-toc__item--h3\"><a href=\"#h-global-and-interdisciplinary-scientific-collaboration\"><span class=\"pkng-toc__nr\">3.<\/span> Global and interdisciplinary scientific collaboration<\/a><\/li><li class=\"pkng-toc__item pkng-toc__item--h3\"><a href=\"#h-european-funding-that-drives-excellence\"><span class=\"pkng-toc__nr\">4.<\/span> European funding that drives excellence<\/a><\/li><\/ul><\/nav>\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"has-text-align-justify wp-block-paragraph\">The innovative research, coordinated by&nbsp;researchers from the Faculty of&nbsp;Computer Science and Mathematics, moves beyond standard AI&nbsp;models operating in&nbsp;the field of&nbsp;real numbers and instead explores higher-order algebraic structures, including complex numbers, quaternions and Clifford algebras. The results may help shape the future development of&nbsp;AI-based technologies in&nbsp;fields such as&nbsp;medicine, industry, materials engineering and energy, while contributing to&nbsp;the creation of&nbsp;AI&nbsp;systems that are safe, transparent and socially acceptable.<\/p>\n\n\n\n<p class=\"has-text-align-justify wp-block-paragraph\">The project Hypercomplex Explainable AI: Mathematical Foundations for Trustworthy Models (HyperXAI) received over EUR 870,000 in&nbsp;funding and an&nbsp;excellent&nbsp;evaluation score of&nbsp;94.8%, placing CUT among the leading institutions coordinating cutting-edge research on&nbsp;the transparency and reliability of&nbsp;machine learning algorithms. The project is&nbsp;coordinated by&nbsp;Agnieszka Niemczynowicz, PhD, from the Faculty of&nbsp;Computer Science and Mathematics and is&nbsp;conducted within the Mathematics for Computer Science and Applications (M4CSA) research group, led by&nbsp;Rados\u0142aw Kycia, PhD. The grant team includes Agnieszka Niemczynowicz, PhD, Rados\u0142aw Kycia, PhD, and Prof. Martin Ostoja-Starzewski, all from the Faculty of&nbsp;Computer Science and Mathematics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Introducing a&nbsp;new paradigm: Hypercomplex neural networks<\/strong><\/h3>\n\n\n\n<p class=\"has-text-align-justify wp-block-paragraph\">Artificial intelligence is&nbsp;increasingly used to&nbsp;support decision-making in&nbsp;healthcare, energy systems, and security. Despite their growing effectiveness, many AI&nbsp;models remain insufficiently transparent. While explainability methods exist for classical, real-valued AI&nbsp;models, hypercomplex neural networks remain largely outside this mainstream.<\/p>\n\n\n\n<p class=\"has-text-align-justify wp-block-paragraph\">\u201c<em>Hypercomplex neural networks represent one of&nbsp;the most innovative directions in&nbsp;contemporary AI&nbsp;research<\/em>,\u201d explains Agnieszka Niemczynowicz, PhD, project leader. \u201c<em>They rely on&nbsp;advanced mathematical structures that allow for the natural modelling of&nbsp;multidimensional and geometric relationships, often with greater computational efficiency than classical neural networks. At&nbsp;the same time, there are currently no&nbsp;established theoretical frameworks or&nbsp;dedicated explainability metrics for such models<\/em>.\u201d<\/p>\n\n\n\n<p class=\"has-text-align-justify wp-block-paragraph\">The absence of&nbsp;formal explainability frameworks makes hypercomplex models difficult to&nbsp;control and verify, despite their significant potential. HyperXAI addresses this gap by&nbsp;developing mathematically rigorous foundations for explainability at&nbsp;the intersection of&nbsp;mathematics and computer science. As&nbsp;a&nbsp;result, explainability becomes a&nbsp;core design principle for next-generation AI&nbsp;systems.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>From fundamental research to&nbsp;real-world impact<\/strong><\/h3>\n\n\n\n<p class=\"has-text-align-justify wp-block-paragraph\">The HyperXAI project focuses on&nbsp;fundamental research, aiming to&nbsp;establish solid mathematical foundations for explainability in&nbsp;advanced AI&nbsp;models.&nbsp;The outcomes of&nbsp;the project will support the responsible use of&nbsp;hypercomplex neural networks in&nbsp;applications involving complex, multidimensional data, including medical imaging, biomedical signal analysis, materials science, industrial process monitoring and energy systems. In&nbsp;these areas, understanding which data features influence algorithmic decisions is&nbsp;essential. The project will lead to&nbsp;the development of&nbsp;new scientific tools, including analytical methods and quality metrics for explanations and evaluation protocols, enabling systematic comparison of&nbsp;models and their interpretability. Thanks to&nbsp;the open nature of&nbsp;the tools, datasets and software developed within HyperXAI, they will be&nbsp;available for further use and development by&nbsp;research teams worldwide.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Global and interdisciplinary scientific collaboration<\/strong><\/h3>\n\n\n\n<p class=\"has-text-align-justify wp-block-paragraph\">HyperXAI is&nbsp;implemented by&nbsp;an&nbsp;interdisciplinary international consortium led by&nbsp;PK&nbsp;Krakow University of&nbsp;Technology and involving institutions from Poland, Spain, the Czech Republic, Latvia, Azerbaijan, Turkey, Iraq, South Korea, and Brazil.<\/p>\n\n\n\n<p class=\"has-text-align-justify wp-block-paragraph\">\u201c<em>The challenge of&nbsp;explainable AI, especially in&nbsp;the context of&nbsp;hypercomplex neural networks, cannot be&nbsp;addressed within a&nbsp;single discipline<\/em>,\u201d notes Kycia, PhD. The project brings together experts in&nbsp;pure and applied mathematics, computer science and machine learning, as&nbsp;well as&nbsp;engineering disciplines, ensuring that theory, algorithms and application contexts evolve in&nbsp;parallel. As&nbsp;emphasised by&nbsp;Prof. Ostoja-Starzewski, interdisciplinarity is&nbsp;not an&nbsp;addition, but a&nbsp;prerequisite for success: mathematics without computer science would remain theory, computer science without mathematics would rely on&nbsp;heuristics, and both without engineering would lack responsible real-world applicability.&nbsp;HyperXAI brings these perspectives together into a&nbsp;coherent research process in&nbsp;which theory, algorithms and application contexts develop in&nbsp;close interaction.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>European funding that drives excellence<\/strong><\/h3>\n\n\n\n<p class=\"has-text-align-justify wp-block-paragraph\">The project is&nbsp;funded under the MSCA Staff Exchanges scheme, part of&nbsp;Horizon Europe, the European Union\u2019s&nbsp;flagship programme for research and innovation. According to&nbsp;the European Commission, in&nbsp;the MSCA Staff Exchanges 2025 call, 363 proposals were submitted and 81 were selected for funding. Among them, Cracow University of&nbsp;Technology is&nbsp;one of&nbsp;only three Polish universities coordinating a&nbsp;funded project. The success of&nbsp;HyperXAI builds on&nbsp;earlier interdisciplinary collaborations within the M4CSA group, including projects funded by&nbsp;the Polish National Agency for Academic Exchange (NAWA).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The HyperXAI consortium brings together the following institutions:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>PK&nbsp;Krakow University of&nbsp;Technology (Poland)<\/strong> \u2013 project coordinator<\/li>\n\n\n\n<li>State University of&nbsp;Campinas (Brazil)<\/li>\n\n\n\n<li>University of&nbsp;C\u00e1diz (Spain)<\/li>\n\n\n\n<li>University of&nbsp;M\u00e1laga (Spain)<\/li>\n\n\n\n<li>University of&nbsp;Ostrava (Czech Republic)<\/li>\n\n\n\n<li>Baku State University (Azerbaijan)<\/li>\n\n\n\n<li>Kocaeli University (Turkey)<\/li>\n\n\n\n<li>Igdir University (Turkey)<\/li>\n\n\n\n<li>University of&nbsp;Latvia (Latvia)<\/li>\n\n\n\n<li>Hanyang University Industry\u2013University Cooperation Foundation (South Korea)<\/li>\n\n\n\n<li>University of&nbsp;Kufa (Iraq)<\/li>\n\n\n\n<li>Ankara University (Turkey)<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>The innovative research, coordinated by&nbsp;researchers from the Faculty of&nbsp;Computer Science and Mathematics, moves beyond standard AI&nbsp;models operating in&nbsp;the field of&nbsp;real 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innovative research, coordinated by&nbsp;researchers from the Faculty of&nbsp;Computer Science and Mathematics, moves beyond standard AI&nbsp;models operating in&nbsp;the field of&nbsp;real 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