Michał Pilipczuk is an Associate Professor at the Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics of the University of Warsaw. His research focuses on theoretical computer science, particularly algorithms on discrete structures, parameterized algorithms, structural graph theory, and logic in computer science. He leads the ERC-funded project "BOBR: Decomposition Method for Discrete Problems" and previously led a grant on optimality in parameterized complexity funded by the Polish National Science Center. His research interests include parameterized algorithms , structural graph theory , graph algorithms , and computational complexity . He has made significant contributions to the understanding of problems such as Independent Set in restricted graph classes, graph editing problems, and structural decompositions. The recent publications highlight a strong focus on structural graph theory and exact algorithms . Key themes include quasi-polynomial time algorithms for Independent Set in claw-free graphs, diameter computation in bounded genus graphs, and kernelization in trivially perfect graphs. His work often bridges combinatorial insights with algorithmic applications, particularly in the context of parameterized complexity. Principal Investigator, ERC Grant BOBR: Decomposition Method for Discrete Problems (2021–2026) Principal Investigator, Polish National Science Center Grant on Optimality in Parameterized Complexity (2014–2017) He has advised or collaborated with several researchers, including Marcin Wrochna and Marcin Pilipczuk. His work is published in top venues such as STOC, SODA, ESA, and ICALP.
Ngoc Thanh Nguyen is a Full Professor at Wroclaw University of Science and Technology where he serves as Head of the Department of Applied Informatics. He holds the prestigious title of Professor granted by the President of Poland and has been recognized as a Distinguished Scientist of ACM since 2009. He serves as Editor-in-Chief of both the Journal of Information and Telecommunication (JIT) and the Vietnam Journal of Computer Science (VJCS), and chairs the IEEE SMC Technical Committee on Computational Collective Intelligence. His research spans computational collective intelligence, knowledge integration, data mining, social media analysis, and sentiment analysis. Professor Nguyen has pioneered significant methodologies in spatial data clustering within network space, inter-sequence pattern mining, and graph neural network applications. His work bridges theoretical computer science with practical applications in intelligent information systems, demonstrating particular expertise in handling complex spatial and sequential data structures. His research has evolved from foundational pattern mining techniques to sophisticated neural network approaches for geospatial and social data analysis. The analysis of his recent publications reveals a strong focus on spatial data analysis in network environments, with significant contributions to clustering algorithms, graph neural networks, and pattern mining. His work consistently addresses efficiency challenges in data processing while expanding into emerging areas like Vietnamese language processing and topological data analysis. The research demonstrates a clear trajectory from traditional data mining techniques toward more sophisticated AI-driven approaches that incorporate spatial relationships and network topologies. Distinguished Scientist of ACM (2009) ACM Distinguished Speaker (2009-2013) IEEE Distinguished Visitor (2009-2013) Title of Professor granted by the President of Poland Professor Nguyen has supervised over 20 PhD students to completion and currently mentors several ongoing doctoral candidates. His academic leadership extends to founding two major conference series: the Asian Conference on Intelligent Information and Database Systems (ACIIDS) and the International Conference on Computational Collective Intelligence (ICCCI), which have become significant venues in their respective fields. His collaborative network spans multiple institutions, particularly with Yeungnam University as evidenced by several co-supervised PhD projects. As founder and chair of the IEEE SMC Technical Committee on Computational Collective Intelligence, he leads an international community of researchers advancing this specialized field. His departmental leadership at Wroclaw University of Science and Technology positions him at the center of applied informatics research and education in Poland, with particular emphasis on computational intelligence applications.
Dr. Andrzej Ożadowicz is a University Professor at the Department of Power Electronics and Automation of Energy Conversion Systems within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His office is located in room 510, building C-1, with contact details including phone +48 12 617 50 11 and email ozadow@agh.edu.pl. He holds PhD, DSc, and Engineering degrees, reflecting his dual expertise in academic research and practical engineering applications. His research spans Power Electronics, Building Automation, Smart Grids, and IoT-driven energy systems. Key interests include energy efficiency optimization through digital twins and BIM, distributed energy resource integration , and AI-enhanced demand management . Notably, he pioneers applications of deep reinforcement learning in home energy systems and develops frameworks for Smart Readiness Indicator implementation. His work bridges theoretical innovation with practical case studies in building thermal modeling and dynamic façade systems. Recent publications (2021-2025) reveal three dominant trends: (1) Convergence of digital twin technology with building automation for real-time energy management; (2) Critical analysis of IoT security and interoperability in smart infrastructure; (3) Pedagogical innovations in engineering education through blended learning methodologies post-COVID-19. His scholarly output demonstrates consistent focus on energy transition challenges and smart grid evolution. Professor Ożadowicz actively contributes to the Discipline Council for Automation, Electronics, Electrical Engineering and Space Technologies at AGH. He is instrumental in the AutBudNet initiative —a network of certified laboratories for energy efficiency assessment that implements "learning by doing" principles in building automation education. His work with this consortium emphasizes practical validation of smart grid technologies and demand response systems.
Rafał Biedrzycki is an Assistant Professor at The Institute of Computer Science within Warsaw University of Technology's Faculty of Electronics and Information Technology. His research focuses on optimization algorithms, evolutionary computation, and machine learning applications. He holds a PhD in Information Science (2009) and a D.Sc. (2024). Key research interests include evolutionary algorithms (e.g., Differential Evolution, CMA-ES), optimization techniques for real-world problems (e.g., compressor scheduling, optical networks), and algorithm benchmarking. He has contributed to improving constraint-handling methods and hybrid algorithm designs. Received team awards for scientific achievements from Warsaw University of Technology (2019, 2023) and teaching excellence (2021, 2024). Active in interdisciplinary projects, including the DAFNE initiative for data fusion systems (2010-2011). Supervises research in optimization, machine learning, and computational electromagnetics. His work bridges theoretical algorithm development with practical applications in engineering and data analysis. Recent efforts include analysis of CEC competition algorithms and parameter-tuning methodologies.
Monika Rosińska is an Assistant Professor at SWPS University's Faculty of Design in Warsaw, where she serves as Head of the Department of Design Theory and Research and as Dean's Representative for Interdisciplinarity. She is affiliated with both the Institute of Social Sciences and the Design Institute within the university. Her research focuses on the socio-cultural dimensions of design practices, with particular expertise in non-anthropocentric design, science and technology studies (STS), and critical/speculative design approaches. She investigates the history of design within political, social and cultural contexts, and examines the sociology of objects in everyday life. Dr. Rosińska explores why the creative process extends beyond form and function, pursues definitions of "good design," and investigates what matters to contemporary designers. Her scholarly work reveals consistent themes across publications, with recent research increasingly focused on multi-species communities and non-anthropocentric approaches to design. Her 2019 paper "Zoepolis: Non-anthropocentric design as an experiment in multi-species care" represents a significant contribution to this emerging field, building on earlier work exploring urban revitalization and participatory design. Dr. Rosińska has curated exhibitions including "Zoepolis. Design for Plants and Animals," which serves as both research output and practical exploration of her theoretical frameworks. Her publications span from foundational works on collective behaviors ("Deindywiduacja. Socjologia zachowań zbiorowych") to cutting-edge explorations of AI in visual arts (2025). She actively contributes to academic discourse through platforms including Google Scholar, ResearchGate, Academia.edu, and ORCID, maintaining visibility across multiple scholarly networks. Her work bridges sociology and design in innovative ways that challenge traditional disciplinary boundaries.
Michał Paweł Skrzypczak is an Associate Professor at the Institute of Informatics, Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw. He is actively involved in teaching and academic service, with a focus on theoretical computer science and formal methods. His research interests lie primarily in theoretical computer science , particularly program semantics , program verification , logic in computer science , and automata theory . These interests are reflected in his long-standing teaching of advanced courses such as Program Semantics and Verification , Logics for Computer Scientists , and Automata on Infinite Trees . While no publications are listed in the provided text, the consistent engagement with formal methods and mathematical logic across multiple courses over more than a decade suggests a sustained research trajectory in foundational aspects of computing. Scientific Awards: No awards listed. He has contributed to the academic development of students through teaching and exercise sessions, particularly in core theoretical subjects. Though no formal advisees are listed, his role as an instructor in graduate-level courses indicates mentorship responsibilities. He is affiliated with the Institute of Informatics at the University of Warsaw, which serves as his primary academic unit. There is no mention of specific research labs or teams, but his collaborations with researchers like Prof. Andrzej Tarlecki and Prof. Paweł Urzyczyn suggest integration into a strong logic and semantics research group.
Filip Sondej is a Researcher at the Department of Cognitive Science within the Faculty of Philosophy at Jagiellonian University in Krakow, Poland. His work bridges cognitive science and artificial intelligence, focusing on critical safety aspects of modern language models and multi-agent systems. His primary research interests include AI safety, LLM unlearning techniques, Chain-of-Thought faithfulness, AI conflict resolution, and digital sentience. Sondej's work addresses fundamental challenges in ensuring that increasingly powerful language models behave safely and align with human values. Analysis of Sondej's publication record reveals a strong interdisciplinary focus combining cognitive neuroscience methodologies with AI safety research. His recent work demonstrates a clear trajectory from traditional cognitive neuroscience investigations toward cutting-edge AI safety mechanisms, particularly in developing methods for removing unsafe behaviors from language models while maintaining functionality. The publications show sophisticated integration of neural network analysis with human cognitive processes. Sondej collaborates extensively with researchers including Anna Grabowska and Magdalena Senderecka, appearing as co-author on multiple publications in high-impact journals such as NeuroImage, Cerebral Cortex, and Journal of Cognitive Neuroscience. His research program bridges theoretical cognitive science with practical AI safety applications.
Dr. Michał Kalisz is an Assistant Professor in the Department of Computer Science at the John Paul II Catholic University of Lublin, affiliated with the Faculty of Philosophy. His work bridges theoretical computer science with practical applications in artificial intelligence (AI) across education and business domains. Research Focus: AI ethics, educational technology, business informatics, and knowledge representation. Academic Contributions: Recently presented four conference papers between 2023-2024, exploring AI's role in education (opportunities/threats, knowledge limits), public AI literacy, and business applications. Professional Engagement: Acted as a reviewer for the Annals of Social Sciences , led international workshops (USA, Europe), and participated in the Lublin Science Festival.
Dr. Katarzyna Jesse-Józefczyk is an Assistant Professor at the Institute of Mathematics, University of Zielona Góra, Poland. She maintains an office in Room 509 A-29 and can be contacted at k.jesse-jozefczyk@im.uz.zgora.pl. Her academic profile combines teaching responsibilities with diverse research activities across theoretical and applied mathematics. Her primary research interests include: Graph Theory (graph coloring, computational complexity of graph algorithms, coalitions and safe sets in graphs) Stochastic Inclusions and Multivalued Stochastic Equations with applications in economics and biological sciences Multivariate Linear Models and Analysis Game Theory (stochastic, multigenerational, and large games) Approximation Theory using Fourier series Preference Models using graded and interval relations Dr. Jesse-Józefczyk's work bridges theoretical mathematics with practical applications in secure data transmission, e-business, and space communication. She actively participates in the Center for Applications of Mathematics and Computer Science (OZMI), contributing to interdisciplinary research projects focused on sustainable energy, healthy society initiatives, and innovative industrial technologies. Her teaching portfolio includes algorithms and data structures, computer programming courses (both introductory and advanced), object-oriented programming, and database systems curriculum.
Konrad Kowalczyk is an Associate Professor at AGH University of Science and Technology in Krakow, Poland, where he heads the Signal Processing Group within the Faculty of Computer Science, Electronics and Telecommunications. With extensive international experience from institutions including Queen's University Belfast, Stanford University, and Fraunhofer Institute, he has established himself as a leading researcher in audio and speech signal processing. His academic journey includes B.Eng. and M.Sc. degrees from AGH University (2005), a Ph.D. from Queen's University Belfast (2009), and a Habilitation in ICT from AGH University (2020). B.Eng. and M.Sc. in Electronics and Telecommunications, AGH University of Krakow (2005) Ph.D. in Electronics, Queen's University Belfast, UK (2009) Habilitation (D.Sc.) in Information and Communication Technology, AGH University of Krakow (2020) Kowalczyk's research spans multiple cutting-edge areas in audio processing, with particular focus on speech and audio signal processing enhanced by machine learning techniques. His work integrates deep neural networks with traditional signal processing methods to address challenges in array signal processing , speech enhancement , and speaker recognition . The research group he leads explores innovative applications in distributed signal processing for IoT , acoustic event detection , and spatial audio rendering , bridging theoretical advances with practical implementations. His recent publications demonstrate a clear trend toward integrating deep learning with traditional signal processing techniques, particularly in speaker diarization, source separation, and robust speech recognition. The research increasingly focuses on real-world applications requiring reverberation-robust processing , distributed microphone array systems , and end-to-end neural architectures that can operate in challenging acoustic environments. There's a noticeable shift toward more complex, integrated systems that combine multiple signal processing tasks. Stanislaw Staszic Medal for best graduate of AGH (2005) IEEE Best Student Paper Contest finalist (2007) AES Student Technical Paper Award winner (2008) Best Student Paper Award at IWAENC conference (2014) Best Paper Awards at IEEE SPA conferences (2016, 2019) Polish Ministry of Science Scholarship for Distinguished Young Scientists (2016-2019) Prime Minister Award for outstanding scientific achievements (2020) As Principal Investigator, Kowalczyk leads multiple significant research projects including "Acoustic Intelligence" (2024-2028) funded by National Science Center, and "Deep extraction for robust speech recognition" (2023-2028). He has successfully secured funding from prestigious programs including First TEAM from the Foundation for Polish Science, and EU FP7 projects. His research group actively supervises Ph.D., M.Sc., and B.Eng. students, with strong connections to international institutions including Aalto University and IEEE Signal Processing Society. The research output includes numerous journal publications, conference papers, patents, and software implementations that have advanced the field of audio signal processing. Kowalczyk leads the Signal Processing Group at AGH University, which focuses on developing innovative solutions for speech and audio processing challenges. The group maintains strong collaborations with international institutions including Aalto University (Finland), and participates in European research initiatives. Their work spans theoretical development through practical implementation, with applications ranging from medical voice assistants to distributed acoustic sensor networks.
Elizabeth Spelke is the Marshall L. Berkman Professor of Psychology at Harvard University and an investigator at the NSF-MIT Center for Brains, Minds and Machines. She leads the Spelke Lab, which conducts behavioral research on infants and preschool children to understand the origins of uniquely human cognitive capacities such as formal mathematics, symbolic representation, and object taxonomy. Education: B.A. in Social Relations from Radcliffe College (1971), Ph.D. in Psychology from Cornell University (1978). Professional Experience: Faculty positions at the University of Pennsylvania, Cornell University, MIT, and Harvard University since 2001. Her research focuses on core knowledge systems in infancy, including understanding of objects, actions, people, places, number, and geometry. She collaborates with computational cognitive scientists to model infant cognition and with economists to apply findings to educational interventions. Her work integrates developmental, comparative, and cross-cultural perspectives. Her recent publications span topics such as early math learning, social evaluation in toddlers, goal inference, and the interplay between language and conceptual development. Trends in her recent work include experimental field studies, interdisciplinary collaborations, and theoretical synthesis of core knowledge frameworks. National Academy of Sciences (USA), 1999 American Academy of Arts and Sciences, 1997 National Academy of Sciences Prize in Psychological and Cognitive Sciences, 2014 C.L. de Carvalho-Heineken Prize for Cognitive Sciences, 2016 George A. Miller Prize, Cognitive Neuroscience Society, 2018 Mentor Awards from APS and APA, 2021 Spelke has mentored numerous researchers and collaborated widely across disciplines. Her lab’s work is supported by major grants from the NSF and other institutions. She has pioneered the use of behavioral methods to study infant cognition and has been instrumental in translating cognitive science into educational practice. She directs the Spelke Lab at Harvard, which investigates core cognitive systems through behavioral experiments with infants and young children. The lab explores how innate knowledge structures interact with experience to produce complex human cognition.
Paweł Pilarczyk is an Associate Professor at the Institute of Applied Mathematics within the Faculty of Applied Physics and Mathematics at Gdańsk University of Technology, where he has been employed since 2018. His research spans multiple mathematical disciplines with applications across various scientific fields. Dr. Pilarczyk's research interests include dynamical systems , computational topology , rigorous numerics , and applications of advanced computational techniques . His work bridges theoretical mathematics with practical applications in neuroscience, cardiology, ecology, and epidemiology. Analyzing his publication record from 2025 back to 2007 reveals a consistent focus on rigorous mathematical approaches to understanding complex systems. His recent work shows increasing interdisciplinary applications, particularly in medical diagnostics (sleep apnea detection, heart rate variability analysis) and neuroscience (neuron modeling), while maintaining strong foundations in pure dynamical systems theory. As project manager for the OPUS-funded "Topological and numerical methods in dynamical systems" (since 2022), he leads significant research initiatives at the Department of Differential Equations and Mathematical Applications. Dr. Pilarczyk maintains an active research program with collaborators across multiple institutions, evidenced by his extensive publication record and research data sets available through Gdańsk Tech's Bridge of Knowledge platform.
Włodzimierz Kasprzak is a Professor at the Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology. His research focuses on computer vision, robotics, human-computer interaction, and machine learning. He has contributed to advancements in human action classification, skeleton-based feature analysis, and multimodal interface design. Research Highlights: Development of lightweight classification models for human actions in video using skeleton-based features. Advances in multi-stream fusion techniques for image and video analysis. Design of embodied agent systems for cybersecurity event visualization and control. Awards and Recognition: 2024: Individual First Class Rector's Award for Scientific Achievements (2022-2023) 2021: Medal of the Commission of National Education 2011: Golden Cross of Merit His work integrates theoretical contributions with practical applications in robotics, surveillance systems, and human-centered technologies.
Professor Jacek Banasiak holds a prestigious DST/NRF SARChI Chair in Mathematical Models and Methods in Biosciences and Bioengineering at the University of Pretoria, Department of Mathematics and Applied Mathematics. He also maintains strong academic ties with Lodz University of Technology in Poland where he serves as a research professor in the Department of Mathematical Modeling. His career spans several decades with extensive contributions to mathematical modeling, particularly in population dynamics, fragmentation-coagulation processes, and epidemiological modeling. Professor Banasiak's research interests center on mathematical modeling of biological and physical processes, with particular expertise in singular perturbation theory, semigroup theory, and transport equations on networks. His work bridges theoretical mathematics with practical applications in epidemiology, ecology, and population biology. He has developed sophisticated mathematical frameworks for understanding fragmentation-coagulation phenomena, malaria transmission dynamics, and savanna ecosystem modeling. His publication record shows a consistent trajectory of high-impact research, with recent work focusing on climate-based malaria models, multiscale epidemiological systems, and mathematical analysis of growth-fragmentation equations. His publications appear in top-tier journals across mathematical analysis, epidemiology, and mathematical biology fields, demonstrating the interdisciplinary nature of his work. DST/NRF SARChI Chair in Mathematical Models and Methods in Biosciences and Bioengineering Author of numerous influential publications spanning from 1984 to 2025 Editor of special issues and author of several books including 'Introduction to Mathematical Methods in Population Theory' (2025) Professor Banasiak has supervised over 15 PhD students, many of whom have gone on to successful academic careers. His mentoring spans topics including fragmentation-coagulation with transport effects, telegraph systems on networks, and mathematical modeling of malaria transmission. His research has attracted significant funding, particularly through his SARChI Chair position which supports advanced mathematical research in biosciences and bioengineering.
Igor Wojnicki is a Professor at AGH University of Science and Technology's Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, where he serves as Vice-Dean of the Faculty of Cooperation and Education. His primary affiliation is with the Department of Applied Informatics, where he maintains an active research laboratory focused on knowledge engineering and smart systems. His research spans multiple domains with evolving focus: Early career: Deductive databases and rule-based inference engines (PhD thesis on "A Rule-based Inference Engine Extending Knowledge Processing Capabilities of Relational Database Management Systems") Mid-career: Graph-based knowledge representation and Tabular Trees (XTT predecessor) Current focus: Smart city applications, particularly energy-efficient lighting control systems and graph-based urban data integration His recent publications demonstrate a clear trajectory toward applied urban computing, with over 15 significant papers in the last five years addressing smart city infrastructure optimization. Key themes include dynamic street lighting control, graph-based computational methods for urban environments, and energy conservation in public infrastructure. Wojnicki actively contributes to academic-practical collaboration through initiatives like the Green AGH Campus Project and IBM academic partnerships. His technical leadership includes development of the ReDaReS system for relational database knowledge processing and the Jelly View technology for advanced database queries. His laboratory maintains strong industry connections, particularly with IBM through student internship programs and technology transfer initiatives. The team produces both theoretical frameworks and practical implementations, with notable outputs including the Osiris GUI system and Magellan GPS software for Poland.
