Find Academic Supervisors

• High-performance algorithms
• Parallel computing
• Hybrid multi-core computing
• +5 more

Dr. hab. Przemysław Stpiczyński is a university professor at the Institute of Computer Science and Mathematics , Maria Curie-Skłodowska University (UMCS), Lublin, Poland. He holds a habilitated doctorate in technical sciences (computer science) from Silesian University of Technology (2010) and a PhD in mathematics from UMCS (1995). As Director of the Institute and Head of the Department of Information Systems, he focuses on high-performance computing and algorithmic innovation. Education: Habilitated Doctor of Technical Sciences in Computer Science (2010), Silesian University of Technology PhD in Mathematics (1995), Maria Curie-Skłodowska University Master of Science in Mathematics (1989), Maria Curie-Skłodowska University Research Interests span the design and implementation of high-performance algorithms for parallel computers and clusters, software adaptation for hybrid multi-core architectures, multi-agent systems, numerical methods, and geoinformatics. His work emphasizes optimizing computational efficiency and data processing techniques. Professional Roles include directing the Institute of Computer Science and Mathematics and leading the Department of Information Systems. He has maintained active academic engagement through consultations, publications, and institutional leadership.

• Computer Science
• Parallel Computing
• Matrix Algorithms

Emilia Bober, M.Sc. Eng., is an Assistant Professor at the Department of Computer Science Basics within the Faculty of Mathematics, Physics and Computer Science at Maria Curie-Skłodowska University. Her research focuses on computer and information sciences, with expertise in parallel computing and matrix algorithm optimization. Her scientific work includes publications documented in the university repository, though specific titles are not listed here. She maintains active engagement in academic activities and offers consultations to students on Tuesdays (15:00-16:00) and Thursdays (19:00-20:00) in room D314.

Mariusz MatuszekView profile

Assistant Professor

• High-Performance Computing
• Parallel Computing
• Energy Efficiency in Computing Systems
• +3 more

Mariusz Matuszek serves as an Assistant Professor at Gdańsk University of Technology within the Department of Computer Systems Architecture, Faculty of Electronics, Telecommunications and Informatics. His research focuses on energy-efficient computing systems and high-performance parallel architectures. His primary research interests span High-Performance Computing , GPU Acceleration , and Energy Efficiency in Computing Systems . Current work examines power-capped optimization for deep neural network training, hardware/software energy measurement methodologies for CPU+GPU systems, and resource-aware problem formulations using ILP, greedy algorithms, and evolutionary approaches. His research bridges theoretical optimization with practical implementation in multi-GPU environments. Analysis of his 2022-2025 publications reveals strong emphasis on measuring and optimizing energy consumption in parallel computing systems, with particular focus on deep learning workloads. Key methodologies include professional hardware metering (Yokogawa WT-310E), Intel RAPL, and NVIDIA NVML interfaces across multi-GPU architectures. His work demonstrates significant performance-energy improvements through strategic power capping. Matuszek actively contributes to computer science education through curriculum development in parallel programming using MPI, OpenMP, and CUDA frameworks. His teaching case studies address the growing demand for HPC expertise in modern machine learning infrastructure development.

Jan DobrosolskiView profile

Lecturer

• Energy-Efficient Computing
• Deep Learning
• Parallel and Distributed Systems
• +3 more

Jan Dobrosolski serves as an Assistant lecturer at the Department of Computer Architecture within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His academic work focuses on performance-energy optimization in high-performance computing systems, with particular expertise in power management constraints. His research spans energy-efficient computing methodologies across multiple domains: deep learning acceleration, natural language processing tokenization, and reinforcement learning applications. Key specializations include power capping techniques for multi-core CPUs and multi-GPU architectures, neural network training optimization, and logic-based agent development using declarative programming frameworks. Dobrosolski's 2024 publications reveal a cohesive research trajectory centered on sustainable computing practices. His work systematically examines energy-performance trade-offs in tokenizer algorithms, deep neural network training, and neural agents for logic tasks—all under controlled power constraints. This integrated approach demonstrates significant contributions to resource-efficient computing across both CPU and GPU hardware platforms. Contact: jandobro@pg.edu.pl

Mariusz UchrońskiView profile

Professor

• Discrete Optimization
• High-Performance Computing (HPC)
• Parallel Algorithms
• +3 more

Professor Mariusz Uchroński is affiliated with Wrocław University of Science and Technology (WUST) as a faculty member in the field of discrete optimization and high-performance computing. His research focuses on advanced computational methods including parallel and distributed algorithms, quantum computing, and task scheduling optimization. Research Interests: He specializes in developing efficient algorithms for complex computational problems, with applications in high-performance computing environments and emerging quantum computing paradigms. His work addresses optimization challenges in parallel/distributed systems and task scheduling methodologies. Contact Information: Email: mariusz.uchronski@pwr.edu.pl Phone: +48 71 320 2456 Office: Z. Janiszewskiego Street 11/17, Wrocław, building C-3, room 214 Consultations: Mondays 16:00-18:00

Jacek BłażewiczView profile

Professor

• Scheduling Theory
• Operations Research
• Bioinformatics
• +7 more

Jacek Błażewicz is a distinguished Professor at Poznan University of Technology, affiliated with the Institute of Computing Science. He is a member of the Polish Academy of Sciences (since 2002), IEEE Fellow (2013), and has held numerous prestigious positions including Vice President of IFORS (2014-2019). His educational background includes: B.Sc. in Control Engineering (June 1973) M.Sc. with distinction in Control Engineering (June 1974) Ph.D. in Computer Science (November 1977) Dr. habil. in Computer Science (May 1980) Professor Błażewicz is a world-renowned expert in scheduling theory, operations research, and computational biology. His research bridges theoretical computer science with practical applications in bioinformatics, particularly in DNA sequencing, RNA structure analysis, and protein structure prediction. He has pioneered the application of combinatorial optimization techniques to solve complex problems in molecular biology. His recent publications (2023-2025) demonstrate continued leadership in both traditional scheduling theory and cutting-edge interdisciplinary research. While maintaining strong contributions to classical scheduling problems (late work minimization, machine scheduling), he has expanded into computational biology applications including RNA world modeling, microRNA analysis in cancer research, and DNA algorithms. His work increasingly integrates modern AI techniques like graph neural networks with traditional operations research methods. His numerous scientific awards include: Member of the Polish Academy of Sciences (since 2002) 8 Scientific Awards of the Ministry of Science, Higher Education and Technology (1978-2008) Gold Medal of the Association of the European Operational Research Societies (1991) Doctor Honoris Causa at the University of Siegen (2006) Copernicus Prize awarded by FNP and DFG (2012) IEEE Fellow (2013) Member of Academia Europea (since 2023) Member of The National Academy of Artificial Intelligence (since 2025) Throughout his career, Professor Błażewicz has supervised numerous students and researchers, contributing significantly to the development of young scientists in both computer science and computational biology. He has led multiple EU projects and grants, establishing collaborative networks across Europe. His work has resulted in influential editorial roles, including Series Editor of International Handbooks in Information Systems for Springer Verlag and membership on editorial boards of leading journals like IEEE Transactions on Industrial Informatics and Journal of Scheduling. He directs several research initiatives including the Laboratory of Algorithm Design and Programming Systems, the BioServer - Computational Biology Server, and the European Centre for Bioinformatics and Genomics. His RNApolis project represents a significant contribution to RNA structure analysis and prediction.

• Quantum Computing
• Machine Learning
• Optimization
• +3 more

Maryam Zomorodi Moghaddam is an Associate Professor in the Department of Computer Science at Cracow University of Technology's Faculty of Computer Science and Telecommunications. Her research focuses on quantum computing, machine learning, and optimization techniques, with applications in distributed systems and medical informatics. Research interests include: Quantum circuit optimization and teleportation Distributed quantum systems architecture Machine learning applications in healthcare Evolutionary computation and genetic algorithms Natural language processing using quantum techniques Recent publications demonstrate strong focus on quantum computing optimizations and AI medical applications, with quantum circuit efficiency and healthcare AI emerging as dominant themes. Notable contributions include distributed quantum gate protocols and medical classification systems using neuroevolution.

• Formal Methods
• Interactive Theorem Proving
• Proof Assistants
• +7 more

Chantal Keller is a Lecturer in the Computer Science Department at IUT d'Orsay, University of Paris-Saclay, and a member of the Formal Methods Laboratory (LMF), a joint unit involving CNRS and ENS Paris-Saclay. Her work bridges theoretical computer science and practical formal verification, with a strong focus on interactive theorem proving and proof assistant technologies. University: University of Paris-Saclay School: IUT d'Orsay Department: Computer Science Department Research Lab: Laboratoire Méthodes Formelles (LMF) Her research centers on enhancing the power and usability of proof assistants like Coq. She has developed tools such as SMTCoq , which integrates SMT solvers into Coq for automated reasoning, and HOLLIGHTCOQ , enabling interoperability between HOL-Light and Coq. Her PhD focused on formal proofs with SMT, and she continues to contribute to foundational aspects of type theory, lambda calculus, and logical frameworks. The 15 most recent articles reflect deep engagement with formal logic, programming language semantics, and automated reasoning. Key trends include proof interoperability, termination analysis, constraint programming, and the formalization of mathematical and computational systems in proof assistants. Her work spans theoretical developments and practical implementations in Coq, Agda, and OCaml. Chantal Keller has served on numerous program committees, including ITP, CPP, POPL, TAP, and JFLA, highlighting her active role in the formal methods community. She has held teaching and research positions since 2007, including roles at École Polytechnique and the University of Nottingham. 2025: Co-PC Chair, ITP 2022: Chair, JFLA 2021: Vice-Chair, JFLA; Co-Chair, PxTP 2017–2022: Coordinator, Digicosme UPSCaLe working group She has advised on workshops and school programs, including the EasyCrypt-F*-CryptoVerif school in 2014. She has not received any explicitly mentioned scientific awards in the provided texts. She is actively involved in teaching programming, algorithms, and mobile development at IUT d'Orsay.

• Computer Science
• Blockchain Technology
• Cyber-Physical Systems
• +3 more

Michał Horodelski is an Assistant Professor at the Department of Applied Computer Science, Faculty of Natural and Technical Sciences, John Paul II Catholic University of Lublin. His research focuses on blockchain technology, cyber-physical systems, and simulation tools, with applications in emergency medical services and healthcare informatics. He utilizes Petri nets and Scilab for modeling and verification. University: John Paul II Catholic University of Lublin School: Faculty of Natural and Technical Sciences Department: Department of Applied Computer Science Academic Rank: Assistant Professor Email: michal.horodelski@kul.pl Horodelski's research spans blockchain for decentralized authorization, smart contracts, and real-time systems in healthcare. His work includes applications of knowledge-based systems for EEG analysis and life-saving algorithms, emphasizing data synchronization and simulation. He has collaborated with institutions like Warsaw School of Economics and Wrocław University of Economics. His projects include methodology development for system behavior description in algebraic environments funded by the National Science Centre. Horodelski is a member of the European Centre for Information Society Technologies (ECIST) and contributes to open-source software adoption in academia.

• Mathematical Modeling
• Differential Equations
• Dynamical Systems
• +4 more

Maria Gokieli serves as a Lecturer at the School of Exact Sciences within the Faculty of Mathematics and Natural Sciences at Cardinal Stefan Wyszyński University in Warsaw. Her academic work focuses on theoretical and applied mathematical research with significant contributions to computational methods. Research Interests: Dr. Gokieli specializes in mathematical modeling with particular expertise in differential equations and dynamical systems. Her work spans applied mathematics, numerical analysis, and computational stability, with applications in parallel algorithms and finite element methods. Research keywords include Navier-Stokes equations, subdifferential calculus, and distributed systems. Publication Metrics: She has published 11 scholarly works with notable impact in her field. Her bibliometric indicators include an h-index of 3 according to Scopus citations and 2 from Web of Science, with a total SNIP score of 4.79 and ministerial score of 366. Her research appears across domains including computer systems, mathematical software, and signal processing. Professional Recognition: ORCID identifier: 0000-0002-8399-3196 Active Scopus profile with verified publications Academic Engagement: Dr. Gokieli maintains regular consultation hours during summer semester on Wednesdays from 1:15-2:45 PM in room 1227 (building 12) or via Microsoft Teams. Special arrangements are made for part-time students with advance email notification required by 4:00 PM the previous day.

Jan LudwiczakView profile

Researcher

• Structural Bioinformatics
• Protein Modeling
• Machine Learning
• +3 more

Dr. Jan Ludwiczak is a researcher at the University of Warsaw, specializing in structural bioinformatics and protein modeling. His work emphasizes machine learning, molecular dynamics simulations, and computational methods for protein design. He has collaborated with the Department of Protein Evolution at the Max Planck Institute for Developmental Biology (2020) and developed scientific software like DeepCoil, PiPred, and localpdb. Research focuses on sequence-structure-function relationships, coiled-coil domains, HAMP signaling, and cofactor specificity in Rossmann fold proteins. Active in distant homology detection using protein language models (pLM-BLAST) and studying neurodegenerative disease mechanisms (β-amyloid fibrils, Huntington’s disease). His publications highlight expertise in AlphaFold2 applications, molecular dynamics benchmarks, and biophysical characterization of novel proteins. He has received prestigious scholarships, including from the Polish Academy of Sciences, and contributed to tools like the Rossmann-toolbox for enzymatic engineering.

• Tomography
• Image Reconstruction
• Machine Learning
• +2 more

Robert Banasiak is Research Professor at Lodz University of Technology's Institute of Applied Informatics, specializing in industrial tomography and computational imaging. His research develops advanced algorithms for 3D electrical capacitance tomography (ECT) with applications in multiphase flow monitoring and process control. Recent innovations include machine learning classifiers for flow regime identification, GPU-accelerated reconstruction techniques, and 3D-printed sensor designs. Key research areas: Real-time 3D imaging for industrial processes Machine learning applications in tomography High-performance computing for image reconstruction Additive manufacturing of sensor systems Multiphase flow quantification Technological contributions include graph-based reconstruction methods, multi-GPU algorithms for tomographic processing, and development of hybrid ECT/ERT systems. Research addresses fundamental challenges in measurement resolution, computational efficiency, and sensor design for harsh industrial environments.

• Computer Science
• Pattern Matching
• Blockchain
• +2 more

Robert Susik is a researcher at the Institute of Applied Computer Science within the Faculty of Electrical, Electronic, Computer and Control Engineering at Lodz University of Technology. His core research focuses on algorithm design, particularly in pattern matching, blockchain applications, and machine learning implementations for healthcare technology. Research interests span: Computational efficiency in algorithm design Blockchain-based academic certification systems Neural networks for mobile health monitoring Human-computer interaction optimizations Recent publication trends demonstrate: Progressive shift toward applied machine learning (2021-2025) Consistent focus on optimization techniques in pattern matching Emerging work in blockchain and cryptocurrency applications Laboratory engagement includes computational algorithm development and interdisciplinary collaborations bridging computer science with healthcare technology.

Maciej MalawskiView profile

Associate Professor

• Parallel and Distributed Computing
• Cloud Computing
• Data Science
• +5 more

Maciej Malawski is an Associate Professor at the Department of Computer Science, AGH University of Science and Technology, and Research Team Leader at the Sano Centre for Computational Medicine in Kraków, Poland. He holds a PhD in Computer Science (2009) and MSc degrees in Computer Science (2001) and Physics (2004). His research focuses on parallel/distributed computing, cloud technologies, and biomedical applications, with contributions to federated learning, serverless computing, and scientific workflow optimization. Education: PhD in Computer Science, AGH University (2009) MSc in Physics, Jagiellonian University (2004) MSc in Computer Science, AGH University (2001) Research Interests: Parallel computing, cloud infrastructures, serverless systems, federated learning, biomedical data analysis, and workflow scheduling. His work bridges computational methods with healthcare, such as federated learning for medical imaging and sensitivity analysis in cardiovascular models. Publications & Awards: Over 50 international publications, including top-tier venues like SC, IPDPS, and IEEE Cluster. Recognitions include the 2018 AGH Rector’s Award, Publons Peer Review Award (2018), and 1st place in the Executable Paper Grand Challenge (2011). Labs & Affiliations: Director of Sano, senior researcher at ACC Cyfronet AGH, and collaborator with CERN. Leads projects on HPC/Cloud integration and scientific computing for medicine.

Aleksander ByrskiView profile

Professor

• Nature-inspired Computing
• Agent-based Distributed Computation
• Artificial Intelligence
• +3 more

Aleksander Byrski is a Full Professor at AGH University of Science and Technology, serving as Deputy Dean for Research and Cooperation in the Faculty of Computer Science, Electronics and Telecommunications. He is also an active member of the Council of Information Technology and Telecommunications and Deputy President of the Try IT Foundation. His research focuses on nature-inspired computing, agent-based distributed computation, artificial intelligence, and parallel/distributed computing and simulations. Byrski's work spans optimization algorithms, high-performance computing frameworks, and applications in urban traffic simulation, healthcare, and engineering. His research interests emphasize scalable agent-based systems, socio-cognitive metaheuristics, and their practical implementations in solving complex real-world problems. Key contributions include advancements in ant colony optimization, evolutionary multi-agent systems, and distributed computing paradigms. Byrski actively bridges theoretical computer science with applied domains such as traffic management, epidemic modeling, and smart city technologies. Articles from 2023–2025 highlight his focus on hybrid metaheuristics, socio-cognitive algorithms, and optimization for time-delay systems. Notable themes include improving algorithm efficiency through migration strategies, leveraging two-dimensional pheromone models, and exploring open/closed source frameworks for large-scale optimization. His work often addresses scalability challenges in HPC environments and real-time systems. Byrski has received no explicitly mentioned scientific awards but is recognized for his leadership roles in academic and industry collaborations. His advising and grants are not detailed here but reflect ongoing research in agent-based computing and functional programming paradigms. He contributes to interdisciplinary teams advancing computational intelligence and societal applications such as epidemic simulation and smart infrastructure.