Find Academic Supervisors

• Computer Architecture
• High-Performance Computing
• Network-on-Chip (NoC)
• +7 more

Vassilis D. Papaefstathiou is a researcher in computer architecture and high-performance computing, affiliated with the Department of Computer Science at the University of Crete and the Institute of Computer Science at FORTH-ICS. His work focuses on energy-efficient manycore systems, network-on-chip design, FPGA prototyping, and RDMA-based communication. His educational background includes a Ph.D. (2013), M.Sc. (2005), and B.Sc. (2002), all from the University of Crete. His research spans advanced topics in computer systems, including hybrid memory management, cache optimization, and scalable interconnects. His research interests include Computer Architecture , Network-on-Chip (NoC) , Reconfigurable Computing , Energy-Efficient Computing , RDMA , and High-Performance Computing . He has made significant contributions to FPGA-based prototyping of manycore systems and low-latency interconnects. His recent publications demonstrate a strong trend in optimizing on-chip networks, memory hierarchies, and communication mechanisms for performance and energy efficiency. Key areas include dual data-rate NoCs, hybrid memory systems with intelligent data migration, and RDMA-enhanced architectures. His work often integrates hardware-software co-design principles for scalable and efficient computing. HiPEAC Paper Award (2012) HiPEAC Paper Award (2016) HiPEAC Paper Award (2017) HiPEAC Paper Award (2020) Best Paper Award Finalist at IEEE/ACM NOCS 2018 He has advised several researchers and students, including Antonis Psistakis, Evangelos Vasilakis, and Ahsen Ejaz, who have co-authored significant publications with him. His collaborative projects include SARC, ExaNeSt, and ECOSCALE, often funded by EU initiatives or research councils. These projects focus on next-generation HPC systems, reconfigurable computing, and scalable architectures. He is a key contributor to the Formic FPGA prototyping platform and has worked extensively on RDMA-capable NICs, cache-integrated network interfaces, and virtualized communication systems. His work is deeply embedded in international research consortia and high-impact venues.

Paweł CzarnulView profile

Associate Professor

• Parallel and Distributed Processing
• High-Performance Computing
• Algorithm Optimization
• +3 more

Ph.D., D. Sc., Eng. Paweł Czarnul is an Associate Professor and Vice-Dean for Cooperation and Development at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology. His academic journey includes a PhD (2003) and habilitation (2015) from Gdańsk Tech, with additional management training at the University of Economics and Innovation in Lublin (2011-2012). He previously served as an assistant professor (2003–2016) and held a visiting research position at the University of Michigan (2001–2002). Currently, he leads the Department of Computer Architecture and holds roles in national scientific committees, including the TASK Supercomputing Centre Users’ Council and Ministry of Science advisory bodies. His research focuses on parallel/distributed processing, high-performance computing systems, algorithm optimization, and energy-efficient computational architectures. He has contributed to interdisciplinary projects and authored numerous scientific publications indexed on MostWiedzy, ResearchGate, and Google Scholar. Awarded the Bronze Cross of Merit and multiple Rector Awards for scientific and didactic achievements, Czarnul also advises on industrial PhD programs. Beyond academia, he is an avid wildlife photographer with a dedicated gallery showcasing nature from Arctic regions to European landscapes, and enjoys cycling, swimming, and bridge.

Haojin YangView profile

Professor

• Efficient AI
• Edge AI
• Deep model acceleration and compression
• +7 more

Dr. Haojin Yang is a Professor and group leader of the Multimedia and Machine Learning Research Group at the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany. He works under the supervision of Prof. Dr. Christoph Meinel and leads research in efficient AI, edge computing, and deep learning applications. His research group includes several PhD students and scientific coworkers focusing on cutting-edge machine learning problems. Dr. Yang's research interests span Efficient AI systems with emphasis on edge computing, binary neural networks, deep model acceleration and compression, computer vision applications, and multimodal data retrieval. His work bridges theoretical advancements with practical applications in medical imaging, weather forecasting, and digital humanities. He has made significant contributions to the field of binary neural networks through frameworks like BMXNet and BITorch. His recent publications demonstrate a strong focus on optimizing neural networks for efficiency, particularly in binary and low-bit implementations, while maintaining performance. His work spans computer vision, natural language processing, and multimodal learning, with particular attention to real-world deployment constraints like computational efficiency and energy consumption. BEST PAPER AWARD at the 22nd IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA 2024) for 'Low-Bit CUTLASS GEMM Template Auto-Tuning Using Neural Network' Dr. Yang actively supervises numerous PhD and Master's students, with several having completed their degrees and moved to prominent positions at companies like Amazon AI, German Aerospace Center, and MILA-Quebec AI Institute. His research is supported by collaborations with SAP, Wildenstein Plattner Institute, and various academic institutions. He also contributes to the academic community as a reviewer and program committee member for top conferences including ICML, NeurIPS, and CVPR.

• Computer Science
• Artificial Intelligence
• Expert Systems
• +3 more

Zbigniew Buchalski is a researcher at the Department of Computer Engineering, Faculty of Information and Communication Technology, Wrocław University of Science and Technology. His research focuses on computer science, artificial intelligence, expert systems, scheduling in multiprocessing systems, and digital circuit logic design. He holds a PhD and is affiliated with the university’s Department of Computer Engineering. His research interests emphasize the development of decision support systems, expert knowledge management, and algorithmic solutions for logistics and transportation optimization. He has contributed to projects addressing construction material selection, business management systems, and angling advisory systems using expert knowledge. Notable publications include works on decision systems for courier services (2012), heuristic scheduling algorithms (2007), and IT applications in document circulation (2015). His recent research (2019) explores expert systems in construction material selection and commercial project planning. Despite his extensive output, no scientific awards have been explicitly cited. Zbigniew is based at the Wrocław University of Science and Technology’s C-3 building, room 325, and holds office hours on Monday 9:15–11:15 and Tuesday 7:30–9:30. No current advisory or grant activities are documented.

Piotr ArłukowiczView profile

Lecturer

• 3D Graphics
• Scientific Visualization
• Parallel Computing
• +2 more

Piotr Arłukowicz is a Lecturer in Software Engineering at the University of Gdańsk, specializing in 3D graphics and computational visualization. He mentors students in graphics programming, animation technologies, and parallel computing frameworks. His applied research includes: Scientific visualization for STEM education High-performance computing architectures Special effects for film production Blender-based educational tools He develops interdisciplinary applications bridging computer science with physics and mathematics education through visual simulation techniques.

Ewa DeelmanView profile

Research Professor

• Scientific Workflow Management
• Distributed Systems
• Resource Provisioning
• +5 more

Ewa Deelman is a Research Professor in the Department of Computer Science at the University of Southern California (USC) and a Principal Scientist at the USC Information Sciences Institute. She also directs Science Automation Technologies, a research group focused on automating scientific processes through advanced workflow management solutions. Her work is foundational in enabling complex scientific computations across distributed computing environments. Deelman's research centers on scientific workflow automation, including resource provisioning, data management, and job scheduling in distributed systems. She pioneered workflow planning for distributed computations and leads the development of the Pegasus Workflow Management System. Current interests extend to anomaly detection using machine learning, quantum-classical hybrid workflows, and edge-to-cloud continuum integration. Her group's work bridges theoretical advances with practical tools for domain sciences like seismology and molecular dynamics. Analysis of her recent publications reveals a strong emphasis on enhancing workflow robustness through machine learning and emerging technologies. Key trends include LLM-based scheduling optimization, pandemic-driven resilience studies, and quantum workflow integration. Her work increasingly addresses cross-cutting challenges in data-intensive science, particularly in managing computational workflows across heterogeneous infrastructures from edge devices to cloud platforms. As Director of Science Automation Technologies, Deelman oversees a team that has made Pegasus a cornerstone tool in scientific computing. The system handles complex workflow orchestration for projects like CyberShake (seismic hazard analysis) and molecular dynamics simulations, automating data movement, execution, and error recovery across campus clusters, supercomputers, and cloud resources.

• Parallel Programming
• Distributed Systems
• Linux Systems
• +3 more

Maciej Pańczyk is a Lecturer at the Faculty of Electrical Engineering and Computer Science at Lublin University of Technology. He teaches courses such as 'Parallel and Distributed Programming' and 'Linux Daemon Programming,' emphasizing practical Linux system usage. His expertise includes distributed systems, operating systems, and software engineering education. He actively supports students with technical guidance on Linux installation, virtualization (VirtualBox), and course-related tools. His research interests extend to 3D digital technologies, as evidenced by projects like digital twin modeling for visually impaired individuals and participation in events like LubGame Conference 2025. Current projects include the 'Integrated Development Programme of Lublin University of Technology,' T1dcoach, Moodle enhancements, and collaborations with institutions like Eduroam and Visual Paradigm.

• Numerical Computation
• Interval Methods
• Parallel Programming
• +5 more

Dr. Bartłomiej Kubica is a Lecturer at the Vistula Academy of Finance and Business, affiliated with the Faculty of Computer Science, Graphics and Architecture. He holds a PhD in Computer Science from the Faculty of Electronics and Information Technology at Warsaw University of Technology (2006), where he also lectures. He concurrently teaches Real-time Systems at the Università degli Studi di Genova as part of the European Master of Robotics (EMARO) program. His research focuses on numerical computation (interval methods), parallel programming, probabilistic modeling, and real-time systems. He has organized mini-symposia on High Performance Computing and Interval Methods at PARA and PPAM conferences. Research Interests: Interval methods in numerical analysis Parallel and multithreaded programming Real-time systems optimization Queuing systems analysis Computer network architectures Teaching Affiliations: Main appointment: Vistula Academy Warsaw University of Technology Warsaw University of Life Sciences Università degli Studi di Genova (Italy)

Marek WodaView profile

Lecturer

• IT Security
• Cloud Computing
• E-Learning
• +4 more

Marek Woda, PhD, is an academic staff member at the Department of Computer Engineering within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His work bridges practical and theoretical aspects of modern computing systems, with a focus on cloud infrastructure, security, and e-learning technologies. His research interests include IT Security , Cloud Computing , E-Learning , Parallel Computing , Web Application Performance , Resource Allocation , and Heterogeneous Systems . These domains reflect both academic rigor and real-world applicability, particularly in scalable and secure computing environments. The recent publications highlight a consistent focus on optimizing cloud-based systems, analyzing performance trade-offs, and leveraging modern hardware architectures. Trends show a strong emphasis on cost-effectiveness in IaaS, responsiveness in SaaS applications, and innovative use of web browsers as platforms for high-performance computing. Marek Woda has not been listed with any scientific awards in the available text. He has collaborated on research projects resulting in peer-reviewed publications but no information is available regarding graduate student supervision or external research grants. His work is closely tied to the research activities of the Department of Computer Engineering, particularly in areas related to dependable and complex computer systems, as evidenced by frequent contributions to the international DepCoS-RELCOMEX conference series.

Cezary CzaplewskiView profile

Professor

• Coarse-grained molecular modeling
• Protein structure prediction
• Molecular dynamics simulations
• +3 more

Professor Cezary Czaplewski of the University of Gdańsk is a leading researcher in computational chemistry and coarse-grained molecular modeling. Affiliated with the Faculty of Chemistry and the Department of Theoretical Chemistry , he heads the UNRES server for protein simulations and contributes to projects like Multi-GPU UNRES and MAGENTA . His work bridges physics-based simulations with experimental data integration. Key roles : Professor, Computational Biophysics, University of Gdańsk Projects : EuroHPC PL, Harmonia9, Enerliq, Parylens Research Focus : His research spans coarse-grained modeling , protein structure prediction , and hydrophobic interaction studies , with applications in virology (SARS-CoV-2), antimicrobial peptide design, and large-scale biomolecular simulations. He specializes in GPU-accelerated algorithms and multi-GPU implementations for scalable systems. Teaching : Currently teaches Molecular mechanics & dynamics , Introduction to Python programming , and Parallel programming (Bioinformatics III) . Past courses include Electronic chemical diagnostics and Software in biomacromolecular calculations for PhD students. Scientific Contributions : Developed the UNRES server for polypeptide simulations, extended the UNRES force field to nucleic acids and membrane proteins, and participated in multiple CASP/CAPRI experiments for protein structure prediction. His 2025 publications focus on time-averaged restraints and multi-GPU scalability. Contact : cezary.czaplewski@ug.edu.pl Location : Room B328, Faculty of Chemistry, University of Gdańsk

• Relational Databases
• Speculative Query Execution
• Distributed Computing
• +5 more

Anna Sasak-Okoń is a Lecturer at the Department of Information Systems Software within the Institute of Informatics and Mathematics , Maria Curie-Skłodowska University (UMCS). Her academic work focuses on database systems, speculative query execution, and distributed computing, with a particular emphasis on graph-based modeling techniques.

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

Paweł KsieniewiczView profile

Professor

• Pattern Recognition
• Data Streams
• Hyperspectral Image Processing
• +7 more

Paweł Ksieniewicz is a Professor at Wrocław University of Science and Technology, affiliated with the Faculty of Electronics and the Department of Computer Systems and Networks. He is a member of the Computer Networks Team and the Machine Learning Team, and serves as a project manager for SWAROG (2021–2025) and contributor to IDSTREAM and GEOM projects. His research interests include: Pattern Recognition Data Streams Hyperspectral Image Processing Imbalanced Data Classification Fake News Detection Classifier Ensembles Ksieniewicz's recent publications focus on challenges in data stream mining, particularly in dynamically imbalanced environments, prior probability estimation, and ensemble methods. His work spans applications in cybersecurity (fake news, IoT attack detection) and healthcare (glaucoma diagnosis). He has contributed to open-source tools like the Stream-learn Python library, emphasizing practical and reproducible machine learning research. His scientific awards include: Minister of Education and Science Scholarship for Outstanding Young Scientists Award of the Polish Society for Artificial Intelligence for Best Doctoral Thesis Multiple Rector's Awards for scientific achievements (2019–2022) Best Paper Award at ICAISC 2021 Winner of the Secundus and Primus research incentive programs Appointment to Academia Iuvenum (2024–2026) Ksieniewicz actively supervises students and has collaborated with researchers such as Michał Woźniak, Paweł Zyblewski, and Robert Burduk. He has secured research grants through national projects and serves on the Discipline Council for Technical Computer Science and Teleinformatics. He has also participated in public outreach, including lectures during Science Week and World Information Society Day. He leads and contributes to multiple research teams, including: Machine Learning Team Computer Networks Team Advanced Data Analysis Methods Team Metaheuristics Team

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.