Find Academic Supervisors

• Embedded Systems
• Artificial Neural Networks
• Signal Processing
• +3 more

Magdalena Szymczyk is a Lecturer in the Department of Biocybernetics and Biomedical Engineering at AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. Her work bridges embedded systems, biomedical signal processing, and geophysical data analysis. Research focuses on energy-efficient sensor networks, neural networks for GPR data classification, and mathematical transforms in signal analysis Expertise in parallel computing, real-time systems, and biomedical engineering applications Her publications (2015–2025) demonstrate a trajectory from parallel neural networks and S-transform/GPR methodologies to recent work on MicroPython in embedded systems. Key themes include energy optimization in distributed architectures and AI-driven signal processing across biomedical and geophysical domains. She has authored works on deterministic chaos in simulations, GPU image processing, and cybersecurity in microcontroller systems. Her current research emphasizes embedded systems security, medical signal diagnostics, and computational methods for geological analysis. She utilizes tools like OpenCL for GPU acceleration and MATLAB for parallel computing implementations.

Michela TauferView profile

Associate Professor

• Heterogeneous Computing
• Cloud Computing
• Volunteer Computing
• +4 more

Michela Taufer is the David L and Beverly JC Mills Chair of Computer and Information Sciences and an Associate Professor at the University of Delaware. She earned her Masters degrees in Computer Engineering from the University of Padova (Italy) and her doctoral degree in Computer Science from the Swiss Federal Institute of Technology (Switzerland). Her research interests include scientific applications and their advanced programmability in heterogeneous computing (i.e. multi-core and many-core platforms, GPUs); performance analysis; modeling and optimization of multi-scale applications on heterogeneous computing; cloud computing and volunteer computing; numerical reproducibility and stability of large-scale simulations on multi-core platforms; and big data analytics and MapReduce. Dr. Taufer has published research on protein folding, molecular dynamics, and workflow execution on cloud and grid platforms. Her work often focuses on performance characterization and optimization of scientific applications on distributed and parallel systems. From 2003 to 2004 she was a La Jolla Interfaces in Science Training Program (LJIS) Postdoctoral Fellow at the University of California San Diego (UCSD) and The Scripps Research Institute (TSRI) From 2005 to 2007, she was an Assistant Professor at the Computer Science Department of the University of Texas at El Paso (UTEP) She joined the University of Delaware in 2007 as an Assistant Professor and was promoted to Associate Professor with tenure in 2012

• GPU Programming
• Multi-Core Processors
• Statistical Mechanics
• +5 more

Prof. Piotr Białas is a Professor of Physical Sciences and Director of the Institute of Applied Informatics at Jagiellonian University. He holds a habilitation in physics from the same institution and has served as department head and faculty member since 1993. His research focuses on GPU programming, multi-core processor optimization, and theoretical physics areas like simplicial gravity and quantum chromodynamics on networks. He has supervised over three doctoral and dozens of master's theses. Current projects include GPU acceleration for medical imaging (J-PET tomograph) and real-time FPGA-based data processing. Education: PhD in Physics (1993), MSc in Physics (1990) from Jagiellonian University. Academic roles include membership in the Academic Council of Technical Informatics and Complex Systems Commission at Polish Academy of Arts and Sciences. Invited professorships at universities in Amsterdam, Bielefeld, Barcelona, and Saclay. Specializes in CUDA optimization, parallel computing, and medical imaging algorithms.

Łukasz ChabudzińskiView profile

Assistant Professor

• Hydrology
• Geographic Information Systems (GIS)
• Climatology
• +4 more

Dr. Łukasz Chabudziński serves as Assistant Professor at the Department of Hydrology and Climatology within the Faculty of Earth Sciences and Spatial Management at Maria Curie-Skłodowska University in Lublin. Employed since October 1, 2008, he earned his Doctor of Earth Sciences in Geography on March 30, 2016, following progressive roles from Assistant in the GIS Laboratory (2008-2016) to Assistant Professor positions. His educational background includes postgraduate studies in 'Management of research projects and development works' (2011-2012) and 'Research Project Manager' (2012-2013) at the University of Economics and Innovation in Lublin. Dr. Chabudziński's research focuses on hydrology, GIS applications, and climatology, with particular emphasis on groundwater resource analysis in southeastern Poland's Roztocze region and Lublin Upland. Analysis of his publication record reveals a strong interdisciplinary approach combining hydrology, geomorphology, and computational methods. His recent work increasingly integrates high-performance computing with traditional hydrological analysis, particularly examining spring dynamics, small water bodies, and geological influences on water resources in loess landscapes. Rector's Award (team, third degree) for organizing GIS Challenge (2017) Rector's Award (individual, third degree) for outstanding work for the University (2020) Dr. Chabudziński has supervised 22 diploma theses (21 bachelor's and 1 master's) with notable student successes in competitions like 'GIS Team' and 'GIS Challenge.' He serves as principal investigator for NCN grants and contributes to numerous research projects examining landscape evolution, hydrological processes, and archaeological site relationships in loess regions. He actively shapes geospatial education through leadership roles in academic conferences, competition organization, and curriculum development for geoinformatics programs, bridging theoretical research with practical applications in water resource management and environmental protection.

• Machine Learning
• Electrical Engineering
• Computer Science
• +7 more

Anna Plichta is an Assistant Professor at the Department of Computer Science, Faculty of Computer Science and Telecommunications at Jagiellonian University. Her primary discipline is Information and Communication Technology (ICT), with a strong focus on interdisciplinary applications spanning electrical engineering, bioinformatics, and educational technology. Her research interests include machine learning techniques (neural networks, decision trees, genetic algorithms), motor diagnostics in electromechanical systems, bacterial classification using computational methods, and pedagogical strategies for student engagement. She has published widely on topics such as fault detection in induction motors, bacterial taxonomy through classifier systems, and the integration of CUDA technology for optimization problems. Anna’s work bridges theoretical computer science with practical applications, including robotics navigation algorithms, FPGA-based signal processing tools, and e-learning systems. She is actively involved in curriculum development, particularly in evaluating strategies like industry study visits for student professionalization. Her academic contributions span 18 documented publications, with a Hirsch index of 3 across Scopus and Web of Science. She maintains an active profile on ORCID and Scopus, reflecting her commitment to interdisciplinary research and academic collaboration.

• Biomedical Engineering
• Information and Communication Technology
• Signal Processing
• +3 more

Dr. Anna Gajos-Balińska serves as a Lecturer in the Department of Neuroinformatics and Biomedical Engineering at the Faculty of Mathematics, Physics and Computer Science, Maria Curie-Skłodowska University (UMCS) in Lublin, Poland. Her office is located in room 503 at the Institute of Computer Science where she conducts regular consultations with students. Her research spans the interdisciplinary intersection of Biomedical Engineering (50%) and Information and Communication Technology (50%), with particular expertise in electroencephalography (EEG) signal processing , parallel computing implementations for neuroinformatics, and quantitative analysis of brain activity in psychiatric disorders. Her methodological approaches integrate machine learning tools, CUDA programming, and multi-core architecture optimization to enhance EEG data analysis workflows. Analysis of her publication record reveals consistent contributions to high-performance computing applications in neurodiagnostics, with significant focus on independent component analysis (ICA) algorithms for EEG data processing. Her work demonstrates technical proficiency in optimizing computational frameworks (including Intel Cilk Plus, OpenMP, and BLAS libraries) while maintaining clinical relevance through applications in psychiatric disorder research. Dr. Gajos-Balińska maintains active scholarly profiles through ORCID (0000-0003-4928-5628), Google Scholar, and Scopus, with bibliometric indicators showing an h-index of 4 (Google Scholar), 3 (Scopus), and 3 (Web of Science). Her research has achieved a total ministerial score of 291 within the Polish academic evaluation system. She participates in institutional initiatives including the European Charter for Researchers and Athena UMCS, reflecting engagement with broader academic community standards. Her technical expertise supports both educational activities within her department and potential collaborations through UMCS's research infrastructure.

• 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.

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.

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

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

Dominik ŻelaznyView profile

Researcher

• Scheduling
• Evolutionary Algorithms
• Multi-objective Optimization
• +3 more

Dominik Żelazny is a researcher at the Faculty of Information and Communication Technology, Department of Computer Engineering, Wrocław University of Science and Technology. He focuses on scheduling algorithms and multi-objective optimization using evolutionary computation methods. Current position: Researcher Primary affiliation: Department of Computer Engineering Research interests include: Development of evolutionary algorithms for scheduling problems Multi-criteria and multi-objective optimization GPU-accelerated computing using CUDA Parallel and distributed algorithm design Industrial applications of operational research

• Fast computational hardware
• Massively-parallel computing
• Residue number systems
• +4 more

Tadeusz Tomczak is a faculty member at the Faculty of Information and Communication Technology , Wrocław University of Science and Technology , within the Department of Computer Engineering . His academic rank is Professor . Research Focus: Fast computational hardware, massively-parallel computing, and residue number systems. Expertise: GPU acceleration, fluid dynamics simulations, sparse geometries, and numerical methods. Recent publications highlight his work on Navier-Stokes solvers , Lattice-Boltzmann methods , and residue number systems , with applications in fluid dynamics, biomedical engineering, and high-performance computing. His research spans hardware acceleration, algorithm optimization, and computational modeling on GPUs.

• Cloud Computing
• Database Systems
• Parallel Processing
• +3 more

Mariusz Fraś is a researcher affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. He works within the Department of Computer Science and Systems Engineering , focusing on computational systems' performance and cloud-based architectures. His research spans database optimization, parallel processing, and mobile application frameworks. Research interests include: Computational cloud scalability and cost optimization Database application performance analysis Parallel processing frameworks Mobile sensor data processing Web content delivery efficiency His publications demonstrate expertise in cloud computing trends, with 10 peer-reviewed works since 2014. Notable collaborations include researchers like Piotr Karwaczyński and Bogdan Marczuk. Current research likely continues exploring hybrid cloud environments and software performance metrics. Contact: mariusz.fras@pwr.edu.pl , Room 301, Building C-3, Wrocław University of Science and Technology.