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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

Janusz FrączekView profile

Professor

• Computer Methods in Mechanics
• Kinematics and Dynamics of Multibody Systems
• Robotics
• +3 more

Prof. Janusz Frączek is a faculty member at Warsaw University of Technology, holding the academic rank of Professor. His research focuses on computational mechanics, kinematics and dynamics of multibody systems, robotics, and biomechanics. University: Warsaw University of Technology Academic Rank: Professor Contact: janusz.fraczek@pw.edu.pl | New Aviation Building, room 322 Research Interests: Computer methods in mechanics Kinematics and dynamics of multibody systems Robotics Biomechanics Teaching Activities: Dynamics of Multibody Systems Surveying and experimental techniques Theory of Machines and Mechanisms I Article Trends: The 15 most recent publications emphasize computational mechanics, robotics, Hamiltonian frameworks, and optimization. Topics include redundant constraints, parallel computing, nonholonomic systems, and augmented Lagrangian methods.

• Quantum Computing
• Hybrid Quantum-Classical Algorithms
• Workflow Scheduling
• +3 more

Katarzyna Rycerz is a Lecturer at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, Poland. She holds the academic rank of Lecturer as part of the assistant professors group, with her office located at D-17, ul. Kawiory 21, room II, 3.54. Her research spans quantum computing, hybrid quantum-classical algorithms, and distributed systems, with significant contributions to workflow scheduling and complex network optimization. Dr. Rycerz's primary research focuses on quantum optimization techniques, particularly quantum annealing and variational algorithms applied to combinatorial problems. Her work bridges theoretical quantum computing with practical high-performance computing challenges, including load rebalancing in HPC systems and community detection in complex networks. She has developed software tools like QHyper for hybrid quantum-classical optimization and explored quantum solutions for thermoelectric properties in graphene and quantum game theory paradoxes. Her 15 most recent publications (2019-2025) reveal a dominant trend toward quantum-classical hybrid methods, with 80% focused on quantum annealing applications for workflow scheduling, network analysis, and optimization problems. Key themes include D-Wave system implementations, quantum game theory extensions, and thermoelectric material simulations, demonstrating a consistent shift from earlier multiscale simulation work toward cutting-edge quantum computing applications. No scientific awards are documented in the available information. Details regarding student advising, research grants, laboratory affiliations, or collaborative teams are not provided in the source materials.

Grzegorz KrynickiView profile

Researcher

• Articulatory Phonetics
• Computer Assisted Pronunciation Teaching
• Laboratory/Experimental Phonology
• +7 more

Grzegorz Krynicki is a Senior Researcher at the Faculty of English , Adam Mickiewicz University, Poznań, Poland. He leads the Speech and Language Processing Laboratory , focusing on experimental phonetics, machine translation, and computer-assisted language learning. With a Ph.D. in English (2006) and MA in English (2000) from Adam Mickiewicz University, he has contributed extensively to articulatory phonetics and Polish-English corpus linguistics. Education MA in English, Poznań (2000) Ph.D. in English, Poznań (2006) His research interests bridge Articulatory Phonetics and Machine Learning , with applications in Computer Assisted Pronunciation Teaching , Automatic Speech Recognition , and Corpus Linguistics . He has developed tools like Wordbuilder for corpus-based exercises and IPA2Polglish for phonetic transcription conversion. The articles reflect his expertise in electropalatography (EPG) for L2 pronunciation teaching, phonetic error detection in Polish learners of English, and machine translation systems like POLENG. His work spans Multilingualism , Speech Technology , and Polish-English Contrastive Analysis . Scientific Awards University of Bielefeld scholarship (1999) Professional Activities include participation in the International Society of Phonetic Sciences and development of the Speech and Language Processing Laboratory . He has presented at conferences such as the International Congress of Phonetic Sciences (2019) and Societas Linguistica Europaea meetings, often collaborating with institutions like the University of Colorado and Linköping University.

• Cryptography
• Computer Science
• High-Performance Computing (HPC)
• +3 more

Dr. Michał Klisowski is a Lecturer at the Fundacja Uniwersytetu Marii Curie-Skłodowskiej , affiliated with the Faculty of Mathematics, Physics and Computer Science and the Department of Information Systems Software . His research focuses on cryptography, high-performance computing, and media truth detection. Research Highlights: Cryptographic protocols, energy-efficient computation, media credibility analysis Email: michal.klisowski@mail.umcs.pl His recent publications address topics such as multicore key exchange protocols , RAPL power monitoring , and fake news detection using machine learning. He holds an ORCID identifier and has published in information and communication technology domains. Collaboration & Tools: Utilizes ORCID and Scopus for academic visibility, with a focus on integrating theoretical cryptography with practical computational efficiency.

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.

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.

• Quantum Information
• Nanophysics
• Statistical Physics
• +7 more

Marcin Kostur is a researcher affiliated with the University of Silesia (ul. 75 Pułku Piechoty 1A, Chorzów, Poland). His research focuses on stochastic processes, Brownian motion, molecular motors, mesoscopic physics, microfluidics, parallel computing, and lattice Boltzmann methods. He has contributed to hemodynamics studies and computational fluid dynamics applications in cardiology. His work spans interdisciplinary areas such as quantum information, nanophysics, and statistical physics. Notable recent projects include AI-driven coronary artery calcium scoring algorithms, deep learning integration with particle swarm optimization, and aortic valve reconstruction in non-contrast CT imaging. He also explores LDL transport mechanisms in arterial walls under hypertension and the impact of flow dynamics on cardiovascular health. His publications highlight contributions to both theoretical frameworks (e.g., Brownian ratchets, fractal tree modeling) and applied medical imaging techniques (e.g., CCTA analysis, CFD-based CAD assessment). Collaborative work includes GPU-accelerated Monte Carlo simulations and open-source tools like the Sailfish lattice Boltzmann method implementation. While no formal awards are listed, his extensive publication record reflects sustained contributions to computational biophysics and medical imaging innovation. His research bridges fundamental physics principles with clinical applications, particularly in cardiovascular diagnostics and fluid dynamics modeling.

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

• 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.

• 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.

• Remote sensing data acquisition and processing
• Parallel computing and computer clusters
• Teleinformatic systems and networks
• +3 more

Dariusz Chaładyniak is a researcher at the Institute of Geospatial Engineering and Geodesy, Military University of Technology, where he has been employed since 1992. He earned his PhD in geodesy and cartography (specializing in geoinformatics) from the Military University of Technology in 2002 and graduated with distinction from its Faculty of Civil Engineering and Geodesy in 1993. His research focuses on remote sensing data acquisition and processing parallel computing and computer clusters teleinformatic systems and networks information security He has participated in multiple research projects funded by the State Committee for Scientific Research (KBN), the Ministry of Science and Higher Education, and European Union programs, and has authored or co-authored over 50 publications in conference proceedings and scientific journals.

• Combustion Engineering
• Artificial Intelligence
• Numerical Simulations
• +4 more

Dr inż. Mateusz Żbikowski is a researcher at the Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology. His research focuses on applying artificial intelligence and computational methods to combustion process optimization, with expertise in numerical simulations, high-performance computing, and data processing frameworks like Apache Spark and Hadoop. Teaching: Conducts courses on Cloud Computing, Combustion Methods (Cantera), and Computer Simulations (OpenFoam) Technologies: Works with MPI/MPJ, OpenFoam, and programming languages including Java, C++, Python, and R Research: Investigates AI-driven combustion optimization, numerical modeling of thermal processes, and distributed computing cluster applications

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.

Paweł MalczykView profile

Associate Professor

• Multibody Systems
• Optimal Control
• Robotics
• +3 more

Paweł Malczyk is an Associate Professor and Head of the Department of Theory of Machines and Robots at the Faculty of Power and Aeronautical Engineering, Warsaw University of Technology. He holds a D.Sc. (habilitation), Ph.D., and M.Eng. His research focuses on multibody systems, optimal control, data-driven modeling, and high-performance computing. He leads the division and oversees research in kinematics, dynamics, and control of complex systems. Key research interests include multibody dynamics simulation, optimal control methods, parallel computing, and applications of machine learning in system modeling. His work emphasizes algorithm development for efficient dynamics simulations and real-time control. Selected articles span topics like neural-network-based inverse dynamics, divide-and-conquer algorithms, and FPGA acceleration. His contributions address both theoretical advancements and practical implementations in robotics and mechanical systems. Malczyk teaches courses on automation, control systems, and multibody dynamics. His lab focuses on computational mechanics and real-time systems, with ongoing projects in parallel algorithms and data-driven control strategies.