Waqwoya Abebe is a Postdoctoral Research Associate at the Geospatial Artificial Intelligence (GeoAI) Group within the National Security Sciences Directorate at Oak Ridge National Laboratory (ORNL) . His work focuses on advanced machine learning techniques for geospatial data science and high-performance computing applications. Education: Ph.D. in Computer Science, Iowa State University Research Interests: Privacy-preserving federated learning Multi-modal foundation modeling Neural architecture search High-performance computing (HPC) challenges for large language models (LLMs) Atomic-scale electron micrograph segmentation Decentralized learning systems Professional Affiliations: Member of IEEE
Prof. Dr. Sergei Gorlatch is a full professor at the University of Münster, Germany, in the Department of Mathematics and Computer Science, where he holds the Chair of Practical Computer Science (Parallel and Distributed Systems) within the Institute of Computer Science. He has been a leading figure in high-performance and parallel computing since joining the university in 2003. University: University of Münster School: Department of Mathematics and Computer Science Department: Institute of Computer Science Academic Rank: Professor His research focuses on algorithm and software development for modern computer systems, particularly in parallel and distributed computing, high-performance computing (HPC), GPU-based systems, cloud and grid computing, and performance optimization. His work bridges theoretical formal methods and practical applications, especially in real-time online interactive systems such as online games and simulations. He has pioneered frameworks like SkelCL, dOpenCL, and the Real-Time Framework (RTF) to simplify parallel programming and improve performance portability. The recent publications (2020–2024) reflect a strong trend in GPU programming, performance optimization, formal verification, and distributed systems. Key themes include the development of safe and high-level GPU languages (e.g., Descend), autotuning and model checking for performance, multi-cloud orchestration, and performance modeling of legacy and real-time systems. His work often combines compiler techniques, functional programming, and systematic transformations to achieve efficient and portable code. Best Poster Award – PUMPS+AI, 2019 Best Paper Award – CGO, 2018 Alexander von Humboldt Research Fellowship, 1991 Prof. Gorlatch has supervised numerous students and researchers, many of whom are frequent co-authors on his publications. He has led multiple funded projects from DFG, EU (e.g., CoreGrid, MONICA), and industry (e.g., NVIDIA Graduate Fellowship). His work includes both theoretical contributions (e.g., algorithmic skeletons, formal verification) and applied systems development, demonstrating a strong record of advising, grant acquisition, and interdisciplinary collaboration. He is actively involved in several research labs and teams at the University of Münster, particularly those focused on parallel computing, GPU programming, and real-time systems. His group develops high-level programming models and tools to make parallel computing more accessible and efficient across diverse architectures.
José L. Sánchez is a full Professor in the Department of Computer Systems at the University of Castilla-La Mancha (Spain), where he has held a permanent academic position since 1986. His research spans high-performance computing, network architecture, and GPU acceleration, with particular focus on energy-efficient interconnection networks for data centers and exascale systems. His research interests center on interconnection network optimization , including torus topologies, deadlock-free routing, and QoS provision in high-radix switches. He pioneers energy-efficient networking through on/off link strategies and develops frameworks like VEF traces for modeling MPI traffic in large-scale simulations. His work bridges theoretical network design with practical GPU-accelerated implementations for real-time computer vision and similarity search algorithms. Recent publications reveal a strong trend toward exascale-ready networking solutions , with 60% of his 2019-2022 work addressing energy efficiency in HPC topologies, congestion management in lossy networks, and photonic interconnect challenges. His methodology consistently combines formal network modeling with hardware-aware implementations, particularly leveraging GPU parallelism for bioinspired vision algorithms and metric search optimization. Professor Sánchez leads significant contributions to network simulation tooling, including the TopGen library for topology modeling and VEF3 framework extensions. His collaborative work spans multiple EU institutions, with frequent co-authorship patterns indicating strong ties to research groups specializing in network-on-chip systems and high-performance interconnects.
Francisco Alfaro-Cortés is a Professor at the Department of Computer Systems, Universidad de Castilla-La Mancha (UCLM), Spain, since 2020. His research focuses on interconnection networks, network-on-chip (NoC), and quality of service (QoS) mechanisms in high-performance computing (HPC) systems. University: Universidad de Castilla-La Mancha Department: Department of Computer Systems Academic Rank: Professor (Catedrático de Universidad) His work explores energy-efficient HPC topologies, adaptive routing algorithms for twin torus networks, and congestion management in high-speed interconnects. He has developed open-source frameworks for MPI traffic modeling and self-configuring NoC infrastructures. Notable contributions include optimizing high-radix switch configurations, formalizing deadlock-free routing mechanisms, and advancing QoS provision in Dragonfly and Torus networks. His research spans 2006–2022, with recent focus on sustainable network design.
Dr. Werner Dobrautz is a quantum chemist and computational physicist who leads the DRESDEN-concept Research Group "AI4Quantum" since 2024. The group is jointly hosted by the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) at TU Dresden and the Center for Advanced Systems Understanding (CASUS) at Helmholtz-Zentrum Dresden-Rossendorf (HZDR). His research integrates quantum computing, machine learning, and high-performance computing to address challenges in chemistry and physics, such as bio-catalysis for ammonia production and unconventional superconductivity. Education : PhD in Theoretical Quantum Chemistry (University of Stuttgart, 2019), Master of Science in Technical Physics (Graz University of Technology, 2014) Dr. Dobrautz specializes in developing computational methods like transcorrelation and quantum algorithms to simulate complex systems, particularly focusing on strongly correlated electron systems and noise-resilient quantum chemistry . His work aims to breakthrough the "exponential wall" in computational resource scaling. Scientific Awards : Marie Skłodowska-Curie Postdoctoral Fellowship (€223,000, 2022) Vinnova Fellowship (€44,000, 2024) He has contributed to quantum computing initiatives like OpenSuperQPlus and NordIQuEst , and his software expertise spans NECI, Qiskit, OpenMolcas, and machine learning frameworks like TensorFlow.
Fabian Denner is an Associate Professor at the Department of Mechanical Engineering, Polytechnique Montréal. His work focuses on modeling multiphase flows and related physical phenomena, including cavitation, acoustic wave dynamics, and high-performance computing (HPC). He develops advanced numerical methods and software tools for simulating incompressible and compressible flows, with applications in medicine, chemical engineering, and aerospace. His research group addresses challenges in predicting cavitation effects, liquid jet breakup, and acoustic modulation in accelerating flows. Education Dipl.-Eng. in Automotive Engineering, University of Stuttgart (2009) Ph.D. in Mechanical Engineering, Imperial College London (2013) Research Interests His expertise spans: Numerical modeling of multiphase flows Acoustic wave propagation and modulation Cavitation dynamics and biomedical applications High-performance computing (HPC) for fluid dynamics Liquid jet atomization and particle-laden flows Surface tension and interface reconstruction techniques Publications and Software Fabian has published extensively in leading journals like Physics of Fluids , Journal of Computational Physics , and Journal of Fluid Mechanics . He co-developed software frameworks such as Wave-DNA and APECSS for simulating complex fluid dynamics and acoustic phenomena. Scientific Recognition Margaret Fishenden Centenary Memorial Prize (2015) Leadership Roles Vice-director, Canadian Society for Mechanical Engineering - Fluid Engineering Technical Committee Co-director, Editorial Advisory Committee of Canadian Journal of Chemical Engineering Active contributor to international conferences (APS DFD, ICMF, IUTAM)
Urvij Saroliya is a doctoral candidate at the Technical University of Munich , affiliated with the Chair of Computer Architecture and Parallel Systems under Prof. Martin Schulz. He focuses on high-performance computing (HPC) and computer architecture, with expertise in resource management, heterogeneous accelerators, and energy-aware systems. Research Interests: Computer Architecture, HPC, Performance Modeling, Reinforcement Learning His recent publications analyze reinforcement learning applications for HPC resource management, including NUMA systems and GPU partitioning. His work emphasizes performance portability and energy efficiency in heterogeneous architectures. He contributes to ongoing projects like PDexa and participates in seminars on quantum computing integration and AI hardware development.
Dirk Stober is a researcher at the Chair of Computer Architecture and Parallel Systems at the Technical University of Munich . As a Ph.D. candidate, his work focuses on low-level programming, heterogeneous computing, and novel computer architectures, with a strong emphasis on FPGA programming and machine learning accelerators. His research explores the intersection of high-performance computing (HPC), quantum computing, and AI hardware development. Research Interests: Low-Level Programming Heterogeneous Computing Machine Learning Accelerators FPGA Programming Novel Computer Architectures His work contributes to advancing programming frameworks and performance modeling techniques for emerging architectures like quantum accelerators and AI-specific hardware. While no explicit scientific awards are listed, his involvement in courses such as Efficient Programming of HPC Systems and projects like SEANERGYS (EuroHPC) and Q-DESSI (MQV) highlights his engagement with cutting-edge computational technologies.
Jonas Winklmann is a researcher at the Chair of Computer Architecture and Parallel Systems at Technische Universität München (TUM), focusing on quantum computing architectures and hardware acceleration. He contributes to projects like SEANERGYS and MUNIQC-ATOMS, integrating quantum systems with high-performance computing (HPC) frameworks. Research: Quantum hardware design, FPGA-based acceleration, and parallel algorithms Collaborations: EuroHPC initiatives, interdisciplinary quantum computing efforts Teaching: Mentoring lab courses on HPC systems and quantum integration His work spans neutral atom quantum computing, algorithm optimization for heterogeneous platforms, and control systems for quantum devices. Publications highlight FPGA applications in quantum simulation and atom detection techniques. He collaborates on software projects like QMPI and SWEET, advancing quantum-HPC hybridization.
Sylvain JUBERTIE serves as a Lecturer at the University of Orleans, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic career spans over two decades with continuous contributions to high-performance computing, particularly in vectorization techniques and seismic simulation methodologies. His research interests demonstrate deep specialization in: Hardware-specific vectorization (ARM NEON/SVE, SIMD) for seismic kernels GPU acceleration and code portability across architectures High-order spectral finite element methods (EFISPEC3D) Energy efficiency optimization in parallel computing Algorithmic skeleton libraries (OSL, NSIMD) for parallel programming Memory layout reorganization for numerical kernels Analysis of his 15 most recent publications (2013-2025) reveals a consistent research trajectory focused on seismic wave propagation simulation. His work systematically addresses vectorization challenges across ARM architectures, GPU offloading, and memory access patterns, with EFISPEC3D serving as the primary application framework. Notable trends include the development of the NSIMD abstraction layer and rigorous energy-performance trade-off studies on embedded platforms like Jetson boards. Dr. Jubertie maintains active collaboration with key researchers including Fabrice DUPROS, Florent DE MARTIN, and Guillaume QUINTIN through the LIFO laboratory, contributing to France's geophysics research infrastructure while advancing compiler-level optimizations for emerging processor architectures.
Antonello Filippi is an Associate Professor at the Department of Chemistry and Pharmaceutical Technologies, Sapienza University of Rome. His research focuses on structural and supramolecular chemistry, combining experimental mass spectrometry (IRMPD, IM) with theoretical modeling via high-performance computing (HPC). He also applies GC-MS to characterize food matrices for analytical and biomedical purposes. Graduated with honors in Chemistry from Sapienza University of Rome (1992) Researcher at CNR’s Institute of Nuclear Chemistry (1986–1996) University Researcher (1996–2006) His research projects include structural studies of DNA adducts and non-covalent interactions in supramolecular systems. Recent publications highlight applications in food chemistry, pharmaceutical analysis, and chiral recognition mechanisms. Current teaching activities involve courses in General and Inorganic Chemistry, Analytical Chemistry, and laboratory sessions. He has authored textbooks in general chemistry and stoichiometry.
Uroš Lotrič is an Associate Professor at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. His academic and professional work spans research in soft computing methods, distributed processing, and high-performance computing applications. Education: BSc in Physics (1994), MSc in Computer Science (1997), PhD in Computer Science (2000), all from the University of Ljubljana. Research Interests He focuses on soft computing techniques, distributed systems, and their applications in industrial and computational domains. His work integrates neural networks, wavelet transforms, and predictive modeling to solve complex problems in fields like rubber processing and time series analysis. Scientific Awards Best Assistant 2007 Best Professor 2009 Projects and Laboratory He is involved in national and European research programs such as P2-0241, EUMaster4HPC, and ARISA. Additionally, he is a member of the Adaptive Systems and Parallel Processing laboratory, contributing to advancements in adaptive algorithms and parallel computing.
Alexandre Fournier is a Professor of Geophysics at the Institut de Physique du Globe de Paris (IPGP), where he leads the Geologic Fluid Dynamics research group. He serves as the scientific manager of IPGP's shared HPC and data processing service (S-CAPAD) and coordinates the MOOC 'Our Planet'. From 2016 to 2020, he headed the STPE and GGG master's programs. His research spans Earth's and planetary dynamos, fluid dynamics in planetary settings, inverse problems, data science, and numerical methods for geophysical applications. He completed his Accreditation to Supervise Research at Paris-Diderot University (2012), PhD in Geosciences at Princeton University (2003), DEA in internal geophysics at IPGP (1998), and Master's in Physics at ENS de Lyon (1998). His students include Elisabeth Canet, Sabrina Sanchez, Marie Bocher, Guillaume Pichon, Venkatesh Gopinath, Thijs Franken, Marie Troyano, and Théo Tassin. His recent articles focus on geodynamo simulations, archaeomagnetic studies, and computational methods for planetary fluid dynamics. Notable collaborations include Julien Aubert, Thomas Gastine, and Yves Gallet. His work emphasizes high-performance computing, data assimilation, and applications to Earth and planetary systems.
Dr. Mahmoud Alzoubi is an Assistant Professor at Queen's University , cross-appointed between the Robert M. Buchan Department of Mining Engineering and the Department of Mechanical and Materials Engineering . He leads an interdisciplinary research program that couples advanced transport phenomena with energy-efficient technologies for mining and renewable energy applications. Education: Ph.D. in Mining & Mechanical Engineering, McGill University (2018) M.Sc. in Engineering Systems & Management, Masdar Institute of Khalifa University in collaboration with MIT (2014) B.Sc. in Mechanical Engineering, Jordan University of Science and Technology (2005) Research Interests: His work centers on transport phenomena in porous media , with emphasis on phase-change heat and mass transfer , artificial ground freezing , thermal energy storage , microfluidic devices , and renewable HVAC cycles . By integrating high-fidelity experiments with large-scale numerical simulations performed on high-performance clusters, he advances sustainable solutions for energy-intensive mining operations and green building technologies. Publication Impact: Across 32 peer-reviewed articles (2013-2024), a dominant theme emerges: developing computationally efficient models for coupled thermo-hydraulic processes in freezing, storage and ventilation systems. Studies range from Stefan-problem analytical solutions for phase-change materials to large-eddy simulations of cough-jet dispersion for indoor-air safety, underscoring a methodological breadth that spans pure mathematics, experimental heat transfer, and applied computational fluid dynamics. Funding & Recognition: Total research funding secured: CAD 466,000+ (direct cash CAD 381,000 + high-performance computing allocation CAD 85,000) Former member, Canadian Hydrogen in Mining Advisory Committee , Natural Resources Canada Laboratory & Teams: Dr. Alzoubi directs a research laboratory at Queen’s University equipped with state-of-the-art instrumentation for multiphysics experimentation and access to national HPC facilities. The group collaborates closely with industry partners (mining, HVAC) and government laboratories to translate fundamental findings into scalable, energy-efficient technologies for northern mining and cold-region infrastructure.
Mahesh M S is an Associate Professor in the Mechanical & Aerospace Engineering department at the Indian Institute of Technology Hyderabad . He holds a Ph.D. in Aerospace Engineering from the University of Illinois at Urbana-Champaign. Education Ph.D. (2013), University of Illinois at Urbana-Champaign M.Tech. (2004), Indian Institute of Technology Madras B.Tech. (2004), Indian Institute of Technology Madras Research Interests include Vibroacoustics , Aeroelasticity , Computational Mechanics , Aerodynamics , Aeroacoustics , and High Performance Computing . His group explores compressible multiphase flows, radar cross-section prediction, and shock wave interactions. Current Projects include Internal Ballistics (ARMREB, 2024–2027) and Hydroacoustics (NSTL, DRDO, 2024–2025). Past projects span underwater supersonic jets (DRDO, 2019–2021), cavity aeroacoustics (ADA, 2020–2021), and terminal ballistics (ARB, 2016–2018). Scientific awards : None listed. Students : Dr. Amartya Jana (Ph.D. candidate), Dubba Bhuvana Sai Charan Nath (Ph.D. scholar), Shikshit Nawani (M.Tech), Guruprasad Arya (M.Tech), Akshay Khare (M.Tech), Swapna Yalamanchili (Ph.D. scholar), Vishnu S. B. (Project Scientist), Chele Rajesh (Junior Research Fellow), Sharad Verma (M.Tech), Mangesh Dholwade (M.Tech). Contact : Room C-515, Academic Block C, IIT Hyderabad, Telangana, India. Email: mahesh@mae.iith.ac.in .
