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Lucas C. Wilcox is a Professor in the Department of Applied Mathematics at the Naval Postgraduate School. His research focuses on scientific computation, particularly in the numerical solution of partial differential equations with emphasis on wave propagation and uncertainty quantification using high-order methods. He is active in developing scalable algorithms for adaptive mesh refinement and parallel computing.
Kevin A. Brown is an active researcher in the field of High Performance Computing with a strong publication record spanning over a decade. His work primarily focuses on HPC network simulation, performance analysis, and optimization of parallel computing systems. He has collaborated extensively with researchers including Christopher D. Carothers, Robert B. Ross, and Satoshi Matsuoka across multiple institutions. Dr. Brown's research interests center around network simulation techniques, particularly Parallel Discrete Event Simulation (PDES) for modeling HPC networks. His recent work explores multi-fidelity network simulation frameworks, surrogate modeling for performance prediction, and machine learning applications for network traffic forecasting. He has made significant contributions to understanding network congestion, quality-of-service mechanisms, and the interference between different types of traffic in HPC environments. His publication record shows consistent output with 19 publications documented between 2014 and 2025, with increased productivity in recent years. The 2023-2025 period shows particularly strong activity with 12 publications, indicating ongoing research momentum. His work appears primarily in top HPC conferences including SIGSIM-PADS, CLUSTER, and ICPP. Notable recent contributions include the development of MFNetSim for multi-traffic modeling of Dragonfly systems, research on zombie packet techniques for hybrid PDES simulation, and work on steady-state fluid models for HPC networks. His research demonstrates a clear trajectory from fundamental network performance analysis toward more sophisticated simulation frameworks incorporating machine learning techniques.
Stephen L. Olivier is a prominent researcher in high-performance computing at Sandia National Laboratories, with a distinguished publication record spanning nearly two decades. His work focuses on parallel programming models, performance optimization, and energy-efficient computing across diverse architectures including CPUs, GPUs, and FPGAs. Olivier has made significant contributions to OpenMP standards and Kokkos programming model development, collaborating extensively with Department of Energy national laboratories and international research teams. Olivier's research interests center on task parallelism, memory management in distributed systems, and performance portability across heterogeneous architectures. His work addresses critical challenges in exascale computing, including efficient task scheduling for unbalanced workloads, power management in large-scale systems, and optimization of communication patterns. More recently, he has expanded his research into medical imaging applications, applying high-performance computing techniques to tuberculosis detection in rural healthcare settings. Analysis of Olivier's recent publications (2021-2024) reveals a strong focus on practical performance engineering for next-generation computing platforms. His work spans traditional HPC domains while increasingly incorporating data science applications and medical imaging analysis. The research demonstrates consistent innovation in parallel programming models, particularly around OpenMP tasking and Kokkos abstractions, with growing emphasis on energy efficiency and hardware-specific optimizations for emerging architectures. Olivier has maintained a prolific research output with numerous publications in top-tier conferences including SC, IPDPS, and IWOMP. His collaborative work extends across multiple Department of Energy laboratories and international institutions, reflecting the interdisciplinary nature of modern high-performance computing research. While specific grant information isn't detailed in the publication record, his work on DOE systems suggests significant involvement in national supercomputing initiatives.
استادیار
Preeti Malakar is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. She leads the Scalable Parallel Computing Laboratory (SParCL) and previously held positions as a postdoc and Assistant Computer Scientist at Argonne National Laboratory (USA) and was a Visiting Affiliate at Lawrence Berkeley National Laboratory (USA). She completed her PhD from the Indian Institute of Science Bangalore under Prof. Vijay Natarajan and Prof. Sathish Vadhiyar. Her research focuses on high-performance computing systems, parallel I/O optimization, in-situ analysis, and scientific visualization. Key areas include job scheduling, communication optimization for MPI applications, machine learning for performance prediction, and adaptive frameworks for large-scale simulations. Her work integrates HPC with climate modeling and extreme weather prediction. Recent publications emphasize job scheduling with SLURM, deep learning for climate applications, in-situ visualization, and auto-tuning of parallel I/O parameters. Her research consistently targets optimization of HPC systems for scientific domains. Awards: Best Paper Award, EduHiPC 2019 TCPP Best Paper Award, HiPC 2009 She actively advises students in HPC research, with alumni pursuing careers at NVIDIA, Microsoft, Apple, and SAP Labs. Her lab (SParCL) hosts internships and winter schools, including the ACM Winter School on HPC at IIT Kanpur.
Torsten Hoefler is a Professor of Computer Science at ETH Zurich, a member of Academia Europaea, and a Fellow of the ACM and IEEE. He previously led performance modeling for the Blue Waters supercomputer at the University of Illinois. His work includes key contributions to the Message Passing Interface (MPI) standard. His research focuses on performance-centric system design , spanning scalable networks, parallel programming techniques, and performance modeling for large-scale simulations and artificial intelligence. Core interests include optimizing computing architectures through mathematical models. Hoefler holds a Ph.D. from Indiana University, where he received the Young Alumni Award (2014) and Distinguished Alumni Award (2022). Major Awards: ACM Gordon Bell Prize (2019) IEEE Sidney Fernbach Memorial Award (2022) 6× Best Paper Awards at ACM/IEEE Supercomputing ERC Starting & Consolidator Grants ACM/IEEE Fellowships He chairs MPI working groups and serves on ACM SIGHPC's steering committee since 2013.
استادیار
Dr. Ryan Grant is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen’s University, Canada. He leads the Computing at Extreme Scale Advanced Research (CAESAR) lab and is affiliated with the Ingenuity Labs Research Institute. His expertise spans cloud computing, high-performance networks, low-level hardware-software interfaces, and energy-efficient supercomputing systems. Dr. Grant holds a PhD from Queen’s University (2012) and previously worked at Sandia National Laboratories (2012–2021), where he contributed to critical supercomputer communication protocols now deployed globally. He has authored over 80 peer-reviewed articles and received prestigious awards including the R&D100 Award and Queen’s University’s 125th Engineering Alumni Award. His research emphasizes advancing Canada’s supercomputing infrastructure to support AI, climate science, and national security applications. Education: PhD in Computer Engineering, Queen’s University (2012) MSc in Computer Engineering, Queen’s University (2005) BSc in Computer Engineering, Queen’s University (2004) Research Interests: Dr. Grant’s work focuses on optimizing supercomputing architectures for extreme-scale systems, with an emphasis on: High-performance networking and MPI communication protocols Power/energy management in HPC systems AI-driven network traffic prediction and resource disaggregation GPU-accelerated computing and cloud infrastructure integration National sovereignty in supercomputing for sensitive applications (e.g., defense, healthcare) Awards & Recognition: R&D100 Award (Oscars of Research) U.S. Defense Programs Awards Public Good Innovator Award Queen’s University 125th Engineering Alumni Award Grants & Labs: Dr. Grant directs the CAESAR lab, one of the world’s leading supercomputing architecture research groups. His work is supported by grants from Canadian and international agencies, focusing on sovereign supercomputing and HPC-AI convergence. Labs/Teams: CAESAR Lab (Queen’s University) Ingenuity Labs Research Institute
پژوهشگر ارشد
Jerónimo Sánchez García is a Research Fellow at Aalborg University's Department of Electronic Systems, part of The Technical Faculty of IT and Design. His work focuses on high-performance computing, quantum-resistant communications, and parallel algorithm development. He contributes to the Horizon Europe-funded QUARC project (2022–2026), advancing post-quantum cryptography applications. Research Interests: Optimizing MPI message matching using optimistic approaches Protocol buffer deserialization in distributed systems Quantum-safe data transmission in data centers Parallel computing techniques for drug discovery Recent Work Trends: His publications (2023–2024) emphasize hardware offloading strategies, resilient communication protocols, and parallelism in both computational science and quantum-resistant infrastructure. Notable contributions include achieving 100 Gbit/s quantum-safe data transfer highlighted in media coverage. Awards: None explicitly listed in the provided materials. Project Contributions: Active participant in QUARC project, presenting at ExaMPI24 and ICTON conferences. Collaborations include work with institutions like the University of Copenhagen and industry partners in high-performance computing. Labs/Teams: Involved in the QUARC research network and interdisciplinary teams focused on exascale computing and quantum security.
Alessia Annibale is a Professor of Disordered Systems in the Department of Mathematics at King's College London. She is affiliated with the Disordered Systems and Neural Networks group and the Centre for Non-Equilibrium Science (CNES). Her research focuses on statistical physics of complex systems, including non-equilibrium dynamics, complex networks, and mathematical immunology. She holds a PhD from King's College London (2007) and a Master's degree in Theoretical Physics from Sapienza University of Rome (2003). Her research interests span Non-equilibrium statistical physics Complex biological systems Network theory and null models Spin glasses and metastable states Recent work includes modeling legal systems (Graphie interface), analyzing election discrepancies, and studying Boolean networks. She has led projects like the CyberMouse initiative and the 'Analysis and Modelling of Bedform Development' study. Her contributions bridge physics, mathematics, and interdisciplinary applications in law and biology.
دانشیار
Vicenç Gómez is an Associate Professor in the Department of Engineering at Universitat Pompeu Fabra (UPF), where he leads research in artificial intelligence and machine learning. He serves as Coordinator of the Erasmus Mundus Joint Master in Artificial Intelligence and the MSc program in Intelligent and Interactive Systems, and teaches in the BSc in Mathematical Engineering in Data Science. His research interests include machine learning, approximate inference, optimal control, and complex networks , with applications in social networks, robotics, brain-computer interfaces, and urban systems. He applies advanced AI techniques to model human behavior, network dynamics, and decision-making processes. The recent publications highlight a strong focus on graph-based learning, reinforcement learning, social network analysis, and health informatics . His work integrates theoretical advances in probabilistic modeling with real-world applications in digital platforms, environmental monitoring, and mental health. There is a consistent theme of modeling complex systems through structured AI and interpretable models. Scientific Awards and Recognition: Coordinator of the prestigious Erasmus Mundus Joint Master in Artificial Intelligence Local Chair of UAI 2024, a top-tier conference in AI Program Committee member for ICAPS 2024 Organizer of the EMAI Summer School in collaboration with UCL Advising and Grants: Vicenç Gómez actively supervises PhD and Master's students, including Nur Alvarez-Gonzalez, Roger Garriga, Emily Theophilou, and Sergio Calo. His advising spans topics in emotion detection, mental health modeling, air quality prediction, and representation learning. He has been involved in organizing major academic events and leading international educational programs, indicating significant leadership and collaborative grant activity. Labs and Research Groups: He is a key member of the Artificial Intelligence and Machine Learning group at UPF’s Department of Engineering, based at the Roc Boronat building in Barcelona. His team engages in interdisciplinary research combining AI theory with applications in social, health, and urban domains.
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.
پژوهشگر
Wei Qu, M.D., Ph.D., M.S., is a Senior Biologist at the National Toxicology Program (NTP) within the National Institute of Environmental Health Sciences (NIEHS). His work focuses on integrating machine learning, bioengineering, and neuroimaging to advance toxicology and medical research. He leads projects in graph neural networks, functional connectivity modeling, and biomedical image analysis. Qu’s research bridges computational methods with biological systems, addressing challenges in neurodegenerative diseases, biosensing interfaces, and automated medical diagnostics. Key research areas include graph-based representation learning, dynamic functional connectivity analysis, and 3D microscopy image processing. His contributions span neuroimaging data interpretation, biosensor design, and cervical cancer screening algorithms. Qu collaborates across interdisciplinary teams to develop replicable machine learning frameworks for environmental health studies.
دانشیار
Biagio Cosenza is an Associate Professor at the Department of Computer Science, University of Salerno, Italy. He leads research in high-performance computing, compiler technology, and software optimization. Previously, he was a Senior Researcher at TU Berlin (2015-2019) and a Post-Doctoral Researcher at the University of Innsbruck, Austria (2011-2015). His educational background includes: Ph.D. from University of Salerno (2011), supervised by Prof. Vittorio Scarano Dr. Cosenza's research focuses on creating efficient programming models for heterogeneous computing systems. His work spans compiler technology, automatic performance tuning, and energy-efficient computing approaches. He has made significant contributions to SYCL-based programming models and MPI implementations, with emphasis on portability across diverse hardware platforms including GPUs and accelerators. His research often bridges theoretical computer science with practical applications in scientific computing and bioinformatics. His recent publications demonstrate a strong trend toward heterogeneous and distributed computing, with particular emphasis on energy efficiency, performance portability, and SYCL-based programming models. Many papers focus on applications in drug discovery and scientific simulations, showing how his theoretical work translates to real-world scientific problems. Dr. Cosenza has received several prestigious recognitions: Best Paper Award at the 14th BenchCouncil International Symposium (2022) Elevated to Senior Member of the IEEE (2022) Recognized as ACM Senior Member (2022) He leads multiple significant research projects including the PRIN 2022 project "LibreRT" and the EuroHPC project "LIGATE." His work has secured substantial funding from sources including the German Research Foundation (DFG), the Italian Ministry of Education, University and Research, and EuroHPC. He actively mentors students and collaborators on advanced topics in high-performance computing. Dr. Cosenza is a member of the ISIS Lab at the University of Salerno and contributes to open standards through his work with the Khronos Group and SYCL Working Group. His research group develops tools like CELERITY, a C++ SYCL-based programming model for accelerator clusters, and EMPI, an enhanced message passing interface in modern C++.
Amina Piemontese is an Associate Professor at the Department of Engineering and Architecture, University of Parma, Italy. She teaches courses in Communication Engineering across multiple programs including Master's Degree in Communication Engineering, Bachelor's Degree in Computer, Electronics and Telecommunications Engineering, and Management Engineering for academic years spanning from 2020/2021 through 2025/2026. Professor Piemontese's research focuses on advanced communication theory and signal processing with particular expertise in phase noise channels , non-terrestrial networks , and advanced modulation techniques . Her work bridges theoretical communication principles with practical implementations for next-generation wireless systems, especially in satellite communications and 5G/6G networks. She has made significant contributions to expectation propagation algorithms for channel detection and spiral constellation design for nonlinear channels, with numerous publications exploring these topics in depth. Analysis of Professor Piemontese's recent publications reveals a strong and evolving research trajectory focused on non-terrestrial networks and satellite communications for 6G applications. Her work increasingly explores OTFS modulation as a solution for multi-satellite systems and addresses critical challenges in spectrum sharing between terrestrial and satellite networks. A consistent thread throughout her research is the application of statistical signal processing techniques, particularly expectation propagation , to solve complex communication problems in challenging channel conditions where traditional approaches fail. Professor Piemontese serves as a reference teacher for the Master's Degree in Communication Engineering across multiple academic years and teaches specialized courses including Communication Fundamentals, Elements of Digital Communications, and Internet and Multimedia. While specific grant information isn't provided in the available text, her extensive research output suggests involvement in European or national research projects focused on next-generation wireless communications and satellite systems. Her work on unified software-defined radio frameworks for flexible waveform design indicates leadership in developing practical implementations for emerging communication standards.
مدرس ارشد
Tommi Junttila serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, Finland, where he conducts cutting-edge research at the intersection of formal methods and computational logic. His academic profile demonstrates sustained contributions to theoretical computer science with practical applications in system verification and blockchain technology. His research program centers on advancing formal verification techniques, with core expertise in: Symmetry reduction algorithms for state space explosion SAT/SMT solving with specialized parity and XOR reasoning Bounded model checking of timed and asynchronous systems Blockchain protocol verification (notably DeFi lending pools) Canonical labeling tools for graph automorphism detection Recent work shows increasing focus on decentralized finance applications while maintaining foundational contributions to solver technology. Analysis of his publication trajectory (2011-2022) reveals consistent innovation in SAT solving methodologies, with symmetry reduction and parity reasoning forming persistent research threads. His 2022 work on DeFi lending pools represents a strategic expansion into blockchain verification, leveraging established formal methods expertise for emerging financial technologies. Tool development (bliss, PySMT) demonstrates commitment to practical research impact. No scientific awards were documented in the source materials. Similarly, no information regarding student supervision, grant funding, or laboratory affiliations was present in the provided text. His independent tool development (including bliss for graph canonical labeling and PySMT for SMT solver interfaces) indicates significant technical leadership within the formal methods community.
پژوهشگر
Dr. Jannek Squar is a researcher affiliated with the University of Hamburg and the German Climate Computing Centre (DKRZ). His work focuses on high-performance computing (HPC) infrastructure, code transformation, and parallelization techniques. Academic Rank: Researcher Key Projects: i_SSS (Integrated Support System for Sustainability), HLRE-5 Supercomputer Procurement His research spans automatic code transformation using LLVM , parallelization via OpenMP/MPI , and high-performance I/O optimization . Recent publications highlight applications in sustainable agriculture and compiler-assisted HPC performance analysis. He has supervised multiple theses on topics including elastic cloud solutions, MPI communication modeling, and LLVM-based code verification. No scientific awards are explicitly mentioned in the provided text.
