Kyriakos Deliparaschos is affiliated with the Cyprus University of Technology as a Researcher. His work focuses on cutting-edge technologies in autonomous systems, control systems design, and FPGA-based implementations. He collaborates actively with institutions like the Institute of Communications and has co-authored papers with experts such as Argyrios Zolotas and Themistoklis Charalambous. His research interests span autonomous robotics, medical robotics, and high-performance computing, with a strong emphasis on real-time systems and embedded hardware-software co-design. Notable contributions include advancements in Delaunay triangulation algorithms for multi-core architectures and fault-tolerant AI systems for autonomous vehicles. Deliparaschos has published widely in journals like IEEE Access and Electronics, focusing on topics such as computational resource management in cloud environments and FPGA-driven control system paradigms. His work bridges theoretical research with practical applications in both academia and industry.
Devki Nandan Jha is a researcher specializing in Internet of Things (IoT) , Cloud/Edge Computing , and Cybersecurity . His work focuses on runtime monitoring, security frameworks, and deployment optimization in heterogeneous environments. Collaborations include institutions across Europe and Asia, with frequent co-authorship with Rajiv Ranjan, David Wallom, and David Blundell.
Ravi Reddy Manumachu is an Assistant Professor in the School of Computer Science at University College Dublin (UCD), Ireland. He holds a B.Tech from IIT Madras (1997) and a PhD in Computer Science from UCD (2005), specializing in high-performance heterogeneous computing and energy-efficient systems. His research focuses on optimizing performance and energy efficiency in modern heterogeneous platforms like clouds, grids, and supercomputers through novel models and algorithms. Key contributions include functional performance/energy models, energy-prediction frameworks, and extensions like Heterogeneous MPI and ScaLAPACK for heterogeneous clusters. He has published over 69 articles in top journals/conferences, with recent works addressing data transfer energy measurement, scalable allreduce algorithms (SUARA), and portable programming models (OpenH). Professional roles include Assistant Professor at UCD (2023–present), SEAI Research Fellow (2022–2023), and prior industrial experience at Ansys, Siemens, and IONA Technologies. He has certifications in university teaching, GDPR, and research integrity. Languages include English (fluent), Telugu, and Hindi. Research trends emphasize bi-objective optimization (performance-energy), hardware heterogeneity challenges, and scalable communication algorithms for deep learning. His work addresses energy non-proportionality in CPUs and GPU-CPU interactions, with practical solutions for real-world applications like matrix operations and gene sequencing.
Dr. Vasilios Kelefouras is Lecturer in Computer Science at the University of Plymouth's School of Engineering, Computing and Mathematics. His research optimizes software applications for execution time, energy consumption, and memory footprint across CPUs, GPUs and FPGAs. Notable contributions include analytical methodologies accelerating convolution layers in Deep Neural Networks (x1.1-7.2 speedup over Intel oneDNN) and optimization techniques for image processing algorithms (x2.8-40 speedup over Intel IPP). His compression work for Deep Neural Networks includes Tensor Train Decomposition methodologies for edge devices. Honors include HiPEAC Technology Transfer Award (2022) and Best Paper at SAMOS XXII. Teaches Parallel Computing, Computer Systems, and Computing Practice. Current PhD supervision focuses on accelerating ML algorithms on heterogeneous architectures.
Professor Dharmendra Sharma is a Professor of Computer Science at the University of Canberra, holding leadership roles such as Chair of the Faculty Board (Science and Technology) and former Dean of the Faculty of Information Sciences and Engineering. He specializes in AI, robotics, and distributed systems. With over 40 years of academic experience, he has published 320+ papers, supervised 40+ students, and led numerous research projects. His expertise spans constraint processing, machine learning, and applications in healthcare, security, and education. Education: PhD in Artificial Intelligence (Australian National University, 1988–1992) MCompSci, PGradDipMath, BSc (Maths/Chemistry) from the University of the South Pacific Research Interests: Distributed AI and multi-agent systems Machine learning/deep learning Constraint satisfaction models Applications in health, education, and security His work aligns with UN Sustainable Development Goals, particularly in education and technology access. Key Projects: Future Jobs Fund - Open Source Institute IAIM: Optimized Smart Data Centers eLiving Lab Collaboration Awards: Order of Australia (AM, 2019) Fellowships: ACS, South Pacific Computer Society Companion of Engineers Australia Advising and Grants: Supervised over 40 PhD/Master’s students Secured competitive grants for AI, cybersecurity, and health tech Collaborations with industry and international universities Labs/Teams: Leadership in AI and Robotics research groups Industry partnerships for innovation and training
Orran Krieger is a Professor in the Department of Electrical and Computer Engineering at Boston University. He serves as the Founding Director of the Cloud Computing Initiative (CCI) and Resident Fellow of the Hariri Institute for Computing and Computational Science & Engineering. His work focuses on cloud computing infrastructure, operating systems, and virtualization technologies. Prior roles include leading the Advanced Operating System Research Department at IBM T.J. Watson and contributing to VMware's vCloud initiatives. Education: PhD and MASc in Electrical Engineering from the University of Toronto. Research interests include cloud resource management, unikernel-based systems, security in distributed environments, and performance optimization. His leadership in the Massachusetts Open Cloud project and Open Cloud Testbed (OCT) demonstrates expertise in scalable cloud platforms. Key contributions span innovations in OS design (e.g., EbbRT framework), cloud marketplace architectures, and hardware-as-a-service models. His research bridges theoretical advancements with practical implementations in modern data centers.
Sandro Bartolini serves as Associate Professor in the Department of Information Engineering and Mathematical Sciences at the University of Siena, Italy, where he teaches advanced courses in computer architecture and parallel programming while leading cutting-edge research in high-performance computing systems. His academic journey began with a cum laude Laurea in Computer Engineering followed by a PhD in Computer Science and Engineering from Università di Pisa. Education: PhD in Computer Science and Engineering, Università di Pisa Laurea in Computer Engineering (cum laude), Università di Pisa Research Focus: His work centers on photonic interconnects for chip multiprocessors , energy-efficient software optimization for multi-core/GPU architectures, and performance-portable parallel programming models . Current investigations span cryptographic acceleration, blockchain algorithms, and hardware/software co-design for emerging computing paradigms, with strong emphasis on practical implementations bridging theoretical advances and real-world applications. Publication Trends: Recent publications (2019-2023) reveal three dominant threads: (1) Photonic network innovations addressing energy bottlenecks in chip multiprocessors, (2) The PHAST library ecosystem enabling seamless CPU/GPU programming across domains from autonomous vehicles to UAV navigation, and (3) Hardware accelerator designs for convolutional networks and cryptographic workloads. These works consistently target performance-portability challenges in heterogeneous computing environments. Grants and Collaborations: As principal investigator for the Italian Ministry-funded PHOTONICA project, he established international research partnerships with Murcia University, Columbia University, and Hong Kong University of Science and Technology, while securing industry collaborations with STMicroelectronics, Intel Munich, IBM, and IMEC. He has also managed complex IT system deployments for Siemens Italy, RAI (Italian public broadcasting), and SpaceDys. Academic Leadership: Bartolini serves as Associate Editor for the Eurasip Journal of Embedded Computing and actively contributes to the European HiPEAC network. His research group at Siena maintains strong industry ties for technology transfer, particularly in photonic interconnect validation and parallel programming frameworks for next-generation computing systems.
Dominique Blouin is an Associate Professor at Telecom Paris, Institut Polytechnique de Paris, and a member of the Autonomous Critical Embedded Systems (ACES) research group within the Information Processing and Communications Laboratory (LTCI). With a PhD in computer science from the University of South Brittany and prior industrial experience as a software architect at Cassiopae and research engineer at Lab-STICC, Blouin bridges academic research and industrial application in model-based engineering. Bachelor of Science in Physics, University of Sherbrooke (1989) Master of Science in Astrophysics, University of British Columbia (1994) PhD in Computer Science, University of South Brittany (2011) Blouin’s research focuses on multi-paradigm modeling (MPM) for cyber-physical systems (CPS), emphasizing model management, domain-specific language (DSL) development, and scalability of model-based approaches. Key projects include the ontological foundation for MPM4CPS and the ALISA framework for architecture-led system assurance. Their work leverages the SAE AADL standard for industrial applications, addressing challenges in model synchronization, requirements engineering, and power-aware design. Recent publications highlight advancements in AADL-based model management, incremental transformation tools, and ROS-based robotic system modeling. Blouin’s contributions to model-driven engineering have been recognized with the SoSyM-First Paper award at MODELS 2022. SoSyM-First Paper award, MODELS 2022 Blouin has supervised numerous PhD, Master’s, and Bachelor’s students, authored tools like RAMSES and OSATE-DIM for AADL processing, and participated in standardization committees (SAE AADL) and organization of international workshops (MPM4CPS, MODELS). Their teaching includes object-oriented programming, embedded system modeling with AADL, and real-time systems courses.
Amir Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, and mobility in distributed systems for emerging technologies like IoT, Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). University: University of Oslo Department: Informatics Academic Rank: Professor His research spans IoT, Edge/Fog Computing, and Cyber-Physical Systems, emphasizing energy efficiency, privacy preservation, and self-adaptive architectures. Key areas include network traffic classification, computation offloading, and federated learning applications in vehicular systems. Recent publications highlight advances in latency-aware IoT data transmission , federated vehicular networks , energy-efficient wireless charging , and privacy-preserving data integration . These works often integrate machine learning with network optimization. Projects include the CPS Lab at UiO, DILUTE (Fluid Service Abstraction), and the Gemini Centre on IoT . He collaborates on initiatives like PACE for energy informatics curricula development.
Dr. Gul N. Khan is a Professor in the Department of Electrical, Computer and Biomedical Engineering at Toronto Metropolitan University (formerly Ryerson University). He has held academic positions at the University of Saskatchewan, Nanyang Technological University, RMIT University, and Quaid-i-Azam University. His career spans over three decades with a focus on embedded systems , network-on-chip (NoC) , and heterogeneous computing . Education: Ph.D. (Imperial College, 1989), M.Sc. (Syracuse University, 1982), B.Sc. (UET Lahore, 1979) Dr. Khan’s research interests include hardware-software co-design , CPU-GPU systems , fault-tolerant computing , and smart RFID systems . His work has led to over 125 refereed publications and three US patents. His recent publications highlight advancements in GPU auto-tuning , NoC synthesis , and digital time interpolators . Despite being listed in a Google Scholar block with unrelated public health topics, these appear to be errors, as his core expertise remains in computer engineering. Dr. Khan has supervised numerous graduate projects in embedded systems and SoC design . He served as Program Director for Computer Engineering from 2004–2015 and leads the Microsystems Research Lab at Toronto Metropolitan University.
Billy Moses is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), with affiliate roles in Electrical and Computer Engineering (courtesy) and the Coordinated Science Library. He holds a PhD and dual S.B. degrees in Electrical Engineering and Computer Science from MIT (2023, 2017), as well as an S.B. in Physics from MIT (2017). His research focuses on compilers, parallel computing, and compiler-driven optimization techniques for high-performance systems. Moses has pioneered work on the Tapir framework for fork-join parallelism, the MLIR compiler infrastructure, and Enzyme for automatic differentiation. Moses' research spans compiler design, GPU acceleration, and AI-driven compiler optimization. Notable contributions include the Polygeist compiler for C-to-MLIR transformation, the Autophase reinforcement learning system for HLS phase ordering, and the Enzyme framework for GPU kernel differentiation. His work emphasizes practical compiler solutions for parallelism, performance portability, and end-to-end code generation in domains like deep learning and scientific computing. Awards: 2024 SIGHPC Doctoral Dissertation Award Courses Taught: CS 598 APE (Advanced Performance Engineering) His recent projects include compiler-based approaches to GPU-to-CPU transpilation, performance portability in heterogeneous systems, and AI-driven compiler decision-making. Moses collaborates with industry and academic partners on advancing compiler technologies for exascale computing and machine learning acceleration.
Zapater Sancho Marina is an Associate Professor at the ReDS Institute (Institute of Reconfigurable and Embedded Digital Systems) within the School of Engineering and Management Vaud (HEIG-VD), part of the University of Applied Sciences and Arts Western Switzerland (HES-SO). She holds dual master's degrees in Electronic and Telecommunication Engineering from Universitat Politècnica de Catalunya (2010) and a PhD in Computer Science from Universidad Politécnica de Madrid (2015). Her career includes postdoctoral work at EPFL (2016-2020) and assistant professorship at Universidad Complutense de Madrid (2015-2016). Education BSc & MSc in Electronic Engineering (UPC 2010) PhD in Computer Science (UPM 2015) Research Focus spans cross-layer optimization of heterogeneous architectures for performance and energy efficiency, with emphasis on: Embedded systems (IoT/edge computing) High-performance compute architectures Analog in-memory computing for AI Thermal/power management in 3D chips Cloud-edge AI workload orchestration Publication Trends show expertise in RISC-V simulation frameworks, analog computing tiles for CNNs, virtual memory redesign, and AI-driven cloud performance prediction. Her recent work explores thermal-aware 3D chip management, hybrid-cache reliability optimization, and open-source teaching platforms for radio theory. Awards include a Spanish government PhD fellowship. She has led 4 European H2020 projects since 2016 and currently serves as PI for 4 industrial collaborations (Facebook/Intel/Huawei), Innosuisse projects, and HES-SO initiatives. Labs & Teams include the ReDS Institute, EPFL's Embedded Systems Laboratory, and collaborations with Yale/Edinburgh. She co-developed the ALPINE simulation framework and SO3 operating system modifications for Midgard project validation.
Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.
Dimitrios Rozakis is an Assistant Professor of Mechanical and Aerospace Engineering. He is actively engaged in research and teaching at the College of Engineering, where he leads the Aerodynamics & Propulsion Laboratory . Education PhD in Aerospace Engineering, National Technical University of Athens (2012) MSc in Fluid Mechanics, University of Manchester (2008) Diploma in Mechanical Engineering, Aristotle University of Thessaloniki (2006) Research Interests His research spans computational and experimental aerodynamics , with particular emphasis on: Transonic and supersonic flows Flow control using plasma actuators Hypersonic boundary-layer transition Reduced-order modelling and machine-learning techniques Turbomachinery aerodynamics Recent work has focused on high-fidelity simulations of buffet phenomena, experimental investigations of plasma-based separation control, and the development of data-driven surrogate models for unsteady aerodynamic loads. Selected Scientific Awards ASME Best Paper Award (2020) European Research Council Starting Grant (2018) AIAA Young Investigator Award (2016) Students, Grants & Funding He currently supervises three PhD students—Maria Koutsogianni, Panagiotis Giannakakis, and Eleni Christoforou—working on projects funded by the ERC, Horizon Europe, and the Greek Secretariat for Research & Technology. Active grants include an ERC Starting Grant on “Physics-informed machine learning for unsteady aerodynamics” (€1.5 M) and a Horizon Europe project on “Green regional aircraft technologies” (€4.2 M). Laboratory & Collaborations He directs the Aerodynamics & Propulsion Laboratory , which houses low-speed and transonic wind tunnels, a Ludwieg-tube facility for short-duration hypersonic experiments, and a high-performance computing cluster (>2 000 CPU cores). Ongoing collaborations include the von Karman Institute, DLR, and ONERA.
Gerhard Wellein is a Professor for High Performance Computing at the Department of Computer Science of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is the head of NHR@FAU (Erlangen National Center for High Performance Computing) and a member of the board of directors of the German NHR-Alliance. Since 2024, he has also served as a Visiting Professor for HPC at the Delft Institute of Applied Mathematics, Delft University of Technology. He holds a PhD in theoretical physics from the University of Bayreuth and has over two decades of experience in HPC education and research. Research Interests: His research focuses on performance modeling and engineering, architecture-specific code optimization, novel parallelization techniques, and the development of hardware-efficient building blocks for sparse linear algebra and stencil solvers. His work bridges computer science, applied mathematics, and computational physics, aiming to maximize efficiency on current and future HPC architectures, including exascale systems. Publication Trends: His recent publications emphasize analytical performance modeling (e.g., Roofline, oscillator models), energy efficiency, GPU optimization, and scalable linear algebra. They reflect a strong focus on both theoretical modeling and practical implementation, with applications in CFD, quantum physics, and molecular dynamics. Scientific Awards: 2011 Informatics Europe Curriculum Best Practices Award (shared with Jan Treibig and Georg Hager) for outstanding teaching contributions in HPC. Grants and Advising: He has led numerous third-party funded projects from the EU, BMBF, and DFG, including EoCoE-III, ESSEX, EXASTEEL, and ProPE. These projects focus on exascale software, performance engineering, fault tolerance, and multiscale simulation. He has mentored multiple researchers and students, contributing to the development of tools such as LIKWID, ClusterCockpit, and GEOPM. Labs and Teams: He leads the HPC research group at FAU and is deeply involved in national and international HPC initiatives. His team collaborates extensively on open-source HPC software and performance tools, fostering a strong community-driven approach to performance engineering.
