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Sriram KrishnamoorthyView profile

Research Professor

• Parallel Programming Models
• Fault Tolerance
• High-Performance Computing
• +6 more

Sriram Krishnamoorthy is a Research Professor at Washington State University's School of Electrical Engineering & Computer Science and a research scientist at Pacific Northwest National Laboratory (PNNL), where he serves as the System Software and Applications Team Leader in PNNL's High Performance Computing group. Dr. Krishnamoorthy earned his B.E. from the College of Engineering, Guindy in Chennai, India, and his M.S. and Ph.D. degrees from The Ohio State University. He is a senior member of the Institute of Electrical and Electronics Engineers. His research focuses on parallel programming models, fault tolerance, and compile-time/runtime optimizations for high-performance computing. He has made significant contributions in areas including: Fault tolerance techniques that minimize rollback during failures Dynamic load balancing for irregular parallel applications Compiler and runtime optimizations for HPC applications GPU programming and heterogeneous computing Quantum chemistry simulations and quantum computing Dr. Krishnamoorthy's publications span computational science, high-performance computing, and quantum chemistry. His recent work shows strong trends toward quantum computing applications, fault tolerance in large-scale systems, and optimization of computational chemistry methods. He has developed techniques for density matrix quantum circuit simulation, floating-point error analysis, and scalable execution of coupled-cluster models. His scientific achievements have been recognized with several prestigious awards: Best Paper Award at International Conference on High Performance Computing (HiPC'03) Best Paper Award at International Parallel and Distributed Processing Symposium (IPDPS'04) U.S. Department of Energy Early Career award (2013) PNNL's Ronald L. Brodzinski Award for Early Career Exceptional Achievement (2013) The Ohio State University's Outstanding Researcher award (2008) Dr. Krishnamoorthy has advised numerous graduate students and collaborated extensively with researchers across computational science domains. His work on the NWChem project demonstrates significant grant funding and large-scale collaborative research efforts in computational chemistry. He leads research efforts in PNNL's High Performance Computing group, focusing on system software and applications development for next-generation supercomputing platforms.

Hadjiefthymiades StathesView profile

Professor

• IoT Security
• Cloud Computing
• Machine Learning
• +7 more

Hadjiefthymiades Stathes is a Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His primary research division focuses on Computer Systems and Applications. He specializes in IoT security, cloud computing, machine learning, and edge computing, with notable contributions to data analytics, trust management, and cybersecurity in distributed systems. Research Interests: His work spans IoT security protocols, optimization of cloud storage systems, anomaly detection in Kubernetes environments, and predictive analytics for sensor networks. He has explored trust models in pervasive computing, energy-efficient node authentication, and hardware-accelerated mixed reality interfaces. His recent projects include frameworks for Earth observation data accessibility and next-generation internet experimentation platforms. Publications: His recent articles highlight advancements in IoT data reduction techniques, trust sustainability in infrastructure, and the application of Markov chains for multivariate event correlation. He has also contributed to the design of experiment description languages for mobile IoT and emergency response platforms for radiological incidents. Awards & Grants: No scientific awards or grants were explicitly mentioned in the provided texts. Labs & Teams: His research involves collaborations on projects like EO4EU-AI (Earth observation with extended reality interfaces) and TRACE (logistics integration systems). He is affiliated with the university's advanced systems research groups focusing on edge computing and secure IoT ecosystems.

George PapagiannakisView profile

Professor

• Mixed Reality
• Computer Graphics
• Virtual-Augmented Reality
• +6 more

George Papagiannakis is a Professor of Computer Graphics at the University of Crete, Greece, and a Marie Sklodowska-Curie Fellow at FORTH's Institute of Computer Science. He holds visiting professorships at the University of Geneva and leads the Computer Graphics Group. His research focuses on mixed reality, GPU programming, and medical XR applications. He earned his PhD from the University of Geneva (2006), M.Sc. from Bristol, and B.Eng. from Manchester. His work includes pioneering projects like MAGES SDK (medical XR training platform) and pyGANDALF (educational framework). Key contributions span over 130 publications, 3400+ citations, and €8M in research funding. He actively participates in EU projects (HDRi, Hifi-PRINTER) and has been awarded the Marie-Curie Fellowship, Best Paper Awards, and NVIDIA grants. Teaching includes advanced courses on computer graphics and programming at the University of Crete. He advises students across 4 postdocs, 15 PhDs, and numerous MSc/BSc candidates. His lab develops cutting-edge tools for immersive technologies, including geometric algebra applications in 5G/6G XR environments.

• Real-time computer resource management systems
• Heterogeneous memory systems
• Programming models for heterogeneous computing systems
• +2 more

Lazaros Papadopoulos serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Democritus University of Thrace since May 2024, focusing on computational resource management for embedded and high-performance systems. His work bridges hardware-software co-design with energy-efficient computing paradigms. His academic credentials include: Bachelor's in Electrical and Computer Engineering from Democritus University of Thrace (2005) Master's degree from the same institution (2008) PhD from the National Technical University of Athens (2016) Research centers on optimizing computing infrastructure through real-time resource management, heterogeneous memory architectures, and AI workload acceleration. His investigations target energy-performance tradeoffs in GPU systems, persistent memory integration, and edge-device neural network deployment, with direct applications in sustainable computing and high-performance data processing. Publication analysis (2018-2022) reveals consistent focus on heterogeneous system optimization, particularly energy-aware GPU acceleration frameworks, memory management for DRAM/NVM hybrids, and efficient CNN implementations for edge devices. Key thematic threads include sustainable computing practices, hardware-aware AI deployment, and memory hierarchy innovations. Scientific recognition includes: Two Best Paper Awards Research leadership spans major EU initiatives: Work Package Coordinator for LAGO (Horizon Europe, 2022-2024) and PRAETORIAN (H2020, 2022-2024); Technical Coordinator for EXA2PRO (H2020, 2018-2021); and Work Package Coordinator roles in SDK4ED (H2020, 2018-2020) and EXCESS (H2020, 2013-2016). These projects address embedded systems security, exascale programming models, and energy-efficient computing toolchains. He operates within the Computer Architecture and High Performance Systems laboratory, directing research on computational resource orchestration for next-generation heterogeneous platforms.

Panagiota FatourouView profile

Professor

• Parallel and Distributed Computing
• Concurrent Data Structures
• Transactional Memory
• +8 more

Dr. Panagiota Fatourou is a Professor in the Department of Computer Science at the University of Crete and a senior researcher at the Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH). She holds a dual affiliation, contributing significantly to both institutions. She has previously served as faculty at the University of Ioannina and held postdoctoral positions at the Max-Planck-Institut für Informatik and the University of Toronto. She has also been a visiting professor at École Polytechnique Fédérale de Lausanne (EPFL). Her research spans the theoretical and practical foundations of parallel and distributed computing. She is a leading expert in concurrent data structures, transactional memory, and the use of persistent memory in system design. Her work integrates theoretical rigor with practical implementation, often publishing in top-tier venues like PPoPP, PODC, SPAA, and PVLDB. Recent publications highlight her focus on efficient garbage collection for multiversioning, persistent data structures, and distributed data series similarity search, reflecting a trend towards high-performance, fault-tolerant systems for modern hardware. Best Paper Award, PPoPP 2022 Marie-Curie Individual Fellowship FSMP Distinguished Professor Fellowship ACM Distinguished Speaker Featured ACM Member Dr. Fatourou has a strong record of leadership and mentorship. She has advised numerous MSc and PhD students and is actively recruiting for her PERSIST project, funded by HFRI. She has led major research initiatives, including the EU-funded TransForm network and the GreenVM project. Her leadership extends to the broader community, where she has served as Past Chair of the ACM Europe Council, Chair of the Greek ACM-W Chapter, and on the steering committees of PODC and OPODIS. She has also co-organized key events on gender equality in computing, such as the Summits on Gender Equality in Computing (GEC) and the WISTEM Summer School. She is deeply involved in the academic service, frequently serving on program committees for premier conferences such as SPAA, NETYS, and SRDS. Her laboratory and research team at FORTH ICS and the University of Crete focus on building the theoretical underpinnings and practical implementations of next-generation concurrent and distributed systems.

Tiago CogumbreiroView profile

Assistant Professor

• Concurrency Theory
• Parallel Programming
• Formal Methods
• +6 more

Tiago Cogumbreiro is an Assistant Professor at the University of Massachusetts Boston, where he has been a faculty member since Fall 2018. His research focuses on advancing the foundations of parallel programming through rigorous quality assurance of languages and runtimes. Dr. Cogumbreiro received his PhD from the University of Lisbon (ULisboa) in March 2015 under Francisco Martins, where he developed techniques to handle barrier deadlocks including the Armus runtime verification tool. His educational background includes a B.Sc. from Universidade dos Açores, followed by research assistant work at Imperial College London (supervised by Nobuko Yoshida) and postdoctoral research at Georgia Tech and Rice University (supervised by Vivek Sarkar). As an expert in formal methods for high-performance computing, Dr. Cogumbreiro's work centers on detecting concurrency errors in parallel programs with special emphasis on GPU systems. His research spans theoretical contributions to deadlock avoidance policies and practical applications of Coq and Why3 for certified algorithms. Recent work demonstrates sophisticated approaches to static analysis of data-races in GPU programming, addressing fundamental challenges in parallel system reliability. His publication pattern reveals a consistent trajectory from foundational work on futures-based deadlock avoidance (2017) through behavioral type systems (2019) to current innovations in GPU program analysis (2023-2024). These contributions form a cohesive research program focused on mathematically rigorous approaches to parallel system correctness. Dr. Cogumbreiro actively contributes to the programming languages community through committee service at major conferences including PLDI (2020 Artifact Evaluation), SPLASH (2025 OOPSLA Review Committee), and PPoPP. His GitHub activity shows ongoing development of formal verification tools, particularly Coq-based projects like gorn-coq and habanero-coq that implement his theoretical contributions.

Christos KotselidisView profile

Associate Professor

• Virtual Machines
• Compilers
• Hardware/Software Co-design
• +7 more

Christos Kotselidis is an Associate Professor at the University of Manchester's School of Computer Science and serves as Chief Engineer at Pierer Innovation (previously mentioned as KTM Innovation). He leads the TornadoVM project, a groundbreaking Java Virtual Machine for heterogeneous hardware acceleration. His work bridges academia and industry, focusing on hardware/software co-design for virtual machines and embedded systems. His research spans chip design, micro-architecture, compilers, virtual machines, and garbage collection. Dr. Kotselidis has pioneered work in heterogeneous computing, particularly in accelerating Java applications on GPUs, FPGAs, and RISC-V architectures. His team's recent development of GPULlama3.java demonstrates the practical application of his research in bringing LLM inference to pure Java with GPU acceleration. His publications show a consistent focus on bridging hardware capabilities with managed runtime systems. The research trajectory reveals increasing sophistication in handling memory hierarchies, parallelism, and cross-architecture execution. His work has significant implications for high-performance computing, AI acceleration, and sustainable computing practices. Dr. Kotselidis actively contributes to the academic community as a program committee member for conferences like ISMM and as an organizer for workshops including VMIL and MoreVMs. He's also involved in EU-funded projects like AERO and DARE, which focus on European processor initiatives and software ecosystems. As project lead for TornadoVM, he mentors a research team at Manchester including Michalis Papadimitriou, Mary Xekalaki, and Thanos Stratikopoulos. His lab (Beehive Lab) develops open-source tools that are widely used in both academic and industrial settings for heterogeneous computing.

Fabrice RastelloView profile

Researcher

• Automatic Parallelization
• Compiler Back-End Optimization
• Static Single Assignment (SSA)
• +4 more

Fabrice Rastello is a leading researcher at Inria, France, where he serves as the leader of the CORSE (Compiler Optimization and Runtime SystEms) research team. His expertise spans automatic parallelization and compiler back-end optimization, with significant contributions to Static Single Assignment (SSA) form theory and register allocation techniques. Affiliated with University Grenoble Alpes - Inria - CNRS - Grenoble INP - LIG, he maintains an active research profile with continuous publications in top-tier conferences including PLDI, CGO, and PPoPP. His research interests focus on combining theoretical foundations with practical applications in compiler design. Key areas include automatic parallelization techniques (building on his PhD work on tiling as a loop transformation), compiler back-end optimization, and the emerging field of hybrid compilation that bridges static compilation with runtime techniques. He has made significant contributions to register allocation for JIT compilation in the context of SSA properties, advising three PhD students on related topics. Analysis of his recent publications reveals a strong trend toward data movement optimization, with numerous papers on I/O complexity bounds, cache modeling, and microarchitectural performance analysis. His work spans both theoretical computer science and practical applications, including contributions to healthcare technology during the pandemic with the eSpiro ventilator project. He has also explored intersections between compiler techniques and machine learning, particularly in using ML for performance modeling. Fabrice Rastello serves on program committees for major conferences in the field, including PLDI, CGO, and CC, demonstrating his standing in the research community. His 2022 book 'SSA-based Compiler Design' as editor further establishes his authority in compiler theory and practice. His recent work shows continued innovation in compiler techniques, with publications in 2024 demonstrating ongoing research activity across multiple domains including cache modeling, program analysis, and machine learning applications.

Yiannis SaridakisView profile

Professor

• Applied Mathematics
• Scientific Computing
• Parallel Algorithms
• +8 more

Professor Yiannis Saridakis serves as a Professor in the Department of Sciences at the Technical University of Crete, leading the Applied Mathematics and Computers Laboratory. He holds a Mathematics degree from the University of Crete (1981) and M.Sc./Ph.D. in Applied Mathematics & Computer Science from Clarkson University (1983/1985). His research focuses on numerical methods, parallel algorithms, stochastic optimization, cellular automata, and AI applications. He has coordinated projects like the TU Crete Library portal and digitization initiatives. His work bridges computational mathematics with environmental, biomedical, and engineering challenges. Education B.Sc. Mathematics, University of Crete (1981) M.Sc. Applied Mathematics & Computer Science, Clarkson University (1983) Ph.D. Applied Mathematics & Computer Science, Clarkson University (1985) Research Interests His research spans Recursive Linear Systems Solving , Numerical Methods for PDEs , Parallel Computing Architectures , Stochastic Optimization (e.g., adaptive optics, aquifer management), Cellular Automata , and Artificial Intelligence applications like agent-based systems and ambient intelligence. Key Contributions Prof. Saridakis pioneered GPU-accelerated Hermite collocation methods, Fokas transform applications in tumor modeling, and ALOPEX stochastic optimization for water resource management. He has published >50 papers in top journals and conference proceedings. Labs & Leadership Director of the Applied Mathematics and Computers Laboratory . Former Department Chair (1997–1999) and Vice-Rector (1999–2005) at TU Crete.

Nikolaos M. MissirlisView profile

Professor

• Scientific Computing
• Parallel Computing
• Numerical Analysis
• +2 more

Nikolaos M. Missirlis is a Professor at the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens (NKUA) since 1995. He earned his BSc in Applied Mathematics from the University of Ioannina, Greece in 1974 and his PhD in Numerical Analysis from Loughborough University of Technology, UK in 1978. He has held visiting positions at Rutgers University (1986-1987), Cyprus University (2006-2008), and SUNY Stony Brook (2013). His research focuses on Scientific Computing , particularly parallel solution of Partial Differential Equations and load balancing algorithms. His work bridges theoretical mathematics with high-performance computing applications in meteorology and fluid dynamics. His publications demonstrate consistent output in parallel algorithms and numerical methods, with recent work emphasizing GPU acceleration and diffusion-based load balancing techniques. Research trends show evolution from foundational iterative methods to applications in heterogeneous computing environments. Teaching activities include undergraduate courses: Numerical Analysis Numerical Linear Algebra Algorithms and Complexity Introduction to Programming Data Structures Graduate courses: Scientific Computing (Numerical Solution of PDEs) Parallel Algorithms He has advised five PhD students and led major projects including: PYTHAGORAS (Distributed iterative methods for weather prediction) SKIRON (Parallelization of Eta model for Hellenic Meteorology Service) Princeton Ocean Model parallelization Contact: nmis@di.uoa.gr | +30 210 7275103 | Department of Informatics and Telecommunications, NKUA, Panepistimiopolis 157 84, Athens, Greece.

• Operating Systems
• Parallel Computing
• Scheduling in Parallel Systems
• +3 more

Venetis Ioannis is an Assistant Professor at the Department of Informatics, School of Information and Communication Technologies, University of Piraeus. His research focuses on operating systems, parallel computing, and high-performance computing applications in seismology and computational neuroscience. His research interests include: Operating Systems Parallel Computing Models Scheduling in Parallel Systems Real-Time Parallel Applications Seismology and Computational Neuroscience GPU and Many-Core Optimization Recent publications highlight his work on heterogeneous cluster scheduling, GPU-based neuronal modeling, and seismic event detection using grid search algorithms. Venetis has participated in European and national research programs related to parallel computing and high-performance applications. He teaches courses such as Operating Systems, Parallel Processing, and Assembly Language Programming, with a focus on practical implementations in high-performance environments.

• Computer Graphics
• CAD Systems
• Geometric Modeling
• +2 more

Ioannis Fudos is Professor and former Chair (2014-2018) of the Computer Science and Engineering Department at the University of Ioannina. He holds a Diploma in Computer Engineering from University of Patras, and MS/PhD degrees in Computer Science from Purdue University. His research spans computer graphics, CAD systems, geometric modeling, animation, and image retrieval. He directs the Computer Graphics Research Group and has secured substantial funding (€2M+) from European and national sources for 17+ projects. His scholarly contributions include publications in top venues (SIGGRAPH, Eurographics, IEEE TVCG) covering rendering techniques, 3D printing quality control, assistive technologies, and cultural heritage digitization. Recent work explores neural rendering transparency methods, wheelchair navigation interfaces, and pedestrian routing algorithms. His laboratory maintains a repository of 3D models for research use.

Tobias GrosserView profile

Associate Professor

• Compilers
• Programming Language Design
• Performance Programming
• +8 more

Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on compiler technology, programming language design, and performance programming, with applications spanning hardware design, climate science, and quantum computing. He leads a research group developing innovative compiler frameworks and tools that bridge theoretical foundations with practical applications. Dr. Grosser completed his undergraduate studies in Computer Science at the University of Passau in Germany and pursued his PhD at École Normale Supérieure Paris as a Google PhD Fellow. Prior to joining Cambridge, he served as a Reader at the University of Edinburgh and held an Ambizione Fellowship at ETH Zurich. His research program centers on rethinking performance programming by re-connecting developers and compilers. He aims to make compilation more modular, predictable, automatic, and trustworthy while bringing open-source compiler innovation to increasingly diverse targets from GPUs to FPGAs and custom hardware. His work spans multiple domains including polyhedral compilation, constraint solving, quantum computing, and hardware design automation. Dr. Grosser is particularly interested in breaking down barriers between compilers and programmers by enabling their interaction through the programming language environment. His recent publications demonstrate a strong focus on compiler infrastructure development, particularly around the MLIR framework. He has pioneered work on Presburger arithmetic optimization with the FPL library, developed new intermediate representations for hardware description and quantum computing, and created tools for compiler education and prototyping like xDSL. His research shows a consistent theme of creating practical, high-performance compiler technologies that address real-world challenges across multiple domains. HiPEAC Technology Transfer Award 2021 for "Fast linear programming through transprecision computing on small and sparse data" OOPSLA 2021 Distinguished Paper Award for "FPL: Fast Presburger arithmetic through transprecision" Dr. Grosser actively mentors PhD students and postdoctoral researchers, currently supervising a team of over a dozen researchers working on various aspects of compiler technology. His group collaborates with industry partners including ARM and Xilinx, and maintains strong ties with the LLVM and MLIR open-source communities. He has secured funding for multiple research projects including work on verified compilation with Lean-MLIR, quantum compiler development, and hardware design automation. His research group operates at the intersection of multiple projects including Open-Source Electronic Design Automation, Seamless design of Smart Edge Processors, Lean-MLIR for verified compilation, FPL for fast Presburger arithmetic, and compilation frameworks for quantum computers. They maintain strong community engagement through regular Compiler Social events in Cambridge and active participation in LLVM developer meetings.

Santosh PandeView profile

Professor

• Program Analysis
• Software Security
• Compiler Optimizations
• +3 more

Santosh Pande is a Professor and Associate Chair for Graduate Studies in the School of Computer Science at Georgia Institute of Technology. His research focuses on compiler analysis and optimizations, providing deep insights into software behavior to improve various system properties through automated methods. His research interests include: Program Analysis for Security Compiler Optimizations and security implications Compiler-OS interactions for resource management Real-time software analysis Machine learning systems performance optimization Dr. Pande's recent publications demonstrate a strong focus on security-oriented compiler techniques, particularly in program debloating to reduce attack surfaces. His work addresses the critical intersection between compiler optimizations and security vulnerabilities, as well as innovative approaches to scheduling and resource allocation through compiler-OS collaboration. Research results show significant performance improvements (2x to 4x) for batch-oriented execution of modern workloads in data-center environments. His research has been consistently funded by: National Science Foundation (NSF) Office of Naval Research (ONR) Defense Advanced Research Projects Agency (DARPA) Air Force Industry partners including Sony, Toshiba, IBM, Motorola, and Infineon Dr. Pande holds a patent on smartcards developed in collaboration with Infineon. He actively mentors PhD students and seeks "smart and motivated PhD students" to join his research group. His work has resulted in over 100 publications and open-source software contributions. He is affiliated with multiple research centers at Georgia Tech including the Center for Experimental Research in Computer Systems (CERCS) and contributes to the Online Master of Science in Computer Science (OMSCS) program.

Saday SadayappanView profile

Professor

• Compiler Optimization
• High-Performance Computing
• Sparse/Dense Matrix Computations
• +3 more

P. (Saday) Sadayappan is a Professor at the University of Utah's School of Computing, specializing in high-performance computing and compiler optimizations. His research focuses on performance optimization for parallel systems, particularly for tensor computations, sparse matrix operations, and machine learning workloads. He leads multiple NSF and DARPA-funded projects focused on GPU optimization, tensor computations, and scalable machine learning frameworks. Research Interests: Dr. Sadayappan's work spans compiler optimizations for high-performance systems, optimization of sparse/dense matrix/tensor computations, scalable machine learning, and algorithm-architecture co-design. His recent projects include developing performance-portable frameworks for tensor applications and optimizing data locality for scientific computing. Publication Trends: His recent publications (2020-2022) predominantly focus on GPU acceleration of machine learning workloads (especially CNNs), automated I/O complexity analysis, and optimization techniques for sparse matrix/tensor operations. Earlier work (2018-2019) established foundations in GPU code generation for tensor contractions and cache optimization. Awards and Honors: ACM SIGPLAN Most Influential PLDI Paper Award (2018) for A Practical Automatic Polyhedral Parallelizer and Locality Optimizer Active Grants and Projects: NSF: A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications (2022-2027) NIH SBIR: Enabling next generation machine learning for large scale image analysis (2023-2025) NSF: AI Institute for Intelligent CyberInfrastructure (ICICLE) (2021-2026) NSF: Data Locality Optimization for Sparse Matrix/Tensor Computations (2020-2024) DARPA SBIR: Performance Portable Framework for Developing Graph Applications (2017-2022) Teaching: He currently teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah.