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Sylvain LefebvreView profile

Researcher

• Computer Graphics
• Texture Synthesis
• Additive Manufacturing
• +4 more

Sylvain Lefebvre is a permanent researcher at INRIA (Institut National de Recherche en Informatique et en Automatique) in France, where he leads the MFX research team since 2018. Previously, he was part of the ALICE group at INRIA Nancy (2009-2018) and the REVES team in Sophia Antipolis (2006-2009). His career includes a postdoctoral position at Microsoft Research Seattle (2005) following his PhD at INRIA Rhones-Alpes under Fabrice Neyret. His educational background includes a PhD in Computer Graphics from Université Joseph Fourier (Grenoble) in 2005, preceded by a Master in Computer Graphics from INP Grenoble in 2001. His habilitation thesis focused on Runtime Texture Synthesis. Lefebvre's research centers on simplifying content creation for highly detailed patterns, structures, and shapes with applications spanning Computer Graphics to additive manufacturing. He develops fast, controllable by-example synthesis approaches that generate content while enforcing user-specified constraints. His work addresses computational challenges through novel data structures and algorithms optimized for GPUs and FPGAs, including his Silice programming language. The ERC-funded ShapeForge project (2012-2017) advanced shape generation for 3D printing, leading to the IceSL software for digital modeling and fabrication. Analysis of his 15 most recent publications reveals a strong focus on additive manufacturing optimization, with recurring themes in structural integrity, material efficiency, and geometric algorithms. His work bridges computer graphics theory with practical fabrication constraints, particularly in microstructure design, slicing techniques, and mechanical metamaterials. The interdisciplinary nature spans computer science, materials engineering, and robotics. EUROGRAPHICS Young Researcher Award (2010) ERC Starting Grant for ShapeForge project (2012) Lefebvre has advised over 25 PhD students and interns including Marco Freire, Thibault Tricard, and Jimmy Etienne. His ShapeForge project received significant ERC funding, supporting research in computational fabrication. He serves on numerous program committees including SIGGRAPH, Eurographics, and SIGGRAPH Asia, reflecting his leadership in the computer graphics community. As leader of the MFX team since 2018, Lefebvre directs research in computational fabrication, focusing on IceSL software development for 3D printing workflows. The team integrates computer graphics techniques with manufacturing constraints, developing tools that simplify complex object design and fabrication while addressing real-world challenges in material usage and structural integrity.

Torsten HoeflerView profile

Professor

• Deep learning Systems
• Parallel programming models
• High performance and datacenter networking
• +8 more

Torsten Hoefler is a Full Professor of Computer Science at ETH Zurich, Switzerland, with an adjunct appointment in Electrical Engineering. He previously held roles at the National Center for Supercomputing Applications (University of Illinois at Urbana-Champaign) and Indiana University. Full Professor of Computer Science, ETH Zurich (2020–present) Adjunct Professor of Electrical Engineering, ETH Zurich (2020–present) Member at Large, ACM SIGHPC Executive Committee (2013–present) Leadership roles in the MPI Forum and Blue Waters project His research focuses on performance-centric system design , with emphasis on scalable networking, parallel programming models, and performance modeling. Key contributions include the Slim Fly network topology, Data-Centric Python framework, and innovations in parallel graph computations and RDMA-based systems. Recent publications span topics like LLM training networks , quantization geometry , chiplet interconnects , and AI-driven climate modeling , reflecting his interdisciplinary approach combining HPC, AI, and hardware-software co-design. ACM Gordon Bell Prize (2019) ERC Consolidator Grant (2020) IEEE TCSC Award for Excellence (2019) SIAM SIAG/SC Junior Scientist Prize (2012) Latsis Prize of ETH Zurich (2015) He has received multiple best paper awards at top conferences (SC10, SC13, SC14, SC19, IPDPS'15, HPDC'15, OOPSLA'16) and contributed to MPI-3 standardization.

Lukas EinkemmerView profile

Professor

• Numerical Analysis
• Plasma Physics
• Computational Mathematics
• +3 more

Prof. Lukas Einkemmer is a faculty member at the University of Innsbruck, holding a position in the Institute of Mathematics. He specializes in numerical analysis, plasma physics, and high-performance computing. His work focuses on developing advanced numerical methods for solving complex kinetic equations and PDEs, with applications in plasma simulation and computational fluid dynamics. Education: He earned a PhD in applied mathematics (2014) and MSc in physics (2013) from the University of Innsbruck, alongside BSc in applied mathematics (2010). He completed research stays at UC Merced and holds notable academic awards, including the SciCADE New Talent Award (2015) and participation in the Heidelberg Laureate Forum (2013). Research & Teaching: His research includes exponential integrators, dynamical low-rank methods, and semi-Lagrangian discontinuous Galerkin schemes. He teaches numerical methods, PDEs, and computational courses at both undergraduate and graduate levels. He also leads training programs in parallel computing (OpenMP/MPI) at the University’s Research Center for High-Performance Computing. Publications & Grants: Over 70 peer-reviewed articles in journals like J. Comput. Phys. and SIAM J. Sci. Comput. , focusing on numerical algorithms and their applications. He has secured grants from FWF and other agencies, advancing methods for plasma physics and kinetic theory. Awards & Recognition: Multiple honors, including the Oberwolfach Leibniz Graduate Student award (2014) and sustained scholarship support for academic excellence.

Alexandru NicolauView profile

Professor

• Parallelizing compilers
• Supercomputing
• High Performance Computing (HPC)
• +5 more

Alexandru Nicolau is a Distinguished Professor and Chair of the Department of Computer Science at the University of California, Irvine (USA), where he has worked since 1992. He previously held positions as Associate Professor (1988–1992) and Assistant Professor (1984–1988) at UC Irvine and Cornell University, respectively. Education : B.A., Brandeis University (1980) MS (1981), Ph.D. (1984), Yale University Research Interests : A leading expert in parallelizing compilers , high-performance computing , and software-hardware co-design , Nicolau pioneered foundational techniques like Percolation Scheduling and Optimal Loop Parallelization . His work enables efficient exploitation of instruction-level parallelism in general-purpose programs, with applications in embedded systems , matrix algorithms , and GPU-based neural networks . He has also contributed to Electronic Design Automation (EDA) and lightweight synchronization protocols . Scientific Awards : IEEE Fellow (2014) ACM SIGPLAN Most Influential Paper (PLDI 20 years) EDAA/IEEE/ACM DATE Most Influential Paper (10 years) ACM ICS Most Influential Paper (25 years) 4 Best Paper awards (VLSI design 2003, ISHPC 2005, CASES 2008, IJCNN 2009) Advising & Grants : He has mentored notable scholars now at Stanford, McGill, and Google, and secured over $20M in funding from NSF , DARPA , and industry leaders like IBM and Intel . His professional service includes chairing ACM ICS’09 and PPOPP’13, and serving on steering committees for LCPC and ICS. Labs & Collaborations : His techniques have been adopted by IBM Watson, Siemens Munich, Fujitsu Labs Japan, and the open-source GCC compiler.

Michael WimmerView profile

Professor

• Computer Graphics
• Real-Time Rendering
• Point Clouds
• +4 more

Univ.Prof. Michael Wimmer is a Professor in the Department of Computer Graphics and Visualization at TU Wien's Faculty of Informatics. His research focuses on real-time rendering, point cloud processing, GPU computing, and computational design. He leads projects involving architectural visualization, thermal simulation, and integrative design frameworks combining 4D sketching with material modeling. His work bridges computer graphics with applications in architecture and engineering. ORCID: 0000-0002-9370-2663 Research Group: Network Lab Recent research emphasizes GPU-accelerated algorithms (e.g., LOD generation for 2 billion points), real-time rendering techniques, and deep learning approaches for surface reconstruction. Collaborations span thermal simulation with civil engineers and 4D design tools for architects. Notable contributions include: Developing PPSurf for detailed surface reconstruction using point convolutions Advancing Vulkan-based rendering pipelines in academic settings Integrating light polarization for HDR imaging His lab explores applications in architectural design metaphors, computational material assessment via GeoRadar, and thermal simulation using precomputed radiative transport. Students under his supervision focus on GPU optimization, 3D reconstruction, and VR ergonomics.

Henri BalView profile

Professor

• Distributed Systems
• Parallel Computing
• High-Performance Computing
• +5 more

Henri Bal is a Full Professor in the Department of Computer Science at Vrije Universiteit Amsterdam's Faculty of Sciences, leading a research group of over 20 members in High Performance Distributed Computing since 2005. His career at the university spans from Research Assistant (1982) to current Professor, with prior appointments including Associate Professor (1994) and split Full Professor roles across Computer Science and Physics (1998). Education: PhD in Computer Science, Vrije Universiteit Amsterdam (1989) MSc in Mathematics, Delft University of Technology (1982) Professor Bal's research integrates fundamental distributed systems with real-world applications, specializing in programming environments for clusters, grids, clouds, and mobile systems. His work spans semantic web reasoning, distributed model checking, and scientific applications in astronomy (LOFAR), high-energy physics (NIKHEF), and climate modeling (Utrecht University), evidenced by systems like Orca, MagPIe, Manta, and Ibis/JavaGAT. Scientific Awards: NWO PIONIER award (1993-1997), comparable to an ERC starting grant Work on Awari recognized in 'The Math Book' as a historic mathematics achievement Multiple first prizes in IEEE International Scalable Computing Challenges (2008, 2010) Two EuroSys Roger Needham PhD awards for his students (2010, 2013) He established foundational Dutch research infrastructure through the NWO DAS project, creating distributed testbeds used by ~100 PhD candidates. As adjunct director of the €40M Virtual Lab for e-Science (2004-2009), he catalyzed the Netherlands eScience Center's formation and participated in EU projects (Gridlab, XtreemOS, Contrail). Currently, he chairs the Department of Computer Science Science committee and explores GPU and smartphone-based distributed systems. His research group actively collaborates with NIKHEF, LOFAR, and Utrecht University on domain-specific applications while advancing hybrid distributed computing frameworks.

Sascha HunoldView profile

Associate Professor

• Parallel Computing
• High-Performance Computing (HPC)
• MPI Collective Communication
• +4 more

Sascha Hunold is an Associate Professor at TU Wien, affiliated with the Department of Parallel Computing within the Faculty of Informatics. He holds roles as Vice Dean of Academic Affairs for Informatics Bachelor programs and Curriculum Coordinator for the Master's program in High-Performance Computing. His research focuses on parallel computing, MPI optimization, scheduling algorithms, and reproducible HPC experiments. Education: Habilitation (venia docendi) in Computer Science from TU Wien, PhD in Computer Science from the University of Bayreuth, and MSc in Computer Science from Martin Luther University Halle-Wittenberg. Research interests include MPI collective communication performance, HPC benchmarking, algorithm selection, and task scheduling. He has contributed to tools like ReproMPI and Scheduling.jl. Key awards include Best Paper awards at IEEE CLUSTER 2020 and EuroMPI/Asia 2014, and recognition for reproducible research methodologies. His work emphasizes practical applications of parallel computing in large-scale systems. Teaching responsibilities include courses on parallel algorithms, scientific programming with Python, and HPC project supervision. Active in program committees for major conferences like IPDPS and SC.

Michael MeisterView profile

Professor

• Wastewater Treatment Plant Modeling
• Numerical Flow Simulation
• Smoothed Particle Hydrodynamics (SPH)
• +3 more

Michael Meister is a Professor at MCI – The Entrepreneurial School, holding a position in the Department of Environmental, Process & Energy Technology. He has held roles including Junior Professor and Post-Doc researcher at Universität Innsbruck and teaching positions at Imperial College London. His research focuses on wastewater treatment plant modeling, numerical flow simulation using SPH, and hydraulic simulations. Education: PhD in Environmental Technology (University of Innsbruck), MSc in Physics (Imperial College London), BSc in Physics (University of Innsbruck). Research interests include optimizing anaerobic digester mixing efficiency, CFD applications in wastewater processes, and developing SPH-based models for environmental systems. His work bridges computational fluid dynamics with practical engineering solutions for sustainable energy and water management. Key projects include OPTIFAUL (energy-efficient mixing optimization) and SPHAUL (SPH-based digester modeling). He has received multiple teaching awards from MCI and recognition for his doctoral and undergraduate work. Advised over 15 students on topics ranging from biogas potential estimation to CFD-based energy optimization in wastewater treatment plants. Active in peer review for journals and conferences, and serves on evaluation committees for international research programs. Labs/Teams: Leads the OptiFaul research initiative and collaborates with institutions like the University of Leeds and the Austrian Research Promotion Agency.

Tirado FranciscoView profile

Professor

• Power Aware Computing
• Heterogeneous Computing
• Applications optimization
• +4 more

Francisco Tirado is a Full Professor of Computer Architecture at Complutense University of Madrid since 1986. He has held leadership roles including Vice-Chancellor for Research (2012-2015), Dean of the Faculty of Physics (1994-2002), and Director of the Complutense Supercomputing Center (2002-2007). He is a founding member of the Spanish Society for Computer Science (SCIE) and Spanish Society for Computer Architecture (SARTECO). His research focuses on power-aware computing, heterogeneous systems, bioinformatics, and hardware design automation. He has authored over 242 publications and supervised 15 PhD theses. His professional service includes chairing 138 international conferences and serving on numerous committees for EUROMICRO, IEEE, and national research agencies. Education: No explicit details provided, but has been active at Complutense University since 1978. Affiliations: Member of COSCE Executive Board (2012), IEEE Senior Member (2005), and EUROMICRO Board (1991-2004). Research Interests: - Optimizing energy efficiency in high-performance computing systems - Design of heterogeneous architectures and accelerators - Bioinformatics applications through hardware/software co-design - Parallel programming models for emerging architectures - Automation tools for FPGA and embedded systems development Conference Contributions: Played multiple roles (General Chair, Program Chair) for major events like ParCo, HPCA, and EUROMICRO PDP conferences. Delivered 64 keynote/plenary lectures globally. Scientific Awards: Doctor 'Honoris Causa' from universities in Spain, Paraguay, and Peru 2013 National Computer Science Award IEEE Senior Membership Advising: Supervised 15 PhD students. Active in technology transfer contracts with industry. Labs/Teams: Founder and leader of the ArTeCS research group at Complutense University, specializing in computer architecture and high-performance computing innovations.

• High-Performance Computing
• Big Data Processing
• Performance Portability
• +3 more

Enes Bajrovic is a researcher affiliated with the Faculty of Computer Science, focusing on high-performance computing (HPC), big data processing, and performance portability. His work spans task-based parallelism, runtime systems, and optimization frameworks for heterogeneous architectures. He has contributed to major European projects like PEPPHER and AutoTune, which aim to advance HPC software tools and autotuning methodologies. His research emphasizes practical applications of parallel computing in domains such as mobile networks and scientific simulations. Education: Dipl.-Ing. Dr.techn., BSc in Computer Science His research interests include developing frameworks for compute- and data-intensive applications, leveraging technologies like Kubernetes, OpenCL, and Intel Xeon Phi coprocessors. He has authored numerous peer-reviewed publications on topics such as pipeline patterns, autotuning algorithms, and hybrid execution models. His work bridges theoretical advancements in parallel computing with real-world software engineering challenges. Bajrovic has collaborated on projects funded by the European Commission’s FP7 program, contributing to deliverables like runtime systems, tuning frameworks, and benchmarking tools. His research also addresses the integration of big data processing with HPC, particularly in telecommunications and distributed computing environments.

Klaus MuellerView profile

Professor

• Visual Analytics
• Explainable Machine Learning and AI
• Algorithmic Fairness and Transparency
• +7 more

Klaus Mueller is a Professor in the Computer Science Department at Stony Brook University , with additional appointments in Biomedical Engineering and Radiology. He serves as Director of the Visual Analytics and Imaging (VAI) Lab, Liaison for the SUNY Korea CS Program, and Interim Chair of the Department of Technology and Society . His career spans roles at Brookhaven National Lab and leadership positions at SUNY Korea. Dr. Mueller earned his PhD in Computer and Information Science (1998), MS in Computer and Information Science (1996), and MS in Biomedical Engineering (1990) from The Ohio State University , alongside a BS in Electrical Engineering (1987) from the Polytechnic University of Ulm, Germany. His research focuses on visual analytics , explainable AI , algorithmic fairness , computational imaging , and medical imaging . He has pioneered GPU-accelerated CT reconstruction techniques, bias mitigation frameworks (e.g., D-BIAS), and tools like DOMINO for causal reasoning. His work bridges data science , human-computer interaction , and medical applications , often integrating large language models for visualization tasks. Recent publications highlight advances in multivariate volume rendering , LLM-driven bias detection , and mDDPM-based medical image synthesis . His articles span IEEE Transactions , Nature Machine Intelligence , and conferences like IEEE VIS and ACM CHI . Award highlights include NSF CAREER (2000), SUNY Chancellor Award (2011), IEEE Golden Core Award (2016, 2022), induction into the National Academy of Inventors (2018), and elevation to IEEE Fellow (2024). He has chaired major conferences and served as Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics (2019-2022). He teaches graduate and undergraduate courses in visualization , medical imaging , and GPGPU programming , and leads the Visual Analytics Seminar (CSE 648). His lab ( VAI Lab ) fosters interdisciplinary research in GPU-accelerated analytics and ethical AI.

• High-Performance Computing
• Parallel Programming
• GPU Computing
• +3 more

assoz. Prof. Dipl.-Ing. Dr. Peter Thoman is an Associate Professor in the Department of Computer Science at the University of Innsbruck. His work focuses on high-performance computing, parallel programming models, and GPU-accelerated systems. He leads research in areas such as SYCL standard implementations, distributed computing optimizations, and energy-efficient algorithms for exascale applications. His research interests include task-based parallelism, heterogeneous computing architectures, and scalable runtime systems. Notable contributions include the Celerity API for accelerator clusters and the LIGATE drug discovery platform. He is affiliated with the University of Innsbruck’s technical research groups and collaborates on projects like the AllScale framework. Dr. Thoman’s teaching includes courses on parallel programming and high-performance computing systems. He is a member of the Department’s leadership team under Prof. Thomas Fahringer.

Thomas FahringerView profile

Professor

• Parallel Computing
• Distributed Systems
• High-Performance Computing (HPC)
• +4 more

Thomas Fahringer is a full Professor and Head of the Distributed and Parallel Systems Group at the University of Innsbruck's Institute of Computer Science. His research focuses on parallel computing, distributed systems, and high-performance computing (HPC), with particular emphasis on GPU acceleration, cloud/edge computing, and serverless architectures. He leads interdisciplinary projects involving scientific workflows, resource management systems, and exascale computing solutions. Key research areas include: Design of high-level APIs for accelerator clusters (e.g., Celerity-RSim) Optimization of IoT and LoRa networks using machine learning Scalable key-value stores for geo-distributed systems Workflow scheduling in edge-cloud continuum environments Recent work emphasizes automation of deployments, energy-efficient transmission policies, and fault-tolerant orchestration of serverless functions. He actively participates in community initiatives like the Workflows Community Summit, driving advancements in scientific workflows and HPC ecosystems. His lab develops frameworks such as Apollo and AllScale, targeting exascale computing and distributed runtime systems.

Majid SalimibeniView profile

Research Fellow

• High Performance Computing
• Parallel Computing
• Message Passing in Large-scale HPC systems
• +6 more

Majid Salimibeni is a PostDoc Researcher and Research Fellow at Vienna University of Technology (TU Wien) , affiliated with the Parallel Computing research unit. He previously held a Postdoc position at the Department of Computer Science at the University of Salerno, Italy (2024), and has been a visiting researcher at TU Wien (2023). Currently based in Vienna, Austria, his work focuses on High Performance Computing (HPC) and its interdisciplinary applications. Education: Ph.D. in Computer Science, University of Salerno, Italy (2020–2024) M.Sc. in Computer Engineering (Software), Shiraz University, Iran (2017–2020) B.Sc. in Computer Engineering (Software), University of Birjand, Iran (2012–2016) Salimibeni's research explores High Performance and Parallel Computing , with a focus on optimizing GPU communication via NCCL , improving energy efficiency in heterogeneous computing environments , and enhancing MPI collective algorithms for large-scale systems. His work bridges HPC infrastructure with emerging applications in Distributed Deep Learning and AI . Recent publications highlight his expertise in GPU frequency scaling , NCCL profiling , and process arrival pattern analysis to improve distributed system performance. These works appear at top conferences like IPDPS , Cluster Computing , and ASHPC . Scientific Awards: Best Paper Award, Bench 2022 Salimibeni contributes to academic committees as a program committee member for IEEE ICPP 2025, ISC 2025, and BigHPC 2024, and serves on artifact evaluation committees for ASPLOS and CF conferences.

Bernhard KerblView profile

Researcher

• Computer Graphics
• Real-Time Rendering
• GPU Programming
• +4 more

Bernhard Kerbl is a Post-doctoral Researcher at the Computer Graphics Group (E193-02) within the Faculty of Informatics at Vienna University of Technology (TU Wien). He actively contributes to research in real-time rendering, GPU optimization, and 3D visualization. His work focuses on point cloud rendering, Gaussian splatting, and low-level graphics API development (particularly Vulkan). Core Research Areas: Real-time rendering, GPU programming, point cloud processing, 3D Gaussian splatting, task-based parallelism Projects: IVILPC (2023–2026), ACD (2020–2028), EVOCATION (2018–2022) Key Contributions: Developed real-time rendering techniques for massive datasets, pioneered GPU-based scene streaming architectures, and created novel methods for visual error prediction using machine learning. His work on Vulkan transition in academia received recognition, and he maintains active collaborations in GPU education. Scientific Achievements: Best Paper Award at EGPGV 2024 for "Fast Rendering of Parametric Objects on Modern GPUs" Recipient of Vienna Science and Technology Fund (WWTF) support for graphics research Technical Expertise: Specializes in CUDA, OpenGL/Vulkan APIs, and real-time systems. His work bridges academic research with practical implementations in virtual reality, augmented reality, and GPU education.