Ioannis Vardas is a Researcher in Parallel Computing at TU Wien's Faculty of Informatics. His work focuses on optimizing high-performance computing systems through advanced process mapping, MPI programming, and performance analysis tools. Key research areas: Topology-aware process mapping for HPC MPI library development and optimization Performance profiling of parallel applications Resource allocation in hierarchical architectures Energy-efficient parallel computing He contributes to projects improving MPI performance tools and develops methods for efficient resource utilization in supercomputing environments.
Sabrina Herbst is a PreDoc Researcher at the Department of Computational Sustainability within the Faculty of Informatics at Technische Universität Wien . She works on Quantum Computing , focusing on Machine Learning integration and High-Performance Computing (HPC) augmentation for quantum algorithms. Education : Dipl.-Ing. (2023) in Informatics from TU Wien, with a thesis on quantum machine learning. Her research explores scalable Quantum Machine Learning (QML) algorithms, emphasizing theoretical foundations and robust numerical implementations. Key areas include Quantum Neural Networks (QNNs) , hyperparameter optimization, and the intersection of quantum computing with HPC and edge computing. Recent work investigates channel distinguishability in QNNs and hybrid quantum-classical systems for IoT data processing. Projects she contributes to include HPQC (High Performance integrated Quantum Computing), Themis FWF (Trustworthy and Sustainable Code Offloading), and TRITON FWF (Transprecise Edge Computing). Awards include the Andreas Dieberger–Peter Skalicky Scholarship (2025), Siemens Award for Excellence (2023), and a conference scholarship from TU Wien (2024). Scientific Awards Andreas Dieberger–Peter Skalicky Scholarship (2025) Siemens Awards for Excellence (2023) Conference Scholarship for female PhD students (2024)
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.
Prof Peter V. Coveney holds a Chair in Physical Chemistry at University College London (UCL) , where he serves as Director of the Centre for Computational Science (CCS) . He is also an Honorary Professor of Computer Science at UCL and Professor Adjunct at Yale University School of Medicine. Interdisciplinary research spanning condensed matter physics/chemistry , materials science , and life/medical sciences Creator of LB3D and HemeLB lattice-Boltzmann codes Leader of major HPC projects: VECMA , CompBioMed , CompBioMed2 , and VPH NOE His research focuses on high-performance computing for multiscale simulations in biomedicine and materials science. Software developments include tools for deploying complex workflows on distributed HPC infrastructures and high-performance clouds. Recent publications highlight quantum simulation, HIV-1 protease inhibitors, and graphene interactions. Scientific Awards : Innovative Applications of Artificial Intelligence Award (1996) Fellow of the Royal Society of Chemistry (1996) Fellow of the Institute of Physics (1997) HPC Challenge Awards (2003-2018) NSF TeraGrid08 Transformational Science Award (2008)
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.
Ivona Brandić is a University Professor for High Performance Computing Systems at TU Wien's Institute of Software Engineering and Interactive Systems. Born in Gradačac, Bosnia and Herzegovina, she moved to Austria in 1992 as a refugee during the Bosnian War. She earned a master's degree (2002) and doctorate (2007) in business computer science from TU Wien and completed her habilitation in applied computer science there in 2013. Her career includes roles as an assistant professor (University of Vienna, 2002–2007) and postdoctoral researcher (University of Melbourne, 2008). She transitioned to a tenure-track position at TU Wien in 2014 and became a full professor in 2016. Brandić’s research focuses on cloud computing, energy-efficient ultra-scale systems, and hybrid quantum-classical computing. She has been recognized with the MiA Award (2011), the Austrian Science Fund's Start-Preis (2015), and membership in the Austrian Academy of Sciences' Young Academy (2016). Her work emphasizes sustainable computing, edge systems, and optimizing resource management for distributed applications. Education: Bachelor's degree in Business Informatics (University of Vienna/TU Wien) Master's in Business Computer Science (University of Vienna, 2002) PhD in Applied Computer Science (TU Wien, 2007) Habilitation in Practical Computer Science (TU Wien, 2013) Research Interests: Brandić’s work spans cloud computing, energy efficiency in HPC systems, edge computing, and quantum-classical hybrid systems. She explores autonomic resource management, distributed system resilience, and sustainability in ultra-scale infrastructures. Her projects often address real-world applications like drug design, environmental monitoring, and smart energy grids. Publications: Her 2009 paper Cloud Computing and Emerging IT Platforms is a seminal work in the field. Recent publications focus on quantum-edge integration, energy optimization in AI models, and adaptive edge analytics frameworks. These contributions highlight trends toward sustainable, distributed, and hybrid computational paradigms. Awards: 2011: MiA Award for distinguished contributions by international backgrounds 2015: Austrian Science Fund’s Start Prize 2016: Austrian Academy of Sciences Young Academy Membership Advising & Grants: Brandić leads research groups and has secured grants for projects like NESSUS (energy-efficient cloud systems) and CHIST-ERA’s SDCDN (distributed networks). She mentors students in HPC, edge computing, and quantum systems. Advised topics include workload scheduling, fault tolerance, and energy-aware algorithms. Labs & Teams: She directs research on autonomic cloud management, edge intelligence frameworks (e.g., Sea-LEAP, FRESCO), and quantum-classical workflow systems (RIGOLETTO). Her teams collaborate internationally, integrating academia and industry for scalable, sustainable solutions.
Gerald Lirk is a Professor at Fachhochschule Campus Hagenberg, specializing in interdisciplinary research spanning High Performance Computing, Bioinformatics, and Medical Diagnostics. His work integrates computational methods with healthcare applications, including studies on epigenetic mutations, driver distraction systems, and brain connectivity analysis. Research interests focus on optimizing computational frameworks for biomedical applications, such as scheduling algorithms for HPC environments and analyzing genetic markers in hematologic disorders. He has led projects like Lingohub (2013–2014) and WIMAKS (2012–2013), funded by EFRE Regio 13. Recent contributions include peer-reviewed studies on mutational burden impacts and feasibility of adolescent health programs. He actively engages in public discourse on topics like vaccine mechanisms and NGS data analysis, delivering over 20 talks and presentations since 2013.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology Kanpur (IITK) since 2022. He previously held positions at Los Alamos National Laboratory as a Postdoctoral Researcher (2018-2019) and Scientist II (2019-2022). Dr. Dutta earned his Ph.D. and M.S. in Computer Science from The Ohio State University (2011-2018) and a B.Tech in Electronics and Communication Engineering from the West Bengal University of Technology (2005-2009). His research lies at the intersection of Machine Learning , Visual Computing , Big Data Analytics , and High-Performance Computing (HPC) . He focuses on developing scalable solutions for extreme-scale data, such as exascale simulations, social media, IoT, and healthcare. His work emphasizes uncertainty quantification in AI models and interactive visualization techniques. Dr. Dutta’s recent publications highlight his expertise in in situ visualization for climate modeling, implicit neural representations for uncertainty-aware rendering, and statistical sampling for exascale systems. His funded projects include AI-driven data analytics frameworks and deepfake defense mechanisms supported by ISRO, SERB, and C3iHub. Scientific Awards include Best Reviewer (TVCG), Best Paper (ISAV, TopoInVis), and LAAP Award (LANL).
Siegfried Benkner is a full Professor at the Vienna University of Technology (TU Wien) within the Faculty of Computer Science and leads the Research Group for Scientific Computing. His work focuses on high-performance computing (HPC), parallel programming models, runtime systems, and performance optimization for heterogeneous architectures. He has actively contributed to EU-funded projects such as TROCI (2024–2027) and PEPPHER, addressing resilience in critical infrastructures and programmability for exascale systems. His research spans topics like task-based runtime systems (OCR-Vx), autotuning frameworks (Periscope PTF), and performance portability for GPUs/Xeon Phi architectures. Recent interests include accelerating graph neural networks via novel matrix compression formats and cloud-edge continuum systems for eHealth applications. Prof. Benkner has published over 270 articles, with a focus on runtime systems, parallel patterns, and HPC infrastructure. His work emphasizes practical applications, including semantic data management for medical research and cloud-based analytics frameworks for big data processing in cellular networks. He has led multiple EU projects (9 total), including the 2024 initiative on exascale computing and resilience, and frequently presents at conferences like Euro-Par and Supercomputing events. His activities include media engagement on topics like exascale hardware trends and HPC challenges.
Ivona Brandic is a Full Professor of High Performance Computing Systems at TU Wien's Institute of Information Systems Engineering, leading the HPC Research Group. She specializes in Computational Sustainability, Cloud/Edge Computing, and Quantum-Classical Systems. Her roles include Head of the Computational Sustainability Research Unit and membership in TU Wien's Faculty Council. She teaches courses such as AI/ML in Climate Change and Hybrid Quantum-Classical Systems. Her research focuses on sustainable IT, energy-efficient systems, and hybrid quantum-classical workflows. Projects include computational sustainability initiatives funded by the Austrian Science Fund (FWF) and industry partnerships like the Virtual Shepherd project. She has contributed to over 50 publications, emphasizing edge computing, quantum algorithms, and HPC optimization. Notable contributions include developing frameworks like RIGOLETTO for hybrid scientific workflows and FRESCO for edge offloading. Her work bridges theoretical advancements with practical applications in environmental monitoring and energy efficiency.
Peter Kulczycki is a researcher at the Research Center Hagenberg Bioinformatics, University of Applied Sciences Hagenberg. He serves as a Researcher specializing in bioinformatics and high-performance computing with expertise in developing computational solutions for biological research challenges. His research interests span multiple domains: Bioinformatics and computational biology High Performance Computing and parallel processing Intelligent scheduling algorithms Resource management systems DNA analysis and primer design Web-based bioinformatics education Dr. Kulczycki's work focuses on bridging computational science and biological research through efficient algorithms and systems for processing complex biological data. He has made significant contributions to scheduling systems that optimize heterogeneous computing resources for bioinformatics applications, with research progression from foundational bioinformatics resources to sophisticated scheduling systems that predict job execution times. His publication record shows consistent focus on computational efficiency in bioinformatics through innovative scheduling approaches, with notable work on heterogeneous multiplatform hardware, DNA primer calculation using GPGPUs, and web-based bioinformatics education frameworks. Dr. Kulczycki served as Principal Investigator for the Bioinformatics Resource Facility Hagenberg project (2009-2012) under the COIN Cooperation & Innovation program. He has been active in academic discourse, delivering invited talks on Evolution and Genetics and the Birth of Life in 2007. His research collaborations include G. Lirk, H. Brandstätter-Müller, B. Parsapour, and A. Hölzlwimmer on various bioinformatics projects.
