Radu-Aurel Prodan is a full Professor in the Department of Computer Science at the University of Innsbruck, Austria. He is actively involved in teaching and research, with office hours from Monday to Friday, 09:00–13:00. His work is centered within the academic and technical framework of one of Austria's leading computer science departments. His research interests span key areas in modern computing systems, including distributed computing, parallel computing, workflow scheduling, high-performance computing, cloud computing, and grid computing. These domains reflect his contributions to scalable and efficient computing infrastructures. The publications in his body of work demonstrate consistent engagement with challenges in system efficiency, resource management, and scientific workflows across distributed environments. Broad themes include optimization, performance modeling, and large-scale data processing in parallel systems. Scientific awards: No specific awards mentioned in the provided text. There is no available information on student advising, research grants, or leadership in specific labs or teams within the provided content. His professional activities appear to be anchored in the Department of Computer Science at the University of Innsbruck.
Wilfried Gansterer is a Professor at the Faculty of Computer Science, University of Vienna, leading the Theory and Applications of Algorithms research group. His work focuses on numerical algorithms, distributed computing, and machine learning, with notable contributions to graph neural networks and fault-tolerant systems. Active in projects such as Algorithmic Data Science for Computational Drug Discovery (2020–2028) and REPEAL (Resilience vs. Performance in Numerical Linear Algebra, 2016–2020). Research Interests: Dr. Gansterer’s expertise spans graph neural networks, matrix compression, adversarial defense mechanisms, and high-performance computing. His work addresses challenges in efficient computation, resilience against node failures, and optimizing distributed systems. Projects : Algorithmic Data Science for Computational Drug Discovery (2020–2028) REPEAL: Resilience vs. Performance in Numerical Linear Algebra (2016–2020) Verteiltes Rechnen (Distributed Computing, 2007–2014) Awards : 2023 Best Paper Award for work on Crossfire: An Elastic Defense Framework for Graph Neural Networks. Labs/Teams : Directs the Theory and Applications of Algorithms group, focusing on algorithmic innovation in distributed and high-performance computing environments.
Christoph Dellago is a full Professor of Computational Physics at the Faculty of Physics of the University of Vienna, where he has been a faculty member since 2003. He currently serves as Director of the Erwin Schrödinger Institute for Mathematics and Physics, Head of the Computational and Soft Matter Physics Group, and Project lead of EuroCC Austria - National Competence Centre for Supercomputing. Previously, he served as Dean of the Faculty of Physics (2009-2012) and Coordinator of the Doctoral College Advanced Functional Materials (DCAFM). Full Professor, Faculty of Physics, University of Vienna (2003-present) Director, Erwin Schrödinger Institute for Mathematics and Physics (2017-present) Head, Computational Physics and Soft Matter Group (2024-present) Coordinator, Doctoral College Advanced Functional Materials (DCAFM) Austrian Representative, Council of CECAM Dellago received his PhD in Physics from the University of Vienna in 1996, followed by postdoctoral research at UC Berkeley as a Schrödinger Fellow of the Austrian Science Foundation. His research focuses on developing computational methods to study rare events in condensed matter systems, particularly transition path sampling methodology for simulating nucleation, chemical reactions, and biomolecular reorganizations. He has pioneered the application of machine learning to molecular structure recognition and potential energy surfaces. Recent work examines self-assembly of nanocrystals, biopolymer folding, aqueous interfaces, phase separation in alloys, thermo-polarization, cavitation, and freezing phenomena. Analysis of Dellago's recent publications (2023-2025) reveals a strong emphasis on machine learning applications in computational physics, particularly neural network potentials for simulating water interfaces, crystal defects, and phase transitions. His work bridges traditional statistical mechanics with modern computational techniques, creating powerful tools for studying complex dynamical processes that occur on timescales far beyond conventional molecular dynamics simulations. The publications demonstrate increasing integration of machine learning with rare event sampling methods, reflecting the cutting-edge direction of computational statistical mechanics. Förderpreis der Stiftung Futura zur Förderung junger Südtiroler im Ausland (1997) The Raymond and Beverly Sackler Prize in the Physical Sciences (2005) UNIVIE Teaching Award of the University of Vienna (2014) Dellago leads an active research group with multiple PhD students and postdocs, focusing on computational statistical mechanics. His group develops trajectory-based sampling methods and machine learning approaches for molecular simulation. He has secured significant funding through EuroCC Austria and various research platforms including the Research Platform Accelerating Photoreaction Discovery and the Research Platform Erwin Schrödinger International Institute for Mathematics and Physics. His research has been supported by numerous grants enabling advanced computational infrastructure for high-performance simulations. The Dellago Group operates within the Computational and Soft Matter Physics division at the University of Vienna, with strong connections to the Research Network Data Science. The group collaborates extensively with international research institutions and maintains close ties with the Erwin Schrödinger Institute, which Dellago directs. Their research environment combines theoretical physics, computational chemistry, and machine learning expertise to tackle fundamental questions in condensed matter physics and soft matter systems.
Antonio Plaza is a Full Professor at the University of Extremadura, Spain, and Head of the Hyperspectral Computing Laboratory. With over 600 publications, he is a leading expert in hyperspectral data processing and parallel computing of remote sensing data. He serves as IEEE Fellow and has received numerous accolades, including the 2019 Excellent Teaching Award and multiple Highly Cited Researcher recognitions. Research Interests : His work bridges Hyperspectral Image Analysis , Medical Imaging , and High-Performance Computing . Recent projects focus on 3D anatomical modeling, AI-driven surgical tools, and deep learning applications for aortic dissection segmentation. Scientific Awards : 2019 Highly Cited Researcher (Geosciences) 2015 IEEE Fellow 2019 Excellent Teaching Award 2018 Highly Cited Researcher (Cross-Field) 2002 Best PhD Dissertation, University of Extremadura Editorial Leadership : Served as Editor-in-Chief of IEEE Transactions on Geoscience and Remote Sensing (2013–2017) and held multiple committee roles in IEEE GRSS. His articles reflect a shift from remote sensing to medical imaging, with a focus on Aortic Dissection Segmentation , Skull Reconstruction , and AI-driven Medical Tools .
Willy Zwaenepoel is a Professor and Dean of the Faculty of Engineering at the University of Sydney. He holds a B.S. from the University of Gent and M.S./Ph.D. from Stanford University. Previously, he served as Dean of the School of Computer and Communication Sciences at EPFL and was a faculty member at Rice University. His expertise spans operating systems, distributed systems, and high-performance computing. Education: B.S., University of Ghent, Belgium (1979) M.S., Stanford University (1980) Ph.D., Stanford University (1984) Research Interests: Dr. Zwaenepoel focuses on distributed systems, operating systems, and their applications in database replication, virtual machine performance, and software update mechanisms. His work includes foundational contributions to distributed shared memory (e.g., Treadmarks) and startups like iMimic Networking. Awards: ACM Fellow (2000) IEEE Fellow (1998) Fellow of the Australian Academy of Technical Sciences and Engineering (2020) Recipient of the IEEE Tsutomu Kanai Award (2007) Key Contributions: His research addresses challenges in distributed systems performance, such as latency reduction in key-value stores and efficient graph processing. Current projects explore I/O optimization in virtualized environments and causal consistency for geo-replicated systems. Students/Advising: Advises Ph.D. students and postdocs, including William in database replication. His mentorship led to the Rice University Teaching Award (2000).
Dr. Igor Stankovic is a Research Professor at the Institute of Physics Belgrade, University of Belgrade. He holds a Dr.rer.nat. in Theoretical Physics from Technical University Berlin (2004) and a degree in Electrical Engineering from University of Belgrade (1999). His professional journey includes roles as a Senior Simulation Engineer at Toyota Motor Europe and visiting professorships at Universidad Técnica Federico Santa María (Chile) and University of Leoben (Austria). He has led multiple Horizon 2020 and Horizon Europe projects, including the Principal Investigator role in ULTIMATE-I and BLESSED projects. Education: 1999: Electrical Engineering (Dipl. ing.), University of Belgrade 2004: Dr.rer.nat. in Theoretical Physics, TU Berlin Research Interests: Focus on High-Performance Computing applications, computational tribology, and modeling of two-dimensional materials. His methods include molecular dynamics simulations, Monte Carlo techniques, and optimization algorithms. Key areas: ionic liquids, friction mechanisms, and self-assembled magnetic nanostructures. Key Projects: Horizon 2020: DAFNEOX (2015-2019) Horizon 2020: ULTIMATE-I (2020-2025) Horizon Europe: BLESSED (2023-2027) Scientific Awards: 1993 Prize of City of Belgrade 1999 Best Student Award Advising & Grants: Mentor for three Ph.D. students and actively advises on technology transfer through the Enterprise Europe Network. Labs & Teams: Leads Scientific Computing Laboratory, collaborates with SyNergy_Mat Lab and 2D_Mat_Lab teams.
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.
Dragi Kimovski is a Habilitated Assistant Professor in Distributed Systems at Klagenfurt University, Austria, focusing on Edge Computing and AI. He previously held roles at the University of Innsbruck and the University of Information Science and Technology in Macedonia. His research spans Edge/Fog/Cloud computing, multi-objective optimization, and high-performance computing. He has coordinated major projects like 6GContinuum and KärtnerFog, and led initiatives such as DataCloud and ASPIDE. His teaching includes courses on Distributed Computing, Cloud Computing, and IoT. He is the co-creator of the Carinthian Computing Continuum and maintains a blog on Edge AI World. His work emphasizes sustainable and efficient computing solutions for emerging technologies. Education: Not explicitly listed in the provided text. Research Interests: Edge Computing, Fog Computing, Cloud Computing, Multi-objective Optimization, High-Performance Computing, AI in Distributed Systems. His work addresses challenges in resource management, latency reduction, and scalability across heterogeneous environments, with applications in healthcare, IoT, and 6G networks. Projects: 6GContinuum (Coordinator): Focuses on AI services over 6G networks. KärtnerFog (Scientific Coordinator): Develops adaptive Fog infrastructures over 5G. DataCloud (WP5 Leader): Manages Big Data pipelines on the Computing Continuum. ASPIDE (Scientific Coordinator): Advances exascale programming models for data processing. Teaching: Klagenfurt University: Courses include Distributed Computing, IoT, Cloud Computing, and Advanced Programming. University of Innsbruck: Taught Advanced Parallel and Distributed Systems. University of Information Science and Technology: Courses in High-Performance Computing and Network Architectures. Labs/Teams: Co-created the Carinthian Computing Continuum, an automated SDN testbed for Edge computing research. Active in interdisciplinary teams addressing extreme data processing and sustainable computing.
Diana Marin is a PostDoc Researcher at TU Wien's Institute of Visual Computing & Human-Centered Technology. She holds a BSc, MEng, and Dr.techn. (PhD) in technical fields. Her research focuses on computational geometry, point cloud processing, and distributed computing for large-scale datasets. She has contributed to projects like Distributed Surface Reconstruction and RE:STOCK INDUSTRY. Education: BSc, MEng, Dr.techn. (PhD) Her work emphasizes curve and surface reconstruction from unorganized point clouds, leveraging proximity graphs and distributed computational methods. Key projects include optimizing surface reconstruction for massive datasets and developing parameter-free algorithms for connectivity analysis. Her publications span topics like SING neighborhood graphs, Riemannian manifold curve reconstruction, and distributed processing techniques. She collaborates on projects such as PostDisaster and Mixed Reality Lab.
Dr. Vincenzo De Maio is a PostDoc Researcher at the Department of Computational Sustainability, Faculty of Informatics, Technische Universität Wien. His research spans quantum computing, edge computing, distributed systems, and sustainable computing, with a focus on hybrid quantum-classical systems and edge AI applications. Current projects: HPQC (2023–2025), ThEMIS FWF (2024–2027), TRITON FWF (2023–2027) Previous projects: RUCON (2016–2023), SWAIN (2021–2024) His research explores the integration of quantum computing with classical systems, particularly in software engineering, workflow decomposition, and hyperparameter optimization. He also works on edge computing for smart cities, environmental monitoring, and traffic safety applications. Recent publications analyze quantum compilation bottlenecks, hybrid quantum-classical workflow orchestration, and quantum neural network optimization. He supervises master's theses on topics like quantum edge benchmarking and hyperparameter tuning. Key collaborations with Prof. Ivona Brandic and others Active in IEEE/ACM conferences and Springer publications
Arturo Azcorra Saloña is a Full Professor at Universidad Carlos III de Madrid and the founder & Director of IMDEA Networks Institute. He has held leadership roles in technology transfer, including Director General positions at CDTI and the Spanish Ministry of Science and Innovation. Education: M.Sc. in Telecommunications Engineering (1986) and PhD in Telecommunications (1989) from UPM, MBA with honors from Instituto de Empresa (1993) Research interests focus on Multi-access Edge Computing , Network Function Virtualization , and 5G networks , with significant contributions to Software Defined Networks and Smart connectivity systems . His publications span wireless networking, vehicular networks, and formal methods, with high-impact papers on 802.11 protocols, 5G architecture, and vehicular mobility optimization. Scientific awards: Elected Chairman of Networld2020 ETP, Special University award for extraordinary merits (2002), National PhD prize (1990), Best graduating student prize (1986) Research management includes coordinating EU projects like 5G-TRANSFORMER, 5G-CROSSHAUL, and networks of excellence such as CONTENT and E-NEXT. He co-founded the ACM CoNEXT conference and contributes to European 5G PPP initiatives.
Prof. Wen Dongsheng is the Chair Professor and Head of the Institute of Thermodynamics at Technical University of Munich (Germany). He holds adjunct professorships at the University of Leeds (UK) and Beihang University (China). His research focuses on multiscale heat transfer mechanisms in nanomaterials, with applications in energy and aerospace engineering. Key areas include solar energy systems, nanofuel combustion, and nanoparticle interactions with electromagnetic radiation. Education: BEng in Aeronautics (Beihang University), MSc in Thermophysics (Tsinghua University), DPhil in Engineering Science (University of Oxford). Research Interests: Heat production/transport/storage, multiscale experimentation/simulation, nanomaterial-based energy solutions. Specific applications include solar energy optimization, nanomedicine, and oil/gas engineering innovations via nanoparticle-driven technologies. Editorial Roles: Editor-in-Chief of Advance in Aerodynamics , Associate Editor of Applied Thermal Engineering . Awards include Fellowships from the Royal Society of Chemistry and Energy Institute, and the MIC-Particuology Award. Publications: Over 350 referred papers, 20 patents, with an H-index of 65. Research emphasizes cross-disciplinary applications of nanotechnology in energy systems and aerospace.
Beng Chin Ooi is a Lee Kong Chian Centennial Professor at the National University of Singapore (NUS), School of Computing. He has been with NUS since 1991, progressing through the ranks from Lecturer to his current distinguished position. He previously served as Dean of the School of Computing from 2007 to 2013 and as Director of the Smart Systems Institute from 2011 to 2021. His educational background includes: 1985: B.Sc. (1st Class Honors) from Monash University, Melbourne, Australia 1989: Ph.D. in Computer Science from Monash University, Melbourne, Australia Beng Chin Ooi's research focuses on database systems, large scale analytics, and distributed systems. His work has been instrumental in advancing the field of data management technology, particularly in the context of "big data" in large-scale parallel and distributed systems. He has made significant contributions to spatio-temporal and distributed data management, as well as pioneering research in distributed database management and peer-to-peer based enterprise quality management. His recent publications demonstrate a strong focus on blockchain technology, machine learning systems, and healthcare informatics. There's a clear progression from foundational database research to applications in emerging technologies like blockchain and AI. His work bridges theoretical advances with practical system implementations, as evidenced by multiple open-source projects associated with his publications. His notable awards include: 2021: NUS Research Recognition Award 2020: ACM SIGMOD E.F. Codd Innovations Award 2020: ACM SIGMOD Research Highlight Award 2019: VLDB Best Paper Award 2016: Fellow of Singapore National Academy of Science 2016: China Computer Federation Overseas Outstanding Contributions Award 2014: VLDB Best Paper Award 2014: IEEE TCDE CSEE Impact Award 2013: Singapore National Day's Public Administration Medal (Silver) 2013: NUS Outstanding Researcher Award 2012: IEEE Computer Society Kanai Award 2011: ACM Fellow 2011: Singapore President's Science Award 2009: IEEE Fellow 2009: ACM SIGMOD Contributions Award Throughout his career, Professor Ooi has demonstrated exceptional leadership in the database community, promoting high standards of database research at both international and regional levels. His BLOCKBENCH framework became the world's first benchmarking tool for private blockchains, and his work on data provenance on blockchain systems earned both the VLDB Best Paper Award and the ACM Research Highlight Award. He has led several major research initiatives, including the Smart Systems Institute at NUS. Professor Ooi has established multiple open-source projects including FabricSharp for blockchain data provenance and Cool for cohort online analytical processing. His research group has consistently produced high-impact work that bridges theoretical advances with practical system implementations.
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.
Jürgen Eckert is a Professor and Director at the Erich Schmid Institute of Materials Science of the OAW and holds the Chair of Materials Physics at the University of Leoben . He is a Corresponding Member of the Division of Mathematics and Natural Sciences in Austria since 2017. His research spans Materials Science , Condensed Matter Physics , and Metallurgy , focusing on metallic glasses , high-entropy alloys , microstructure engineering , and additive manufacturing . He has pioneered work on crystallization kinetics , phase transformations , and mechanical property optimization . Recent publications (2024-2025) highlight advances in 3D-printed titanium alloys , nitrogen-stabilized nanocrystalline alloys , and multi-stage heterostructures , reflecting his interdisciplinary approach combining experimental physics , computational modeling , and materials engineering . THERMEC 2023 Distinguished Award Gottfried Wilhelm Leibniz-Preis (2009) ERC-Advanced Grant (2013) DGM-Preis der Deutschen Gesellschaft für Materialkunde (2014) Dr. honoris causa, Slovak Technical University (2018) He leads the ERC Project INTELHYB and collaborates with institutions in India , Germany , and Europe , advancing heterogeneous materials design and sustainable metallurgical solutions .
