جست‌وجوی استاد و پژوهشگر

• Computational Sustainability
• Cloud Computing
• Edge Computing
• +۳ مورد دیگر

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.

• Computational Chemistry
• Lattice Boltzmann Methods
• Scientific Computing
• +۳ مورد دیگر

Dylan Baumann is a Researcher at the Department of Embedded Computing Systems, Technische Universität Wien. He holds a BSc and Diplom-Ingenieur (Dipl.-Ing.) degree. His research focuses on computational methods for simulating chemical reactions using lattice Boltzmann approaches. He is actively involved in teaching and supervising courses such as Digital Design, Computer Engineering Project, and Project in Computer Science. His work bridges computational science and engineering, emphasizing interdisciplinary applications in numerical simulation and embedded systems. Education: Bachelor of Science (BSc) Diplom-Ingenieur (Dipl.-Ing.) His research interests include advancing lattice Boltzmann methods for complex chemical reaction systems, with applications in computational fluid dynamics and multiphase systems. He contributes to the academic community through teaching and research in embedded computing and numerical modeling.

Adam Celarekمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Physically Based Rendering
• Deep Learning for 3D Data
• +۴ مورد دیگر

Adam Celarek is a PreDoc Researcher and University Assistant at the Computer Graphics department (E193-02) within the Faculty of Informatics at Vienna University of Technology. He holds the position of Scientist in the Rendering and Modeling workgroup and serves as a PhD student. Celarek teaches courses including Computer Graphics, Fundamentals of Computer Graphics, Seminar in Visual Computing, and Rendering. His research interests span Physically Based Rendering, Deep Learning for 3D data, Point Clouds, Gaussian Mixture Models, and Terrain Rendering. Celarek's work focuses on the intersection of computer graphics and machine learning, particularly in processing unstructured 3D data. He is currently working on deep learning applications for Gaussian mixture models and developing AlpineMaps, a navigation aid for alpinists in collaboration with Manuela Waldner. His publication record shows a consistent focus on rendering techniques and 3D data processing, with recent work emphasizing the application of deep learning to Gaussian mixture models and terrain visualization. The research demonstrates progression from foundational work on light transport algorithms to more recent applications in geospatial visualization and deep learning for 3D data. OCG-FÖRDERPREIS 2018 (with Christoph Weinzierl-Heigl) Celarek has supervised multiple student projects including Johannes Eschner's work on real-time avalanche risk visualization and Stefan Fraiss's research on Gaussian mixture models from point clouds. He is involved in the EVOCATION training network as a former Marie Skłodowska-Curie fellow and currently leads the High-performance triangle-mesh streaming for unbounded datasets project funded by netidee Projektförderung. As a member of the Rendering and Modeling workgroup, Celarek contributes to the department's research in physically based rendering, 3D data processing, and visualization techniques, with particular expertise in light transport algorithms and Gaussian mixture models.

Luo Kai Hongمشاهده پروفایل

استاد

• Fluid mechanics and turbulence
• Computational engineering
• Multiscale modelling and simulation
• +۶ مورد دیگر

Prof. Luo Kai Hong is a Professor and Chair of Energy Systems at University College London (UCL) within the Faculty of Engineering Sciences. He leads the Energy and Environment Research Division and directs the UK Consortium on Mesoscale Engineering Sciences (UKCOMES). His career spans roles at the University of Southampton and Queen Mary University of London, where he advanced computational fluid dynamics (CFD) and energy systems research. Key leadership roles include heading the Energy Technology Research Group and serving on the National Engineering Policy Centre Committee of the Royal Academy of Engineering. He earned his PhD from the University of Cambridge in 1991 under Prof. Ken Bray FRS, followed by postdoctoral research at Imperial College London and Queen Mary University of London. His research focuses on fluid mechanics, multiscale modeling, and energy technologies, with notable contributions to DNS/LES simulations and mesoscale engineering systems. Prof. Luo’s work bridges fundamental science and industrial applications, emphasizing decarbonization and renewable energy innovation. His research interests include fluid turbulence, combustion science, and multiphase flows. He has pioneered lattice Boltzmann methods and neural network-driven computational techniques for complex fluid systems. Recent work emphasizes CO2 sequestration mechanisms and hydrogen energy systems. Key Projects: UKCOMES, National Engineering Policy Contributions, Mesoscale Modeling Consortia Education: PhD (University of Cambridge, 1991), Postdoctoral Training (Imperial College London/Queen Mary University of London) Prof. Luo has been honored with the ASME James Harry Potter Gold Medal, AIAA Energy Systems Award, and fellowships from the Royal Academy of Engineering. His 500+ publications include highly cited works in computational physics and energy systems.

Tobias Schreckمشاهده پروفایل

استاد

• Visual Data Analysis
• 3D Object Retrieval
• Visual Analytics
• +۵ مورد دیگر

Tobias Schreck is a Professor at the Institute of Visual Computing (formerly Computer Graphics and Knowledge Visualization) at Graz University of Technology, affiliated with the Faculty for Computer Science and Biomedical Engineering. His research focuses on Visual Data Analysis, 3D Object Retrieval, and Immersive Analytics, with applications in engineering and industrial contexts. He holds a Dr.rer.nat. from the University of Konstanz and has held academic positions including Assistant Professor at the University of Konstanz and Head of the Visual Search and Analysis Group at TU Darmstadt. Education: Dr.rer.nat., University of Konstanz (2006) M.Sc. in Information Engineering, University of Konstanz (2002) Dipl.-Volkswirt (M.Sc. Economics), University of Konstanz (1999) His research interests include visual analytics for high-dimensional and spatial-temporal data, digital libraries, and collaborative analysis tools. He leads funded projects like HEREDITARY (EU Horizon Europe) and VR4CPPS (FFG), and has served as a program chair for IEEE VAST and EuroVis. His work emphasizes user interaction, eye-tracking integration, and immersive visualization techniques. He supervises a research team including postdocs, PhD students, and student assistants, and collaborates on projects like CrossSAVE-CH and the Joint PhD Programme with Nanyang Technological University. His contributions span publications in IEEE Transactions, EuroVis, and ACM conferences, addressing challenges in data exploration, anomaly detection, and visualization design.

Keshav Pingaliمشاهده پروفایل

استاد

• Programming languages
• Parallel computing
• Runtime systems
• +۲ مورد دیگر

Keshav Pingali is currently the W. A. "Tex" Moncrief Chair of Computing and a Professor in the Department of Computer Science at The University of Texas at Austin. He also serves as Director of the Center for Grid and Distributed Computing at the Oden Institute for Computational Engineering and Sciences. Previously, he held positions as India Chair of Computer Science at Cornell University (2003-2006) and Professor in both the Department of Computer Science and the Department of Electrical and Computer Engineering there (1999-2006). Education: Sc.D. (1986) and S.M./E.E. (1983) from MIT B.Tech. (1978) from Indian Institute of Technology, Kanpur Research Interests: Keshav works in programming languages, compilers, and runtime systems for program optimization and parallelization, with a focus on multicore processors and irregular applications from domains like social networks, data mining, and graphics. Scientific Awards: ACM SIGPLAN Programming Languages Achievement Award (2024) ACM/IEEE CS Ken Kennedy Award (2023) IEEE Charles Babbage Award (2023) Foreign Member, Academia Europaea (2020) IIT Kanpur Distinguished Alumnus Award (2013) Fellowships: ACM (2012), AAAS (2010), IEEE (2010) NSF Presidential Young Investigator Award (1989-1994) IBM Faculty Development Award (1986-1987) President's Gold Medal and Lalit Narain Das Memorial Gold Medal at IIT Kanpur (1978) Leadership & Service: He has served on steering committees of major conferences (PLDI, PPoPP), chaired editorial boards (ACM Transactions on Programming Languages and Systems), and contributed to NSF advisory committees. His research group maintains a homepage at http://iss.ices.utexas.edu/ .

Yuan Xiao-Congمشاهده پروفایل

استاد

• Optical Communication
• Photoacoustic Microscopy
• Optical Trapping
• +۴ مورد دیگر

Professor Yuan Xiao-Cong is a Chair Professor and Director of the Nanophotonics Research Centre at Shenzhen University, China, leading an internationally recognized research group in nanophotonics. He holds adjunct roles at Zhejiang Lab and has held distinguished positions at Nankai University and Nanyang Technological University. His work spans optical singularities, plasmonics, and high-capacity optical communication systems. \n\n Research interests include orbital angular momentum (OAM) multiplexing, photonic skyrmions, and advanced optical communication technologies. Notable achievements include pioneering OAM-based free-space communication systems with Huawei, achieving 160 Tbit/s speeds, and developing plasmonic optical tweezers for nanoscale metrology. \n\n He has published over 500 papers in top journals like Nature Physics , Science Advances , and Physical Review Letters , and has received honors such as Fellowships from the Optical Society of America and Chinese Optical Society. His contributions include the design of metasurface-based multiplexers and spin-momentum frameworks for nanoscale dynamics. \n\n His lab, the Nanophotonics Research Centre, is central to Shenzhen’s research ecosystem, focusing on topological optics, chiral matter interactions, and integrated photonics. Current efforts explore applications in high-performance computing and subwavelength imaging.

Zomaya Albertمشاهده پروفایل

عضو هیئت علمی

• Complex systems
• Parallel and distributed computing
• Sustainable computing
• +۷ مورد دیگر

Dr. Zomaya is the Chair Professor of High Performance Computing & Networking and Founding Director of the Centre for Distributed & High Performance Computing at the University of Sydney, Australia. He has held leadership roles including Deputy Head of School and Head of School at The University of Western Australia and the University of Sydney. A Fellow of AAAS, IEEE, and IET, he leads research in distributed computing systems, sustainable computing, and edge intelligence. His research spans parallel/distributed computing, energy-efficient systems, and cloud/edge technologies. He has published over 600 papers, authored/edited 20+ books, and secured $22M+ in funding from ARC, Cisco, IBM, and others. He currently supervises 12 PhD students and has mentored 35 PhD completions. Key awards include the ACM MSWIM 2017 Fessenden Award and IEEE Technical Achievement Award (2014). He serves as Editor-in-Chief of IEEE Transactions on Sustainable Computing and has chaired over 700 conferences. His current projects focus on data analytics over cloud/edge platforms and holistic energy-aware scheduling for distributed systems.

Annette Mützeمشاهده پروفایل

استاد

• Electric Drives and Machines
• Electrical Engineering
• Materials Science
• +۳ مورد دیگر

Univ.-Prof. Dr.-Ing. Annette Mütze is a full Professor at Graz University of Technology's Electric Drives and Machines Institute, serving as its head since 2010. Her academic journey includes roles as an Assistant/Associate Professor at the University of Wisconsin-Madison and University of Warwick. She holds a PhD from Technical University of Darmstadt (summa cum laude) and dual engineering degrees from Ecole Centrale de Lyon and TU Darmstadt. Her research focuses on interdisciplinary advancements in electric drives and machines, addressing efficiency, reliability, and system integration across electrical engineering, materials science, and systems engineering domains. Key research contributions include foundational work on inverter-induced bearing currents, manufacturing impacts on steel performance, and fractional horsepower drive innovations. She leads high-profile projects like the Christian Doppler Laboratory for Brushless Drives and the FWF-funded Computational Electric Machine Lab. Awards include the 6th Nagamori Award (2020), IEEE Industry Application Society’s top paper prize (2019), and NSF CAREER Award (2005). Education: PhD (TU Darmstadt, 2004), Dual Engineering Degrees (Ecole Centrale Lyon/TU Darmstadt, 1999) Funded Projects: Christian Doppler Lab (2016–2023), SFB Computational Electric Machine Lab (2022–2026) Awards: IEEE Fellow (2016), Nagamori Award (2020), NSF CAREER (2005) Lab/Teams: Head of TU Graz’s Electric Drives and Machines Institute, collaborating on industry-academia projects

• Railway Engineering
• Structural Engineering
• Civil Engineering
• +۳ مورد دیگر

Prof. Josef Fink is a faculty member at TU Wien's Institute of Structural Engineering (E212). His research focuses on railway bridge dynamics, track-bridge interaction, and structural dynamics analysis. He specializes in dynamic response evaluation of railway bridges under high-speed traffic, damping factor determination, and SCSC-plate (steel-concrete-steel composite) applications in bridge engineering. His work integrates experimental and numerical methods to address challenges in railway infrastructure resilience and vibration control. Key research areas include: ballasted track behavior, longitudinal/lateral track-bridge interaction, Lehr's damping coefficient analysis, and composite materials for bridge decks. He collaborates on projects such as the Network Lab and contributes to advancements in structural health monitoring and finite element modeling for bridge systems. Prof. Fink has supervised numerous theses on topics like SCSC plate performance, damping analysis, and bridge-soil interaction. His publications emphasize practical engineering solutions for dynamic railway infrastructure challenges.

Karl Hollausمشاهده پروفایل

پژوهشگر

• Computational Electromagnetics
• Multiscale Finite Element Methods
• Eddy Current Problems
• +۳ مورد دیگر

Prof. Karl Hollaus is a researcher at TU Wien's Computational Mathematics in Engineering Research Group within the Faculty of Mathematics and Geoinformation . His work focuses on computational electromagnetics , multiscale finite element methods (MSFEM) , and eddy current problem simulations . He leads projects such as Effektive Materialtransformation für ferromagnetische Bleche and Magnetik für Filter , addressing challenges in laminated iron cores and magnetic materials. His research emphasizes high-performance computational techniques for engineering applications, including nonlinear systems and material modeling. Key research interests include domain decomposition methods , vector hysteresis modeling , and effective interface conditions . He has developed innovative approaches for simulating eddy current losses, magnetic flux routing in electrical machines, and ventilation panel thermal behavior using multiscale FEM. Collaborators include Markus Schöbinger and Valentin Hanser, with whom he co-authored numerous conference papers and journal articles. Education: Dipl.-Ing. (Master's) and Dr.techn. (PhD) in engineering. Awards: None explicitly listed. Recent publications (2022–2023) highlight advancements in harmonic balance methods combined with model order reduction for nonlinear systems, magnetic microwire materials for flux guidance, and effective material modeling for laminated cores. His work bridges theoretical computational mathematics with practical engineering solutions, particularly in electromechanical systems and metamaterials. Advising: Supervised PhD candidates Valentin Hanser (2021 thesis on eddy current computations) and Markus Schöbinger (multiple co-authored presentations). Active in TU Wien's Network Lab and contributes to the Scientific Computing and Modelling Research Division .

• Neoplasm
• Magnetic Resonance Imaging
• Molecular Imaging
• +۳ مورد دیگر

Vanessa Fröhlich is a Researcher at the Competence Center for Preclinical Imaging and Medical Technology within the Faculty of Health, specializing in advanced medical imaging techniques. Her work bridges preclinical radiation research, molecular imaging, and artificial intelligence applications in radiology. Education: Master's degree in MedTech (approved February 5, 2016) Bachelor's degree in radiology technology (approved July 11, 2013) Her research focuses on high-field magnetic resonance imaging (15.2T) for cancer studies, with emphasis on geometric distortion correction in preclinical settings and AI-driven image analysis. Key areas include neoplasm imaging, blood-brain barrier studies, and molecular imaging techniques for breast cancer subtypes. She develops open-source computational pipelines to enhance multimodal data processing in radiation research. Recent publications (2024-2025) demonstrate a strong trend toward open-source tool development for preclinical imaging and AI validation in mammography. Her work addresses critical challenges in high-field MRI distortion correction while advancing quantitative analysis methods for radiation therapy applications. Fröhlich actively participates in major research initiatives including the PAIR project (2022-2026) and Applied Molecular Imaging project (2021-2026), collaborating with interdisciplinary teams on molecular imaging technologies and preclinical radiation methodologies. These projects provide critical infrastructure for her computational imaging research. As a core member of the Competence Center for Preclinical Imaging and Medical Technology, she contributes to cutting-edge research on preclinical imaging platforms, working with teams focused on translating advanced imaging techniques to clinical applications in oncology and neurology.

Markus Steinbergerمشاهده پروفایل

دانشیار

• GPU Computing
• Computer Graphics
• Parallel Processing
• +۳ مورد دیگر

Markus Steinberger is an Associate Professor at Graz University of Technology (Austria), leading the GPU Computing and Visualization Group at the Institute for Computer Graphics and Vision. He holds an MSc and PhD in Computer Science from TU Graz, with a postdoc and multiple leadership roles in academic and industry labs, including NVIDIA and the Max Planck Institute. His research focuses on GPU scheduling, parallel computing, and high-performance visualization techniques. He has led teams at TU Graz, Huawei Technologies (Cloud Rendering Lab), and the Max-Planck-Center for Visual Computing. Education 11/2020: Habilitation in Practical Computer Science at TU Graz 10/2013: PhD in Computer Science (Dynamic Resource Scheduling on Graphics Processors) 10/2010: MSc in Telematics 10/2005: BSc in Telematics Research Interests Steinberger’s work centers on optimizing GPU computing for real-time rendering and parallel processing. He explores efficient resource scheduling, dynamic graph management, and high-performance visualization techniques, with applications in cloud rendering, scientific visualization, and interactive graphics. His methods emphasize scalability and real-world applicability in both academic and industrial settings. Awards Heinz Zemanek Prize (2016) – First Austrian recipient GI Dissertation Prize (2014) – Best dissertation in German-speaking computer science Multiple Eurographics Best Paper Awards (2021, 2020, 2014) HPEC Best Student Paper (2017) ACM CHI Honorable Mention (2014) Advising & Grants Steinberger has advised numerous students and led projects funded by EU initiatives and industry partnerships. His research has been recognized through grants supporting GPU scheduling, cloud rendering, and parallel computing. Labs & Teams Leads the GPU Computing and Visualization Group at TU Graz and directed the Cloud Rendering Lab at Huawei, focusing on scalable rendering solutions for distributed systems.

Neda Ghiassiمشاهده پروفایل

پژوهشگر

• Urban Energy Modeling
• GIS Applications
• Sustainable Architecture
• +۳ مورد دیگر

Neda Ghiassi is an affiliated researcher at TU Wien's E259 - Institut für Architekturwissenschaften , focusing on urban energy modeling and computational frameworks. Her work integrates Geographic Information Systems (GIS), data analysis, and interdisciplinary approaches to address urban energy challenges. Key projects include MOTIVE (vacuum glass integration in construction) and EMULATE (urban energy assessment tools). She has published extensively on topics like high-resolution energy modeling, building product data handling, and cluster analysis for urban systems. Her research emphasizes sustainable urban development through innovative computational methods, bridging technical, social, and policy dimensions. She collaborates on projects funded by Research Studios Austria and contributes to teaching via research-guided courses. Notable outputs include a doctoral thesis on an hourglass model for urban energy systems and reports detailing project advancements in energy efficiency and building technology. Her expertise spans technical documentation, multi-stakeholder data collaboration, and scenario-based modeling. Current work explores human-centric energy modeling (MAIN_STREAM) and semantic web approaches for building product data. No awards are explicitly listed, but her contributions reflect significant engagement in academic and applied research.

• Formal Methods
• Model-Driven Engineering
• SAT/QBF Solving
• +۴ مورد دیگر

Martina Seidl is a researcher and principle investigator at TU Wien's Institut für Softwaretechnik und Interaktive Systeme (E188). Her main affiliation is within the Faculty of Informatics. She leads the FAME Project (Formalizing and Managing Evolution in Model-Driven Engineering), focusing on advancing formal methods in software engineering. Her research interests include formal verification techniques, SAT/QBF solving algorithms, model-driven engineering, and automated reasoning. She has contributed to advancements in quantified Boolean formula (QBF) solving, parallel computing methodologies for logical problems, and formal methods in software model analysis. Her work spans theoretical computer science and practical applications in automated theorem proving and model checking. Notable contributions include expansion-based QBF solving approaches, parallel solving frameworks, and feature-based classifications of formal verification techniques. Seidl co-organized the QBF Gallery initiative, which curates benchmark suites for quantified Boolean formula competitions. She has also published extensively on clause redundancy optimization, blocked clause analysis, and the integration of formal methods in educational contexts like UML@Classroom.