جست‌وجوی استاد و پژوهشگر

Onur Mutluمشاهده پروفایل

استاد

• Computer architecture
• Bioinformatics
• Computer memory systems
• +۷ مورد دیگر

Onur Mutlu is a Full Professor of Computer Science at ETH Zurich (since 2015), with adjunct professor positions at Carnegie Mellon University (since 2016) and Bilkent University (since 2015). His academic career spans prestigious institutions including Carnegie Mellon University where he served as Assistant Professor (2009-2013) and Strecker Early Career Endowed Professor (2013-2016). His research focuses on computer architecture, particularly memory systems, with expertise in: Computer memory systems and DRAM architecture Multi-core processor design Fault tolerance and reliability Hardware/software interactions Systems security related to hardware Emerging memory technologies Dr. Mutlu's work has significantly impacted both academic research and industry practices. His discovery of the RowHammer problem created a new field at the intersection of hardware reliability and systems security. His research on memory controllers, flash memory reliability, and emerging memory technologies has been adopted by major technology companies including Samsung, Intel, IBM, and Microsoft. His numerous scientific awards include the IEEE Computer Society Harry H. Goode Memorial Award (2025), IFIP Jean-Claude Laprie Award (2024), Huawei OlympusMons Award (2023), IEEE Computer Society Edward J. McCluskey Technical Achievement Award (2020), and ACM SIGARCH Maurice Wilkes Award (2019). He is an IEEE Fellow (2018), ACM Fellow (2017), and member of Academia Europaea (2018). Dr. Mutlu has received significant industry recognition with Faculty Awards from Google, Facebook, HP, Huawei, IBM, Intel, Microsoft, NSF, and VMware. His work has earned over 20 Best Paper awards and 12 papers selected for IEEE Micro's Top Picks as some of the most influential papers in computer architecture. He maintains strong industry connections, having worked at Microsoft Research (2006-2009), Intel (multiple summers), AMD (multiple summers), VMware (2016), and Google (2016), enabling direct technology transfer of his research ideas into commercial products.

John V. Tuckerمشاهده پروفایل

استاد

• Theory of synchronous algorithms with digital and analogue components
• History of science
• Physical foundations of computation
• +۵ مورد دیگر

John V. Tucker is a Professor of Computer Science at Swansea University since 1989. He served as Head of the Department of Computer Science (1994-2007) and Head of the School of Physical Sciences (2007-2011). His research spans theoretical computer science, history of computing, and computational foundations of physics. Education: MSc in Mathematical Logic and Foundations of Mathematics and Computer Science (1974) and PhD in Mathematical Logic (1977) from the University of Bristol. His research interests include the theory of synchronous algorithms, physical foundations of computation, algebraic specification, and algorithmic models of nonlinear systems. He pioneered equational specification methods for microprocessors, 3D graphics, and whole-heart modeling. Scientific awards include: Founding Fellow of the Learned Society of Wales (2010) Elected Fellow of British Computer Society (1998) He has supervised 16 PhD students directly and contributed to supervision in Uppsala, served on EU project evaluation panels, chaired appointments, and led editorial roles in journals and book series.

• Computer Vision
• Image Processing
• Stereo Matching
• +۴ مورد دیگر

Asmaa Farouk Mohammed is a researcher at Vienna University of Technology's Department of Software Technology and Interactive Systems. Her work focuses on computer vision, stereo matching, and real-time algorithms, with a strong emphasis on cost-volume filtering and adaptive support weight techniques. She holds a PhD in computer science and has contributed significantly to 3D video technology and interactive video segmentation. Key research areas include: Efficient stereo matching algorithms for real-time applications Spatio-temporal filtering for video processing Interactive systems for object segmentation Geodesic-based image processing methodologies Her publications demonstrate a clear trajectory in advancing both theoretical and applied aspects of visual correspondence and 3D reconstruction. Collaborations with researchers like Michael Bleyer and Margrit Gelautz highlight her role in interdisciplinary projects. While no specific grants or awards are listed, her extensive publication record reflects sustained contributions to the field of computer vision since 2009. She is affiliated with the Network Lab at TU Wien, contributing to cutting-edge research in interactive systems and algorithm optimization.

• Visual Interfaces
• Interactive Data Exploration
• Human-Computer Interaction
• +۳ مورد دیگر

Manuela Waldner is an Assistant Professor (tenure track) at TU Wien's Department of Computer Graphics and Visualization. She leads research in visual interfaces for exploring large-scale data, focusing on human-computer interaction and attention guidance in complex visualizations. Her work spans academic roles including Visualization Group membership and PhD Program Visual Heritage involvement. Research interests include: Interactive data exploration Guided visual attention mechanisms Emerging display environments (VR, multi-display systems) Publications emphasize practical applications of visualization techniques in domains like geographic data, medical imaging, and collaborative analytics. Notable projects include weBIGeo (geographic visualization) and Deskollage (visual information foraging). Awards include the Best Paper Award at EuroVA 2024 . Teaching roles include courses on Information Visualization , Computer Graphics , and Visual Computing . Active in academic service through program committees and editorial roles.

Camille Schreckمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Physics-based Simulation
• Geometric Modeling
• +۲ مورد دیگر

Camille Schreck is a researcher in Computer Graphics currently holding an Inria Starting Faculty Position (ISFP) at Inria Nancy within the MFX Team. She has been actively contributing to the field of computer graphics with a focus on physics-based simulation and geometric modeling since completing her PhD in 2016. Her educational background includes an engineering degree from ENSIMAG (Grenoble INP) in 2013, followed by a PhD at the University of Grenoble-Alpes supervised by Stefanie Hahmann and Damien Rohmer. Between 2016 and 2020, she conducted postdoctoral research at IST Austria in Chris Wojtan's group. Dr. Schreck's research spans physics-based simulation of natural phenomena, geometric modeling, and computational fabrication techniques. Her work demonstrates particular expertise in simulating deformable materials like paper, water dynamics, and developing novel methods for 3D printing and shape manipulation. She has successfully bridged theoretical computer graphics with practical applications in digital fabrication. Her publication record shows consistent high-impact contributions to top computer graphics venues including SIGGRAPH, Eurographics, and Computer Graphics Forum, with recent work focusing on star-shaped particle simulation, self-shaping 3D printed structures, and efficient mesh processing techniques. The progression of her research demonstrates a clear trajectory from fundamental paper simulation to increasingly complex physical phenomena and fabrication methods. Young Research Fellow of Eurographics France (YFR EGFR) - 2024 Dr. Schreck serves on numerous program committees including SIGGRAPH, Eurographics, and Shape Modeling International. She teaches courses in computer graphics and parallelism at ENSG-GeoRessources and Telecom Nancy, both part of Université de Lorraine. Her teaching spans from 2020 to the current 2024-2025 academic year. As a member of the MFX research team at LORIA (Inria Nancy), she collaborates on advancing the state of the art in physics-based animation and geometric modeling, with her StarDEM project representing a significant contribution to particle simulation methods.

Chao Zhangمشاهده پروفایل

دانشیار

• Dielectrics
• Electrochemistry
• Electrolyte materials
• +۳ مورد دیگر

Chao Zhang is an Associate Professor at the Department of Chemistry-Ångström Laboratory, Uppsala University, specializing in computational electrochemistry and multi-scale modeling of electrolyte materials. His research bridges atomistic simulations with machine learning approaches to address challenges in energy storage and conversion systems. Education: Dr. rer. nat. from RWTH Aachen University (2013); Docent from Uppsala University (2020) Appointments: Postdoctoral researcher at the University of Cambridge (prior to joining Uppsala in 2017) His group develops finite-field methods for computational electrochemistry and investigates electrified solid-liquid interfaces. Recent research trends include neural rendering for underwater SLAM systems (2025), robust path-following control in marine robotics, and event-based localization in LiDAR-integrated environments. Scientific Awards: ERC Starting Grant (2020) Junior Research Fellowship, Wolfson College (2015) Jülich Excellence Prize for Young Scientists (2013)

Markus Tatzgernمشاهده پروفایل

استاد

• Augmented Reality
• Virtual Reality
• Mixed Reality
• +۳ مورد دیگر

Dr. Markus Tatzgern serves as Professor for Mixed Reality and Game Development and Head of Research at the Department for Creative Technologies, Salzburg University of Applied Sciences. He leads the Digital Realities Lab, a multi-disciplinary research group focused on human-centered solutions at the intersection of software, design, and creative engineering for technology interaction. His academic foundation includes a Master of Science and Doctorate with highest distinction from Graz University of Technology. He furthered his expertise through post-doctoral research at the Christian Doppler Laboratory for Handheld Augmented Reality, recognized as a world-leading facility in mobile AR. Tatzgern's research spans Augmented Reality, Virtual Reality, Mixed Reality, and Human-Computer Interaction with emphasis on practical applications. His work addresses critical challenges in view management, haptic feedback systems, industrial procedure visualization, medical imaging interfaces, and game development. Key contributions include novel techniques for eye-perspective rendering, breathing-based VR interaction, and latency compensation in medical robotics. His publication trajectory reveals evolving focus from foundational AR visualization (2010-2016) toward applied XR solutions in healthcare, industrial training, and user experience optimization (2017-2024). Recent work demonstrates increasing integration of physiological inputs, real-world safety applications, and enterprise-grade XR prototyping. Scientific recognition includes: Honorable Mention for Best Paper at CHI 2022 for 'AirRes Mask' breathing interface Honorable Mention for Best Paper at IEEE 3DUI 2017 for adaptive rendering techniques As Head of Research, Tatzgern actively shapes the field through program committee roles and peer review for top venues including ACM CHI and IEEE VR. His four patents in AR visualization demonstrate translational impact from academic research to practical applications. The Digital Realities Lab operates as an innovation hub where software engineers, designers, and creative technologists collaborate on next-generation interaction paradigms. Current projects emphasize medical XR applications, industrial training systems, and accessibility-focused AR solutions for vulnerable populations.

Renata Georgia Raidouمشاهده پروفایل

دانشیار

• Biomedical Visualization
• Visual Analytics
• Medical Imaging
• +۳ مورد دیگر

Associate Professor Renata Georgia Raidou is affiliated with the Institute for Visual Computing and Human-Centered Technology at the Vienna University of Technology (Faculty of Computer Science). Originally from Greece, she earned her B.Sc./M.Sc. in Electrical and Computer Engineering from the National Technical University of Athens , followed by an M.Sc. in Biomedical Engineering from Delft University of Technology and a Ph.D. in Medical Visualization from Eindhoven University of Technology (2017). She has held postdoctoral and faculty roles at TU Wien and the University of Groningen. B.Sc./M.Sc.: Electrical and Computer Engineering, National Technical University of Athens (2005–2010) M.Sc.: Biomedical Engineering, Delft University of Technology (2010–2012) Ph.D.: Medical Visualization, Eindhoven University of Technology (2013–2017) Renata’s research bridges Visualization , Visual Analytics , and Machine Learning in medical contexts, particularly focusing on cancer radiotherapy . Her key areas include: Medical Visualization and Visual Analytics for P4 medicine (personalization, predictiveness, preventiveness, patient participation) Uncertainty Visualization in anatomical variability and biological models Ensemble and Comparative Visualization for large patient cohorts Data Physicalization for anatomical education and public engagement She leads the B.Sc. Informatics (Digital Health specialization) and M.Sc. Medical Informatics programs at TU Wien and contributes to international academic communities through editorial roles (e.g., Associate Editor for Computers & Graphics ) and workshop organization. Scientific Awards Dirk Bartz Prize for Visual Computing in Medicine (2017) EuroVis Best PhD Award (2018) EuroVis Young Researcher Award (2022) Best short paper awards at IEEE Vis (2020), EG VCBM (2022, 2023)

Martin Fedaمشاهده پروفایل

پژوهشگر

• Radiosity
• Photorealistic Image Synthesis
• Parallel Graphics Algorithms
• +۱ مورد دیگر

Martin Feda is an Associated Researcher at the Institute of Computer Graphics, Vienna University of Technology, where he previously served as Assistant Professor. His research focuses on advancing radiosity methods for photorealistic image synthesis, with significant contributions to algorithmic efficiency and visual quality in global illumination rendering. Feda's primary research interests include radiosity, photorealistic image synthesis, and parallel graphics algorithms. He has pioneered techniques for stochastic radiosity, progressive refinement, and parallel implementations to accelerate computation. Key innovations involve hierarchical subdivision algorithms, methods for reducing shadow leaks without explicit meshing, improvements to intermediate radiosity images through directional light, and overshooting techniques to speed up progressive radiosity. His work consistently bridges theoretical advancements with practical applications for handling highly complex scenes. Analysis of his 1991-1997 publication record reveals a clear evolution in radiosity research: early work established parallel implementations on transputers and foundational radiosity algorithms, while later contributions developed sophisticated stochastic and hierarchical methods. The trajectory shows increasing emphasis on Monte Carlo techniques and computational optimizations to achieve greater efficiency and visual fidelity in global illumination, with consistent publication in top-tier graphics venues like Eurographics and Computer Graphics Forum.

Lukas Radlمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Lukas Radl is a University Assistant and PhD Student at the Institute of Visual Computing, Graz University of Technology, where he works on 3D Scene Representations for View Synthesis under the supervision of Markus Steinberger. His research focuses on advancing real-time rendering techniques, particularly in Neural Radiance Fields (NeRF) and Gaussian Splatting, to bridge digital and physical world representation. Education: Master of Science in Computer Science (with distinction), Graz University of Technology, 2018-2023 Bachelor of Science in Software Engineering, Graz University of Technology, 2018-2023 Research Focus: Radl's work intersects Computer Graphics, Computer Vision, Machine Learning, and Parallel Processing. He pioneers practical implementations of Radiance Field Representations, addressing critical challenges in view consistency, anti-aliasing, and real-time performance for interactive applications. His innovations enable robust rendering in virtual reality and complex lighting scenarios through novel geometric and neural approaches. Publication Impact: Recent publications (2024-2025) demonstrate a cohesive trajectory toward production-ready radiance field systems. Key advances include sorting algorithms for view consistency (StopThePop), anti-aliasing frameworks for Gaussian Splatting (AAA-Gaussians), and VR-optimized pipelines (VRSplat). These contributions establish new standards for real-time performance while maintaining visual fidelity across diverse hardware platforms. Scientific Recognition: Dean's List (top 5% of students) at Graz University of Technology (2019, 2020) Mentorship & Service: Radl actively shapes academic discourse as a reviewer for premier venues (CGF, ICCV, TVCG) and mentors students through open projects in real-time rendering. His teaching portfolio spans exercise coordination for core visual computing courses since 2020, with current leadership in Real-Time Graphics and Computer Graphics instruction. He fosters talent through student projects advancing Gaussian Splatting implementations. Research Ecosystem: Embedded in Graz University of Technology's Institute of Visual Computing, Radl collaborates within a specialized team focused on radiance field optimization. The group maintains active pipelines for NeRF and Gaussian Splatting research, with strong industry connections evidenced by his upcoming Meta Reality Labs internship. Current projects target foveated rendering, geometric consistency, and editing capabilities for next-generation AR/VR systems.

• Statistics
• Data Science
• Machine Learning
• +۴ مورد دیگر

Ursula Laa is an Assistant Professor at the Institute of Statistics , University of Natural Resources and Life Sciences, Vienna (BOKU) since 2020. Previously, she was a Postdoctoral Researcher at the Department of Econometrics & Business Statistics and School of Physics & Astronomy , Monash University (2017–2020), and earned her doctorate in particle physics at the University of Grenoble-Alpes , France (2014–2017). Her academic journey began with physics studies at the University of Vienna (2007–2014). Research Interests: She specializes in statistics, with a focus on data visualization, machine learning, computational statistics, and environmental statistics. Her work bridges high-dimensional data analysis with applications in particle physics and environmental studies, particularly in climate change and hydrology. Scientific Contributions: Ursula has actively contributed to projects involving climate change impacts on hydrological systems interactive visualization tools (lionfish, cubble, tourr) statistical modeling for environmental and particle physics data Her recent publications emphasize tour algorithms, projection pursuit, and spatiotemporal indexing, reflecting a blend of data science and domain-specific applications. Awards & Recognition: She has been a keynote speaker at DAGStat 2025 active participant in international conferences (EGU, useR!, Joint Statistical Meetings)

• Medical Imaging
• Haptic Systems
• Computer Graphics
• +۶ مورد دیگر

Prof. Matthias Harders is a Professor at the Department of Computer Science, University of Innsbruck. His work focuses on medical imaging, haptic systems, virtual reality, and data-driven simulation. He leads research in interactive visualization tools, medical device development, and machine learning applications in healthcare and environmental engineering. Research areas include haptic augmented reality for surgical training, deformable medical image registration, and synthetic data generation for retinal imaging. Notable projects include SPBView for eye movement analysis and the PoRi device for post-stroke rehabilitation. His work bridges computer science with biomedical applications, emphasizing real-world impact in healthcare technology. Publications span medical simulation, machine learning for biogas prediction, and perceptual interfaces. He collaborates on EU-funded projects involving VR/AR systems and has contributed to open-source tools for point cloud analysis and surgical planning.

Florian Huberمشاهده پروفایل

استاد

• Bayesian Econometrics
• Macroeconomics
• State Space Modeling
• +۳ مورد دیگر

Florian Huber is a Professor of Economics and Vice-Head of the Department of Economics at the University of Salzburg. His research focuses on Bayesian macroeconometrics, particularly large-scale non-linear multivariate time series modeling. He has published in top journals such as the Journal of Econometrics and the Journal of Applied Econometrics. His work integrates machine learning and nonlinear methods to analyze macroeconomic dynamics and uncertainty. Huber serves as a Scientific Consultant to the Oesterreichische Nationalbank (OeNB), European Central Bank (ECB), and European Commission. He holds roles as Associate Editor of Macroeconomic Dynamics, Senior Scientist at the International Institute for Applied Systems Analysis (IIASA), and Research Fellow of Bocconi University’s Baffi Center. His accolades include the 2024 Kurt-Zopf-Förderpreis and Fellow status in the Society for Economic Measurement. Research interests span Bayesian econometrics, state space modeling, forecasting, and financial spillovers. His recent work addresses topics like growth-at-risk, nonlinear VAR models, and real-time inflation forecasting. Huber’s contributions bridge theoretical econometrics with policy-relevant analysis, emphasizing the integration of big data and structural models.

Günther Raidlمشاهده پروفایل

دانشیار

• Combinatorial Optimization
• Heuristic Optimization
• Metaheuristics
• +۷ مورد دیگر

Günther Raidl is an Associate Professor and Head of the Algorithms and Data Structures Group at the Institute of Computer Graphics and Algorithms, Faculty of Informatics, TU Wien. He holds a Dipl.-Ing. (1992), Ph.D. (1994), and Habilitation (2003) from TU Wien. His research focuses on combinatorial optimization, heuristic methods, and hybrid optimization techniques, addressing large-scale problems in transportation, network design, and cutting/packing. He leads a group of 1 PostDoc, 8 PhD candidates, and collaborates with institutions like the Vienna Graduate School on Computational Optimization (VGSCO). Education: Dipl.-Ing. in Computer Science (1992), TU Wien Ph.D. in Computer Science (1994), TU Wien Habilitation (2003), TU Wien Research Interests: Raidl’s work combines exact and heuristic optimization techniques, including mixed-integer programming, metaheuristics, and matheuristics. Applications span transportation systems (e.g., electric vehicle routing, bike-sharing systems), network design, and bioinformatics. His group leverages high-performance computing resources, such as the Vienna Scientific Cluster (VSC). Publications & Awards: Over 115 reviewed articles in journals/conferences like INFORMS Journal on Computing and Evolutionary Computation. Notable recognition includes the EvoStar 'Old Croc' Award (2012) for contributions to evolutionary computation. Advising & Grants: Supervises 8 funded PhD students and contributes to the Vienna Graduate School’s DK funding (€20,000/year for personnel and travel). Labs/Teams: Algorithms and Data Structures Group at TU Wien, collaborating with researchers like Monika Henzinger and Nysret Musliu.

• Visual Computing
• Computer Graphics
• Computer Vision
• +۳ مورد دیگر

Ana Maria Stanescu is a Researcher at the Institute of Visual Computing, Technische Universität Graz. She holds academic degrees including Dipl.-Ing. Dr.techn. and BSc. Her work focuses on visual computing and related fields, with affiliations at the university's Plüddemanngasse and Inffeldgasse campuses. Contact: +43 316 873-5048 or stanescu@tugraz.at. Education: Diplom-Ingenieur (Dipl.-Ing.), Doctor of Technical Sciences (Dr.techn.), and Bachelor of Science (BSc). Research interests center on advanced visual computing techniques, including computer graphics, image processing, and 3D reconstruction. She is affiliated with TU Graz's Institute of Visual Computing, contributing to its interdisciplinary research initiatives. No academic awards or grants are explicitly listed, and there is no mention of advised students or specific laboratory affiliations beyond her department.