Karl Haubenwallner is a PhD student at the Institute of Computer Graphics and Vision at the Technical University of Graz , supervised by Prof. Dieter Schmalstieg. His research focuses on procedural content generation for computer games, with specific interests in shape grammars, artificial intelligence, and GPU computing. Education : Master's degree in Computer Science from Graz University of Technology (2017) His research bridges computational design and AI, aiming to automate content creation in gaming environments. The 2017 publication ShapeGenetics demonstrates his work integrating genetic algorithms with procedural modeling techniques. While no scientific awards are mentioned in the text, his academic work centers on scalable solutions for game development using evolutionary computation and parallel processing frameworks.
Ronny Lorenz is a researcher affiliated with the Faculty of Chemistry and Department of Theoretical Chemistry , focusing on RNA structure prediction, computational biology, and bioinformatics. His work integrates chemical probing data, phylogenetic constraints, and thermodynamic models to advance RNA folding algorithms. RNA Folding Algorithms : Development of tools like ViennaRNA Package Modified Nucleotides : Energy parameter estimation and molecular dynamics simulations Deep Learning Applications : Improving cotranscriptional folding predictions His research trends include RNA-RNA interactions , G-quadruplex prediction , and non-redundant sampling of RNA structural landscapes. He actively presents findings through poster presentations and scientific talks . Projects : "Bestimmung von RNA Strukturen durch Probing und Vorhersagen" (2020–2023) Collaborations : With Ivo Hofacker, Thomas Spicher, and Yuliia Varenyk Publications : Contributions to journals like Bioinformatics and Journal of Computational Biology
Markus Schütz is a Researcher at the Department of Computer Graphics, Faculty of Informatics, TU Wien. He holds a Dipl.-Ing. Dr.techn. and BSc. His work focuses on real-time rendering of massive point clouds, GPU acceleration, and interactive visualization. Key projects include 'Bringing Point Clouds to WebGPU' and 'Instant Visualization and Interaction for Large Point Clouds'. He has developed the Potree library for web-based point cloud visualization. Education: Bachelor of Science (BSc) Diplom-Ingenieur (Dipl.-Ing.) Doctor of Technical Sciences (Dr.techn.) from TU Wien Research Interests: His research emphasizes real-time rendering techniques for large-scale point clouds, GPU optimization, and efficient data processing. He explores areas such as level-of-detail generation, compute shader utilization, and web-based visualization tools like Potree. Recent work includes software rasterization of 2 billion points and simultaneous LOD generation for point clouds. Awards: Best Paper Award at EGPGV2024 High-Performance Graphics 2022 Best Paper Award Second Place in SIGGRAPH Poster Student Research Competition (2018) AGEO AWARD 2017 Projects & Grants: Bringing Point Clouds to WebGPU (2024–2025, netidee Foundation) Instant Visualization and Interaction for Large Point Clouds (2023–2026, WWTF) IVILPC (Interactive Visualization of Large Point Clouds) project Labs & Teams: Active in TU Wien's Computer Graphics Group, collaborating on GPU-accelerated rendering and real-time visualization systems.
Katharina Krösl is a Lecturer at Vienna University of Technology, affiliated with the Institute of Computer Graphics and Algorithms within the Rendering and Modeling workgroup. Her research bridges computer graphics and assistive technologies through immersive virtual reality (VR) and augmented reality (AR) applications. PhD in Computer Science (2016-2020), supervised by Michael Wimmer Master's thesis on interactive photon tracing for lighting design Her work focuses on simulating vision impairments (e.g., cataracts), developing real-time rendering techniques, and creating VR-based training systems for disaster management and attention disorders. Publications span path tracing optimization, XR accessibility, and perceptual modeling. Scientific recognitions include the Young Experts Award 2021 and IEEE VR 2020 Best Research Demo Award. She contributes to IEEE and ACM publications while integrating luminaire design with VR/AR workflows.
Stefan Bruckner is Professor of Visualization at the University of Bergen, specializing in biomedical visualization, volume rendering, and visual data exploration. His work develops novel techniques for analyzing complex scientific datasets across meteorology, medicine, and materials science. Dr. Bruckner's research group develops interactive visual analytics tools for weather forecasting, medical diagnostics, and ensemble data analysis. His methodological innovations include GPU-accelerated rendering, visual parameter exploration, and uncertainty visualization. He received the 2011 Eurographics Young Researcher Award for contributions to illustrative visualization. Professional service includes program committee roles for IEEE VIS, Eurographics, and ECRTS conferences. His pedagogical contributions span visualization, computer graphics, and programming languages at institutions including École normale supérieure and École polytechnique.
Annalena Ulschmid is a PreDoc Researcher at the Vienna University of Technology , affiliated with the Faculty of Informatics and the Computer Graphics group. She holds an MSc and contributes to research in physically-based rendering , user studies , and real-time graphics algorithms . Research spans incremental path tracing , alternative rendering datastructures , and probabilistic models for participating media . Key projects include ACD (Automated Prioritization for Context-Aware Re-rendering) (2020–2028) and IVILPC (Single-Exemplar Lighting Style Transfer) (2023–2026). She teaches courses like Computer Graphics , Fundamentals of Computer Graphics , and Rendering . Publications focus on real-time rendering , VR accessibility , and lighting style transfer . Her work has been recognized with the Best Student Paper Award at VISIGRAPP 2025 . Notable collaborations include Michael Wimmer and Katharina Krösl . Further details are available via her ORCID profile and GitHub repository .
Jelena Pesic is a Research Associate Professor at the Institute of Physics Belgrade and a PostDoctoral Researcher at Montanuniversität Leoben. She holds a Ph.D. in Solid State Physics and Statistical Physics from the University of Belgrade, where her thesis focused on superconductivity in graphene and related materials using ab-initio methods. Her research spans 2D materials, low-dimensional systems, and advanced computational methods in solid state physics, with a particular emphasis on strain-driven effects and electron-phonon interactions. Education : Bachelor and Master in Theoretical and Experimental Physics (University of Belgrade, 2013), Ph.D. in Solid State and Statistical Physics (University of Belgrade, 2017). Her work involves national and international projects, including collaborations with institutions in Austria, Slovenia, China, and Germany. She has contributed to research on perovskites, iron chalcogenide superconductors, and 2D heterostructures, leveraging GPU programming and high-throughput computational techniques. She actively reviews for journals like Zeitschrift für Naturforschung A and has organized conferences such as the 21st Symposium on Condensed Matter Physics. Notable projects include the JESH grant from the Austrian Academy of Science and multilateral initiatives in the Danube Region. Her skills integrate computational modeling, material synthesis, and characterization of 2D systems.
Michele Collevati is a PreDoc Researcher at TU Wien's Faculty of Informatics, affiliated with the Institute of Logic and Computation. He teaches courses such as 'Introduction to Knowledge-based Systems' and 'Introduction to Artificial Intelligence.' His research focuses on neurosymbolic AI, SAT solving, and GPU parallelism. He contributes to the LCS project (2017–2025). His work bridges symbolic AI with neural networks and computational optimization. Education: Not explicitly stated in provided texts. Research interests include neurosymbolic integration, knowledge representation, and parallel computing for AI. His 2024 work on slice discovery via neurosymbolic AI exemplifies his focus on hybrid systems. Earlier work explored GPU-based optimizations for SAT solvers, reflecting an interest in computational efficiency. No scientific awards are mentioned. No advised students listed. Labs/Teams: Likely involved with the Institute of Logic and Computation's research groups, though specific lab names are not provided.
Luca Di Stefano is a Post-Doc Researcher at Technische Universität Wien (TU Wien) since March 2024. His research focuses on the specification and verification of complex collective systems like multi-agent systems and robot swarms, using formal methods such as model checking and reactive synthesis. He works on online formal techniques including runtime monitoring. Research Interests: Software verification Model checking Multi-agent systems Formal semantics Process calculi Reactive synthesis Static analysis Recent Article Trends: His work spans formal verification of reconfigurable systems, emergent behavior in collective systems, and synthesis techniques for infinite-state models. Keywords include Agent-Based Modelling, Formal Methods, Temporal Logic, and Runtime Monitoring. Thesis Supervision: He supervises BSc and MSc theses at TU Wien, with Ezio Bartocci as main advisor. Examples include "Type checking a novel language for reconfigurable multi-agent systems" (Benjamin Stolz, 2025) and "Evaluating in-memory caching strategies" (Love Lyckaro, 2023). Teaching: He teaches courses like "Scientific Research and Writing" and "GPU Architectures and Computing" at TU Wien, and has served as teaching assistant for concurrent programming courses at University of Gothenburg and Chalmers. Labs & Projects: He contributes to tools like LAbS (attribute-based stigmergy language), SLiVER (verification tool), R-CHECK (model checking for reconfigurable systems), sweap (symbolic reactive synthesis), and pyxmv (Python interface for nuXmv).
Christoph Heinzl is Privatdozent at Technische Universität Wien, Faculty of Informatics, Institute of Visual Computing & Human-Centered Technology, and an associated researcher at University of Applied Sciences Upper Austria, Wels. His work bridges scientific visualization and industrial X-ray computed tomography, with applications in non-destructive testing and metrology. Education & qualifications: Heinzl holds a Dipl.-Ing.(FH) degree and a doctorate (Dr.) from TU Wien; in 2021 he completed his professorial dissertation (Habilitation) on visualization and analysis of XCT data. Research interests revolve around interactive visual analysis of high-dimensional and multimodal industrial data, uncertainty visualization, metrological evaluation of CT data, and development of immersive analytics workspaces for materials characterization. Across 60+ peer-reviewed publications since 2006 he has advanced techniques for porosity quantification, defect tracking, metal-artefact reduction, and comparative visualization of 3-D volumes, often in close cooperation with the Austrian COMET centre “EXCELLENCE IN COMET” and the K-project “ADVANCED METROLOGY”. Awards & recognition: While no major personal prizes are listed, Heinzl has served as paper co-chair and editorial guest editor for leading visualization venues, indicating peer recognition. Teaching & supervision: He regularly offers bachelor thesis topics such as “ImNDT: Immersive Workspace for Analysis of Multidimensional NDT Data” and teaches courses on visualization and computer graphics; specific PhD students are not named in the supplied sources. Funding & labs: Research is embedded in the university’s Visual Computing research area and supported by national competence centres for metrology, providing access to state-of-the-art XCT facilities and VR/AR laboratories.
Peter Kán serves as a Senior Scientist at TU Wien's Faculty of Informatics, Institute of Visual Computing and Human-Centered Technology. He manages the Mixed Reality Laboratory and teaches multiple courses including Mixed Reality Lab (193.169), PhD Seminar (193.083), and various Project courses in Computer Science and Visual Computing. PhD in Computer Science from TU Wien Research on High-Quality Real-Time Global Illumination in Augmented Reality His research spans photorealistic rendering, augmented and virtual reality systems, automatic 3D content generation, and embodied conversational agents. Current projects include RE:STOCK INDUSTRY (2024–2027), Circular Twin (2022–2025), and VR Tennis Trainer (2020–2022), funded by FFG and WWTF. His work integrates deep learning with real-time rendering for applications in industrial design, sports training, and accessibility solutions for deaf and hard-of-hearing users. Recent publications demonstrate strong focus on procedural building design, motion analysis, and accessibility interfaces. Key trends include embodied conversational agents with situation awareness, multi-objective optimization for industrial buildings, and haptic feedback systems. His research bridges computer graphics, human-computer interaction, and practical applications in architecture and healthcare. Peter Kán has received research funding from Austrian Research Promotion Agency (FFG) and Vienna Science and Technology Fund (WWTF) for projects including Conversational Agents (2023–2024) and Realistic Indoor Path Visualization (2016–2018). Supervised 10 theses including Tennis Motion Learning in VR (Sebernegg, 2025) and Embodied Conversational Agents (Rumpelnik, 2023) Manages Mixed Reality Laboratory focusing on photorealistic AR/VR systems Leads research teams on projects like Circular Twin and RE:STOCK INDUSTRY
Yuri Matiyasevich is a Russian mathematician and computer scientist affiliated with the Steklov Institute of Mathematics (Leningrad/St.Petersburg Branch) since 1980, where he serves as head of the Laboratory of Mathematical Logic. He also holds part-time professorships at Leningrad/St.Petersburg State University (since 2003) and previously at Polytechnical Institute (1980-1981). His research focuses on Diophantine equations, Computability theory, Algorithms, and Decidability problems. Full member, Russian Academy of Sciences (2008) Corresponding member, Bavarian Academy of Sciences (2007) Docteur Honoris Causa, Université Pierre et Marie Curie (2003) Humboldt Research Award (1997) Markov Prize, Academy of Sciences of the USSR (1980) His recent work involves computational approaches to Riemann's zeta function, probabilistic reformulations of graph theory problems, and algorithmic analysis of Diophantine representations. He has published extensively on connections between number theory and computational models, including studies on exponential Diophantine equations and their applications. Notable contributions include definitive solutions to Hilbert's Tenth Problem through Diophantine representations of recursively enumerable sets, and computational experiments supporting the Riemann Hypothesis. His publications span multiple languages and cover intersections between mathematical logic, number theory, and theoretical computer science.
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.
Thomas Auzinger is a Research Fellow and head of the AutoMold spin-off project at the Institute of Science and Technology Austria (ISTA). He previously held a postdoctoral position in Computational Fabrication under Prof. Bernd Bickel at ISTA. His educational background includes B.Sc. and M.Sc. degrees in Mathematical Physics from the University of Vienna and a PhD in Computer Graphics from TU Wien. He also completed entrepreneurial training through the I.E.C.T. Summer School and the Ludwig Boltzmann Society's Innovator’s Road Program. His research focuses on computational design for fabrication, particularly in mechanical and optical applications, as well as visual computing topics like anti-aliasing and medical visualization. He leads business development for AutoMold, aiming to commercialize automated mold design in industrial tooling. He has published in top-tier journals and contributed to conferences as a Program Committee Member (Pacific Graphics 2016, Eurographics 2020 Short Paper track) and co-organizer (Symposium on Computational Fabrication 2016). Auzinger has taught courses on rendering, visualization, and GPU programming at TU Wien and ISTA, and delivered invited courses on computational fabrication internationally. His work bridges academic research and industry application, emphasizing innovation in fabrication and visual computing.
Daniel Cornel is a researcher affiliated with TU Wien's Department of Engineering Hydrology, part of the E222-02 research group. He holds a Dipl.-Ing. (engineering diploma) and a Dr.techn. (technical doctorate). His work focuses on integrating computational methods with environmental hydrology, emphasizing flood modeling, high-performance computing, and geospatial visualization. Cornel collaborates extensively with experts like Prof. Günter Blöschl and Prof. Jürgen Waser on projects such as the HORA 3.0 flood risk zoning initiative. His research interests span GPU-accelerated flood simulations, hydrodynamic modeling at regional scales, and decision-support systems for disaster management. Notable contributions include developing interactive visualization tools for real-time flood data and VR-based training modules for emergency response. Cornel also explores algorithm optimization for terrain modeling and subsurface network visualization. Key projects include the 'Integrated Simulation and Visualization for Flood Management' framework and the 'Master of Disaster' VR training system. His work bridges computational science with environmental engineering, addressing both technical challenges and practical applications in flood risk mitigation.
