Vicente Javier Pérez Valero serves as a Visiting Erasmus Professor in the Department of Drawing at the Faculty of Fine Arts, Miguel Hernández University of Elche (Altea campus). He leads initiatives integrating digital tools like vector software and graphic paddles into Morphological Drawing curriculum for second-year Fine Arts students under the European Higher Education Area Space (EHEA) framework. His research pioneers the convergence of traditional line drawing with vectorial techniques, arguing computers have evolved from final-art tools to equals of traditional media. This philosophy fosters boundary-free creation where 'nothing is impossible,' directly enhancing professional preparation for illustration and graphic design careers through hands-on technology implementation programs. No scientific awards or student advising roles were documented in the source material.
Regina Maria Veronika Schuster is a researcher affiliated with the Faculty of Computer Science in the Research Group Visualization and Data Analysis . Her work contributes to the UN Sustainable Development Goals through interdisciplinary research in data visualization and science communication. Research Interests Scientific Visualization Climate Change Communication Data Journalism Science Communication Visual Analytics Her research focuses on visual data communication, particularly in popular science contexts like climate change and public health, utilizing insights from semistructured interviews and expert opinions to enhance sustainability messaging. Publications Recent work (2023–2024) emphasizes data visualization strategies for climate change and COVID-19 in periodicals such as Harvard Data Science Review and arXiv , addressing challenges in conveying complex scientific information to broader audiences. Labs & Collaborations Active in the Visualization and Data Analysis Research Group , she collaborates with international researchers on topics like climate communication and digital media, with recent contributions to open-access publications .
Jiri Filipovic is a researcher affiliated with the Faculty of Computer Science, active in the Research Group Scientific Computing. His work focuses on optimizing computational performance through dynamic autotuning techniques in heterogeneous computing environments. Primary Fields: OpenCL, Autotuning, Dynamic Autotuning Secondary Areas: Graphics Processing Unit (GPU) Computing, Performance Portability, Energy Efficiency In 2020, Filipovic co-authored a significant article in Future Generation Computer Systems presenting a benchmark suite for CUDA/OpenCL kernels with dynamic autotuning capabilities. This work reflects his emphasis on maximizing computational efficiency across diverse hardware architectures. The PlumX Metrics indicate 25 citations, 16 Mendeley readers, and 47 social media shares, highlighting the visibility of his research in high-performance computing communities. No specific scientific awards or formal student advisement activities are documented in the available materials.
Franz Aurenhammer is a University Professor (Univ.-Prof.) at the Institute of Machine Learning and Neural Computation, Graz University of Technology, Austria. He holds the academic title DI Dr. techn. and has been active in computational geometry research for several decades. His position as Full Professor was appointed in October 1992 at the Institute of Theoretical Computer Science, where he also served as head of the research group on algorithms, geometry, and optimization. Professor Aurenhammer earned his academic credentials at Graz University of Technology: his MS degree (Dipl. Ing.) in Technical Mathematics in April 1982, his PhD degree (Dr. techn.) in November 1984, and completed his Habilitation (Universitätsdozent) in Theoretical Computer Science in May 1989. Prior to his current position, he served as Assistant Professor at the Institute for Information Processing from January 1985 to April 1989, and held research positions including at the Free University of Berlin (April 1990 to May 1992). Aurenhammer's research focuses primarily on computational and combinatorial geometry, data structures and algorithms, and graph algorithms. His work has particularly emphasized Voronoi diagrams and straight skeletons, with numerous publications on these topics spanning several decades. His research has strong theoretical foundations while also addressing practical applications in computer science, optimization, and geometric modeling. Analysis of his recent publications reveals a continued focus on geometric structures, particularly Voronoi diagrams in various forms (including piecewise-linear farthest-site variants) and straight skeletons in both 2D and 3D contexts. His work often bridges theoretical computational geometry with practical applications in computer-aided design, shape analysis, and spatial data structures. The research demonstrates progression from fundamental theoretical work to increasingly sophisticated applications in 3D modeling and complex geometric structures. Professor Aurenhammer has been actively involved in research funding, with grants from major institutions including the Austrian Ministry of Science (BMWFK), Austrian National Bank (ÖNB), National Science Foundation (FWF), Austrian Academic Exchange Program (ÖAD), and the Special Research Council (SFB) 'Optimization and Control'. His current project FWF I1836-N15 (2015-2020) focuses on Voronoi diagrams as versatile data structures for spatial proximity problems. As an educator, Aurenhammer has supervised numerous MS and PhD theses in theoretical computer science and taught courses including Basic Data Structures & Algorithms, Languages and Automata, Design & Analysis of Algorithms, Computational Geometry, and Information Theory. His teaching responsibilities include Privatissimum courses on Algorithms and Geometry and Dissertation seminars. His international research collaborations span numerous institutions across Europe, the United States, Canada, Japan, Taiwan, Korea, and China, reflecting the global significance of his work in computational geometry. These collaborations have resulted in significant contributions to the field, particularly through the DACH project on Voronoi diagrams and related geometric structures.
Dieter Fellner is a Professor of Computer Science at Technical University of Darmstadt, Germany , and Director of the Fraunhofer Institute of Computer Graphics (IGD) . He also serves as Founding Director of the Institute of Computer Graphics and Knowledge Visualization at Graz University of Technology, Austria. His career spans multiple prestigious institutions including University of Bonn, Memorial University of Newfoundland, and University of Technology Braunschweig. Dr. Fellner's research focuses on algorithms integrating modeling and rendering , digital libraries , virtual/augmented reality , collision detection , and radio wave propagation simulation . His work in digital libraries led to a DFG-funded strategic initiative (1997-2005) involving 50 researchers annually across 21 groups. The 15 most recent articles highlight his expertise in 3D document modeling , stereoscopic projection , subdivision surfaces , and color quantization . His publications include influential textbooks like the German standard on computer graphics (1988) and Digitale Bibliotheken (2000). 2000 Fellow of the Eurographics Association Best Technical Paper Award (Günther Enderle Award) at Eurographics’98 2007 IST Advisory Group (ISTAG) Member for the European Commission 2019 Honorary Doctorate from University of Rostock Dr. Fellner actively participates in editorial boards of journals like Computer Graphics Forum , IEEE CG&A , and JOCCH , and serves on program committees for conferences such as Eurographics, VSMM, and ACM Web3D. His leadership roles include Chairmanship of the Eurographics Association (1999-2000) and Directorship of Fraunhofer IGD (since 2006).
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.
Dr. Shohei Mori is a Junior Research Group Leader at the Visualization Research Center (VISUS) of the University of Stuttgart, Germany, and a Guest Associate Professor at Keio University, Japan. His research focuses on computational Mediated Reality, combining Augmented and Diminished Reality to address human-centered visual computing challenges. Key applications include education, entertainment, and cinematography. He has received numerous awards, including the IEEE ISMAR 2023 Best Journal Paper Award, IEEE VR 2022 Best Journal Paper Award, and multiple presentation/demonstration awards. His work spans grants such as the FWF-funded 3DDR project and collaborations with institutions like TU Graz, NTT Laboratories, and AVL List GmbH. Mori has taught courses on topics like Neural Rendering, Mixed Reality, and Computer Vision at institutions including TU Graz and FH Salzburg. His teaching emphasizes practical assignments and student research presentation management. His research interests include 3D reconstruction, light field displays, and perceptual aspects of mixed reality. He actively contributes to conferences like IEEE VR and ISMAR as a reviewer, chair, and committee member.
Horst Possegger is a Computer Vision researcher at the Institute of Computer Graphics and Vision at Graz University of Technology, Austria. He holds a BSc and MSc in Software Development and Business Management (2011, 2013) and a PhD in Computer Science (2018), all from Graz University of Technology. His research focuses on multiple object tracking/detection, human behavior analysis, and video analysis with applications in autonomous systems, LiDAR data processing, and robotics. Key projects include trajectory prediction, 3D object detection, and domain adaptation techniques for real-world scenarios. Recent work emphasizes cross-modal learning (e.g., vision-language models for LiDAR data), robust localization in challenging environments, and lightweight motion prediction models. He contributes to the Learning, Recognition & Surveillance (LRS) group and has published extensively in top-tier venues like IEEE/CVF CVPR and IROS. Publications span autonomous driving benchmarks, UWB-based localization, and warehouse automation. His work often bridges theoretical advancements with practical implementations in robotics and surveillance systems.
David Christian Schedl is a Professor of Visual Computing at the Upper Austrian University of Applied Sciences, Campus Hagenberg. Previously, he was a post-doctoral researcher at the Institute of Computer Graphics at Johannes Kepler University Linz (until 2021), and earned his Ph.D. there in 2018. He joined the Rendering group at Vienna University of Technology in 2012 and holds a Master's degree in Interactive Media from the University of Applied Sciences in Hagenberg (2011). His research focuses on computer vision, computer graphics, and machine learning, particularly optimal sampling strategies and novel algorithms for multi-view data like light fields. He leads the BAMBI project (2022–2025), which explores biodiversity monitoring using intelligent UAV sampling. Key contributions include airborne radiance fields and wildlife accident risk modeling using Gaussian mixture models. His work intersects drone technology, synthetic aperture imaging, and ecological applications. He has supervised 3 academic works and actively participates in conferences such as EU Safety 2023 and camera trap AI workshops. His affiliations include the Research Center Hagenberg and the Digital Media Lab. With over 43 publications and an h-index of 43, his research bridges academia and applied technologies in visual computing.
Prof. Vladimir Kolmogorov is a faculty member at the Institute of Science and Technology Austria (IST Austria), specializing in discrete optimization and algorithm design. He holds a Ph.D. in Computer Science from Cornell University and has held positions at Microsoft Research and University College London. His research focuses on combinatorial optimization, MAP inference in graphical models, and applications in computer vision. Educations: M.S. in Applied Mathematics and Physics, Moscow Institute of Physics and Technology Ph.D. in Computer Science, Cornell University Research Interests: Dr. Kolmogorov's work spans algorithmic optimization, including complexity analysis of constraint satisfaction problems, graph algorithms, and machine learning applications. His contributions include foundational work on graph cuts for computer vision and the development of efficient optimization methods for discrete problems. Publications: His recent work includes advancements in parallel algorithms for Gibbs distributions, semidefinite programming, and combinatorial optimization. These contributions highlight his expertise in bridging theoretical computer science with practical applications. Awards: Royal Academy of Engineering/EPSRC Research Fellowship (2006–2011) ERC Consolidator Grant (2014–2020) Best Paper Award at ECCV 2002 Outstanding Student Paper Award (NIPS 2007) Best Paper Honorable Mention (CVPR 2005) Advising and Grants: He has advised multiple PhD students and leads a research team at IST Austria. His grants include significant funding for exploring optimization in machine learning and discrete systems. Labs/Teams: His lab focuses on theoretical and applied discrete optimization, collaborating with institutions globally. Current projects include developing faster algorithms for graph problems and advancing Gibbs distribution analysis.
Bernd Bickel is a Full Professor for Computational Design at ETH Zurich, embedded in the Design++ research center. He holds a Master's degree from ETH Zurich and a PhD from ETH Zurich under Markus Gross. Previously, he was at IST Austria (2015–2023), Disney Research, and TU Berlin as a visiting professor. His research focuses on computational design, digital fabrication, and simulation, with applications in robotics, computer vision, and material science. Key interests include physics-based simulation, geometry processing, and interdisciplinary engineering. Notable awards include the Academy of Motion Picture Technical Achievement Award (2019), SIGGRAPH's Significant New Researcher Award (2017), and the EUROGRAPHICS Best PhD Thesis (2012). He leads the Computational Design Lab at ETH Zurich and collaborates with institutions like Inria Nancy on projects such as MFX team collaborations. His work spans academic contributions (over 50 publications) and industrial applications, including the FlexMaps Pavilion (First Prize at IASS 2019) and computational tools for 3D printing. He actively mentors PhD students and oversees grants like ERC Starting Grant 'Materializable'.
Christoph H. Lampert is a Professor at the Institute of Science and Technology Austria (ISTA), leading the Machine Learning and Computer Vision Group. His research focuses on creating robust, fair, and verifiable machine learning systems with strong theoretical foundations. Academic Rank: Professor (ISTA) Research Focus: Machine Learning, Computer Vision, Robustness, Fairness, Formal Verification Editorial Roles: Action Editor (JMLR), Former Editor (IJCV), Associate Editor-in-Chief (TPAMI) His recent publications explore robust deep learning architectures, formal verification of neural networks, and fairness in multi-source learning environments. Research keywords span neural network design, algorithmic accountability, and structured data modeling. Scientific achievements include: DARPA Disruptive Ideas award (2023) ISTA Alumni Award (2023) He has mentored numerous PhD students including: Bernd Prach (2022 thesis: Robust image classification with 1-Lipschitz networks) Egor Zverev, Nikita Kalinin, Hossein (Qualifying Exam passed 2023-2025) Alex Peste (2023 thesis: Robustness and Fairness in Machine Learning) Mary Phuong (2021 thesis: Underspecification in Deep Learning) Amelie Royer (2020 thesis: Computer Vision applications) Alexander Kolesnikov (2018 thesis: Weakly-Supervised Segmentation) Alex Zimin (2018 thesis: Dependent data learning)
Victor Adriel de Jesus Oliveira is a Lecturer at the Department of Media and Digital Technologies , Institute of Creative Media and Technologies , St. Pölten University of Applied Sciences . He holds a PhD in Computer Science (2018) and MSc/BSc in Computer Science from Brazilian institutions. Research Focus: Haptics, 3D user interfaces, virtual reality, tactile communication systems, and tele-rehabilitation technologies. Projects: Involved in EU-funded initiatives like AniVision , CLEA , and IoT4LAC , focusing on smart data applications and environmental action. His recent publications emphasize multimodal visualization for facility management, sonification in rehabilitation, and data experience through comics. He contributes to IEEE and Springer conferences, bridging human-computer interaction and clinical applications .
Chris Wojtan is a Professor at the Institute of Science and Technology Austria (IST Austria), leading the Visual Computing Group. His research focuses on geometric and numerical algorithms for computer animation and geometry processing, particularly in simulating fluid dynamics, solid materials, and 3D shape manipulation. Key contributions include methods for realistic water surface animation, cloth simulation, and fracture mechanics. He has received prestigious awards such as the ERC Consolidator Grant (2022), ERC Starting Grant (2014), SIGGRAPH Significant New Researcher Award (2016), and Eurographics Young Researcher Award (2015). Education: PhD in Computer Science from Georgia Institute of Technology (2010). Research Group: Current PhD students include Georg Sperl, Peter Synak, and Sadashige Ishida. Former students and postdocs include Morten Bojsen-Hansen (now at Autodesk) and David Hahn (TU Wien). Grants: Principal Investigator for ERC grants and other funding initiatives. Scientific Awards: Highlighted awards include ERC grants, SIGGRAPH, and Eurographics recognitions. Publications span advanced fluid simulation techniques, topology optimization, and procedural materials. The lab actively recruits PhD students and postdocs, emphasizing English-language research collaboration.
Lisa Perkhofer is an Assistant Professor at the University of Applied Sciences Upper Austria, Faculty for Business and Management, with a focus on data visualization, management reporting, and accounting systems. She holds a PhD from Vienna University of Economics and Business (2021), and master's and bachelor's degrees in Controlling and Financial Management from Upper Austria University of Applied Sciences. Her research explores interactive visualizations for big data analytics, cognitive load in information design, and sustainable reporting practices. Key research projects include FinCoM (Financial Condition Monitoring), DDI (Data-Driven Insights via Dashboarding), and USIVIS (User-Centered Interactive Visualization). She has received awards such as the Joung Researcher Award (2022) and Dr. Hermann Zemlicka Award. Her teaching includes courses on Financial Engineering, Management Reporting, and Business Analytics. Education: PhD in Social and Economic Sciences, Vienna University of Economics and Business (2014–2021) M.A. in Controlling, University of Applied Sciences Upper Austria (2011–2013) B.A. in Controlling, University of Applied Sciences Upper Austria (2008–2011) Research Interests: Data visualization techniques, dashboard design, cognitive load theory, sustainability reporting, and big data applications in accounting. Her publications span peer-reviewed journals like Journal of Management Control and Clinical Chemistry and Laboratory Medicine , with a focus on visualization efficacy and decision-support systems. She has supervised numerous bachelor and master theses on topics like AI in taxation, CRM system adoption, and sustainability metrics. Lisa contributes to academic communities through conference presentations (e.g., CARF, FRAP) and co-authored textbooks such as Grundlagen der finanziellen Unternehmensführung . Her work bridges academic research and practical business challenges, emphasizing user-centered design and data-driven decision-making.
