Dietmar Schreiner is a Senior Lecturer at TU Wien's Faculty of Informatics within the Department of Compilers and Languages. His research focuses on compiler optimization, real-time systems, robotics software engineering, and component-based automotive software. He has contributed to major projects like C3Pro (2010–2015) and ALL-TIMES (2007–2010), addressing challenges in embedded systems and automotive software. He advises students on robotics and embedded systems projects, including thesis works like 'Robotersteuerung mittels Genetic Programming für die RoboCup Standard Platform League' (2011) and 'Reasoning capabilities for a cognitive-assistive assembly system' (2017). He is actively involved in the Austrian Kangaroos robotics team and has received the Best Poster Award at SAC 2007 for work on UML-based component modeling. His teaching responsibilities include courses such as 'Introduction to Programming 1', 'Programming Principles of Mobile Robotics', and 'Fundamentals of Digital Systems'. He has also contributed to EU and national grants, advancing research in timing analysis and automotive system software.
Johann Blieberger is an Associate Professor and Head of the Institute of Computer Engineering at Technische Universität Wien. He also leads the Automation Systems Research Unit (E191-03) and serves as a Principal Member of the Faculty Council. His work focuses on real-time systems, concurrent programming, railway operation optimization using Kronecker Algebra, static analysis, and Ada programming. Key projects include developing simulation tools for sustainable public transport (2021–2023), coordinating autonomous vehicles in pedestrian environments (2017–2019), and optimizing railway systems via Kronecker Algebra (e.g., the Zagreb-Rijeka line). He has secured grants from the Austrian Research Promotion Agency (FFG) and Austrian Science Fund (FWF). Blieberger’s research interests span weak memory models, symbolic evaluation, and algorithm analysis. He actively contributes to the ISO JTC1/SC22/WG9 committee for Ada language development. His advising spans multiple PhD and master’s theses, including work on concurrent program verification and Kronecker Algebra applications.
Ingo Feinerer serves as an Associate Professor at Vienna University of Technology's Faculty of Informatics, specifically within the Institute of Information Systems Engineering (E192-02). His research bridges theoretical computer science and practical applications in database systems, artificial intelligence, and text mining, with significant contributions to configuration management and formal methods. His educational background includes: PhD Dissertation (2007): A formal treatment of UML class diagrams as an efficient method for configuration management Diploma Thesis (2005): Formal program verification: a comparison of selected tools and their theoretical foundations Feinerer's research focuses on database theory (particularly schema mapping and dependencies), AI-driven configuration systems using integer linear programming, and text mining infrastructure development in R. His work combines theoretical rigor with practical tool development, notably through the textcat and tm packages. The integration of formal methods in software engineering remains a consistent thread throughout his publications. Analysis of his 15 most recent publications reveals strong specialization in database constraints (40%), text mining applications (30%), and software configuration systems (30%), with increasing interdisciplinary work connecting computer science to digital humanities. His notable recognition includes: INiTS Award (2007) for technology commercialization potential Feinerer has supervised doctoral research including F. I. Chertes' work on schema mapping languages and diploma theses on UML semantics and probabilistic databases. He led major research projects HINT (2012-2017) and SEE (2012-2016) focusing on database technologies and information systems. His work demonstrates consistent funding through Austrian national research programs. As a core member of the Databases and Artificial Intelligence research group, he collaborates extensively on R package development for text analysis and contributes to international workshops on theoretical aspects of software engineering.
Tobias Geibinger is a PreDoc Researcher at the Institute of Logic and Computation of Vienna University of Technology , supported by a DOC Fellowship from the Austrian Academy of Sciences. His work focuses on explainability in Answer-Set Programming (ASP), particularly for advanced language features and hybrid ASP systems. Education: BSc, Dipl.-Ing. (Master's equivalent) in Computer Science Projects: CD Laboratory for AI and Optimization (2017–2025), KIRAS-PrEMI (2019–2022), ARTIS (2017–2025) His research combines logic programming with constraint programming and hybrid methods to solve industrial scheduling problems. This includes automated test laboratory scheduling, photolithography job scheduling, and pandemic-era physician scheduling. Recent work investigates neurosymbolic integration and contrastive explanation frameworks. Selected publications analyze ASP optimization techniques for parallel machine scheduling, large-neighborhood search strategies, and nonmonotonic paraconsistent logics. These contributions align with broader trends in declarative AI and logic-based optimization. Scientific Recognition: ASAI Master Thesis Prize (2023)
Bernhard Kerbl is a Post-doctoral Researcher at the Computer Graphics Group (E193-02) within the Faculty of Informatics at Vienna University of Technology (TU Wien). He actively contributes to research in real-time rendering, GPU optimization, and 3D visualization. His work focuses on point cloud rendering, Gaussian splatting, and low-level graphics API development (particularly Vulkan). Core Research Areas: Real-time rendering, GPU programming, point cloud processing, 3D Gaussian splatting, task-based parallelism Projects: IVILPC (2023–2026), ACD (2020–2028), EVOCATION (2018–2022) Key Contributions: Developed real-time rendering techniques for massive datasets, pioneered GPU-based scene streaming architectures, and created novel methods for visual error prediction using machine learning. His work on Vulkan transition in academia received recognition, and he maintains active collaborations in GPU education. Scientific Achievements: Best Paper Award at EGPGV 2024 for "Fast Rendering of Parametric Objects on Modern GPUs" Recipient of Vienna Science and Technology Fund (WWTF) support for graphics research Technical Expertise: Specializes in CUDA, OpenGL/Vulkan APIs, and real-time systems. His work bridges academic research with practical implementations in virtual reality, augmented reality, and GPU education.
Berry Gérard is a distinguished Professor at Collège de France, holding the Chair in Algorithms, Machines, and Languages since 2012. He previously served as Director of Research at INRIA Sophia Antipolis and Chief Scientist at Esterel Technologies. His academic journey includes roles at École des Mines de Paris and École Polytechnique. Gérard specializes in models of computation, programming language design, and synchronous systems. He has contributed significantly to the development of the Esterel language for embedded systems and formal verification techniques. Education: Docteur d’Etat in Mathematics, Université Paris VII (Computer Science option), 1979 Ingénieur des Mines, Corps National des Ingénieurs des Mines, 1970 École Polytechnique, 1967 Research Interests: Focuses on computational models (e.g., lambda-calculus, synchronous concurrency), programming languages (Esterel, Hop/HipHop), circuit synthesis, and formal verification. His recent work explores diffuse programming and web orchestration. Key Awards: 2014: Médaille d'or du CNRS 2005: Member, Académie des technologies 1993: Member, Academia Europaea 1979: Bronze Medal of CNRS Professional Roles: President of the Council of Education and Research at École Polytechnique Member of the Scientific Council of IRCAM Former President of the INRIA Evaluation Committee Labs/Teams: Active in INRIA’s Indes project on diffuse programming and collaborates with IRCAM on real-time music systems.
Professor Eric Westhof at the University of Strasbourg is a leading expert in nucleic acid stereochemistry, RNA structural biophysics, and bioinformatics. He has developed critical computational tools for RNA modeling and crystallographic refinement, including pioneering all-atom tertiary models of catalytic RNAs validated by experimental studies. Positions: Director of CNRS Unit 'Architecture and Reactivity of RNA', Director of CNRS Institute of Molecular and Cellular Biology (IBMC), Vice-President for Research at University of Strasbourg Editorial Roles: Executive Editor of RNA and Nucleic Acids Research His research focuses on RNA sequence-structure evolution and functional dynamics. Key contributions include geometric classifications for RNA base pairs and molecular modeling of ribozymes. Scientific honors include the Jacques MONOD Prize, Structural Biochemistry Chair from Institut Universitaire de France, Fellowships from AAAS and Institute of Physics, and memberships in EMBO, Academia Europaea, and multiple national academies. Grants: Human Frontier Science Program (1997-2000, 2005-2008), European Union projects Leadership: President of RNA Society (2005), President of Société française de Biochimie (2004-2009), EMBO Publications Committee Chair
Markus Kuba is a Professor (FH-Professor) at FH-Technikum Wien in Vienna, Austria, where he leads the Department of Applied Mathematics and Physics. He holds a habilitation (Privatdozent) from TU Wien's Faculty of Mathematics and Geoinformation, granting him the venia docendi. He is also a teacher at HTL-Spengergasse, a higher technical institute, specializing in mathematics and computer science. His academic journey includes a PhD in Mathematics from TU Wien (2006) and a habilitation in 2013. His research focuses on theoretical computer science, analysis of algorithms, analytic combinatorics, and random structures. Notable specializations include tree structures, urn models, multiple zeta values, and SAT-problems. His work bridges combinatorial theory with practical applications in network analysis and stochastic processes. Key contributions include studies on urn models, tree growth models, and interdisciplinary projects like integrating traffic simulation tools. His publications span prestigious journals such as Theoretical Computer Science , Combinatorics, Probability and Computing , and Journal of Combinatorial Theory . Teaching spans secondary schools and universities of applied sciences since 2009, with a focus on mathematics and programming. Collaborations include co-authors from institutions worldwide, reflecting his active role in international academic networks.
Ivona Brandić is a University Professor for High Performance Computing Systems at TU Wien's Institute of Software Engineering and Interactive Systems. Born in Gradačac, Bosnia and Herzegovina, she moved to Austria in 1992 as a refugee during the Bosnian War. She earned a master's degree (2002) and doctorate (2007) in business computer science from TU Wien and completed her habilitation in applied computer science there in 2013. Her career includes roles as an assistant professor (University of Vienna, 2002–2007) and postdoctoral researcher (University of Melbourne, 2008). She transitioned to a tenure-track position at TU Wien in 2014 and became a full professor in 2016. Brandić’s research focuses on cloud computing, energy-efficient ultra-scale systems, and hybrid quantum-classical computing. She has been recognized with the MiA Award (2011), the Austrian Science Fund's Start-Preis (2015), and membership in the Austrian Academy of Sciences' Young Academy (2016). Her work emphasizes sustainable computing, edge systems, and optimizing resource management for distributed applications. Education: Bachelor's degree in Business Informatics (University of Vienna/TU Wien) Master's in Business Computer Science (University of Vienna, 2002) PhD in Applied Computer Science (TU Wien, 2007) Habilitation in Practical Computer Science (TU Wien, 2013) Research Interests: Brandić’s work spans cloud computing, energy efficiency in HPC systems, edge computing, and quantum-classical hybrid systems. She explores autonomic resource management, distributed system resilience, and sustainability in ultra-scale infrastructures. Her projects often address real-world applications like drug design, environmental monitoring, and smart energy grids. Publications: Her 2009 paper Cloud Computing and Emerging IT Platforms is a seminal work in the field. Recent publications focus on quantum-edge integration, energy optimization in AI models, and adaptive edge analytics frameworks. These contributions highlight trends toward sustainable, distributed, and hybrid computational paradigms. Awards: 2011: MiA Award for distinguished contributions by international backgrounds 2015: Austrian Science Fund’s Start Prize 2016: Austrian Academy of Sciences Young Academy Membership Advising & Grants: Brandić leads research groups and has secured grants for projects like NESSUS (energy-efficient cloud systems) and CHIST-ERA’s SDCDN (distributed networks). She mentors students in HPC, edge computing, and quantum systems. Advised topics include workload scheduling, fault tolerance, and energy-aware algorithms. Labs & Teams: She directs research on autonomic cloud management, edge intelligence frameworks (e.g., Sea-LEAP, FRESCO), and quantum-classical workflow systems (RIGOLETTO). Her teams collaborate internationally, integrating academia and industry for scalable, sustainable solutions.
Hubert Missbauer is a Full Professor for Production and Logistics Management at the University of Innsbruck. He holds a Diploma (1982) and Doctorate (1986) in Business Administration from the University of Linz, with a Habilitation in Business Administration (1994) focusing on manufacturing planning systems. His research emphasizes production planning concepts, workload control, and optimization in manufacturing systems, particularly in steel production. He co-organizes the International Working Seminar on Production Economics and serves on the editorial board of the International Journal of Production Economics . Research interests include order release optimization, production scheduling in steel industries, and quantitative methods in operations management. Recent work focuses on Lagrangian decomposition algorithms, behavioral perspectives in workload control, and integrated scheduling in steel production processes. He has published extensively in top-tier journals like International Journal of Production Research and European Journal of Operational Research . Education: University of Linz (Diploma 1982, PhD 1986, Habilitation 1994) Affiliations: Institute for Information Systems, Production and Logistics Management at University of Innsbruck Key Projects: Steel production scheduling, iterative LP-simulation algorithms, behavioral studies in manufacturing control Editorial Roles: Guest Editor for International Journal of Production Economics since 2020 His work bridges theoretical models (e.g., clearing functions, transient analysis) with practical applications in industries like steelmaking and construction. Recent presentations include discussions on Lagrangian approaches at the INFORMS Annual Meeting (2024) and EURO conferences.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology Kanpur (IITK) since 2022. He previously held positions at Los Alamos National Laboratory as a Postdoctoral Researcher (2018-2019) and Scientist II (2019-2022). Dr. Dutta earned his Ph.D. and M.S. in Computer Science from The Ohio State University (2011-2018) and a B.Tech in Electronics and Communication Engineering from the West Bengal University of Technology (2005-2009). His research lies at the intersection of Machine Learning , Visual Computing , Big Data Analytics , and High-Performance Computing (HPC) . He focuses on developing scalable solutions for extreme-scale data, such as exascale simulations, social media, IoT, and healthcare. His work emphasizes uncertainty quantification in AI models and interactive visualization techniques. Dr. Dutta’s recent publications highlight his expertise in in situ visualization for climate modeling, implicit neural representations for uncertainty-aware rendering, and statistical sampling for exascale systems. His funded projects include AI-driven data analytics frameworks and deepfake defense mechanisms supported by ISRO, SERB, and C3iHub. Scientific Awards include Best Reviewer (TVCG), Best Paper (ISAV, TopoInVis), and LAAP Award (LANL).
Thomas Eiter is a Full Professor at the Vienna University of Technology (TU Wien) in the Department of Knowledge-Based Systems, Faculty of Informatics. His research focuses on artificial intelligence, knowledge representation and reasoning, logic programming, computational logic, and neurosymbolic AI integration. He leads projects in declarative problem-solving, intelligent agent systems, and stream reasoning frameworks like LARS. Eiter has contributed to foundational work in answer set programming (ASP), algebraic reasoning, and their applications in scheduling, robotics, and real-time data processing. He has extensive international collaborations, including EU-funded projects like HumanE-AI-Net and the Austrian Science Fund (FWF) initiatives. His work emphasizes bridging symbolic AI with modern machine learning techniques, particularly in visual question answering and neural-symbolic systems. Eiter has supervised numerous PhD students and maintains active roles in academic leadership, including editorial boards of journals like Theory and Practice of Logic Programming . Key contributions include development of the DLVHEX system for hybrid knowledge representation, optimization frameworks for ASP, and methodologies for stream reasoning in dynamic environments. His research also addresses ethical AI through projects like the TAIGER initiative, focusing on training AI agents with ethical rules.
Siegfried Benkner is a full Professor at the Vienna University of Technology (TU Wien) within the Faculty of Computer Science and leads the Research Group for Scientific Computing. His work focuses on high-performance computing (HPC), parallel programming models, runtime systems, and performance optimization for heterogeneous architectures. He has actively contributed to EU-funded projects such as TROCI (2024–2027) and PEPPHER, addressing resilience in critical infrastructures and programmability for exascale systems. His research spans topics like task-based runtime systems (OCR-Vx), autotuning frameworks (Periscope PTF), and performance portability for GPUs/Xeon Phi architectures. Recent interests include accelerating graph neural networks via novel matrix compression formats and cloud-edge continuum systems for eHealth applications. Prof. Benkner has published over 270 articles, with a focus on runtime systems, parallel patterns, and HPC infrastructure. His work emphasizes practical applications, including semantic data management for medical research and cloud-based analytics frameworks for big data processing in cellular networks. He has led multiple EU projects (9 total), including the 2024 initiative on exascale computing and resilience, and frequently presents at conferences like Euro-Par and Supercomputing events. His activities include media engagement on topics like exascale hardware trends and HPC challenges.
Tom van Dijk serves as an Assistant Professor in the Formal Methods and Tools group at the University of Twente, where he conducts cutting-edge research in formal verification and develops practical software tools for the verification community. His work bridges theoretical computer science with real-world applications in model checking and synthesis. His educational background includes: PhD in Computer Science (2016), University of Twente. Thesis: Sylvan: Multi-core Decision Diagrams MSc in Computer Science (2012, cum laude), University of Twente. Thesis: The parallelization of binary decision diagram operations for model checking BSc in Computer Science (2010), University of Twente. Thesis: Analysing And Improving Hash Table Performance Van Dijk's research centers on formal verification with specialization in parity games and binary decision diagrams . He pioneers multi-core parallel algorithms for symbolic model checking, focusing on practical implementations that scale to industrial problems. His work integrates SAT/SMT solving , reactive synthesis , and algorithm optimization to advance the state-of-the-art in verification tooling. His publication trajectory reveals consistent innovation in parity game solving, evolving from foundational work on tangle-based algorithms to recent contributions in reproducibility and AI-aided verification. The 2024 papers demonstrate expanding scope into educational technology while maintaining core focus on game-solving efficiency and synthesis techniques. His key recognitions include: Dutch national M&I Informatie Scriptieprijs 2012 (2nd place) for MSc thesis Best paper award at SPIN 2017 for distributed BDD research Van Dijk actively mentors students and seeks collaborations: Student Supervision : Welcomes BSc/MSc students for projects on parity games and BDDs Tool Development : Maintains open-source research tools (Sylvan, Oink, Knor) Community Service : Serves on 20+ program committees including CAV and TACAS As core member of the Formal Methods and Tools group, he contributes to major verification frameworks including LTSmin, Storm, and IscasMC. His Lace work-stealing framework underpins parallelization in multiple verification tools, while ongoing projects focus on polynomial-time parity game solutions and AI integration in verification workflows.
Bogdan Burlacu serves as R&D-Headquarters at the Center of Excellence for Smart Production HEAL at University of Applied Sciences Hagenberg. With an ORCID identifier 0000-0001-8785-2959 and h-index of 10 (619 citations), he maintains active research leadership through 2025. His research focuses on Symbolic Regression and Genetic Programming, with significant contributions to Multiobjective Optimization and Benchmark Problems. Key application areas include Explainable AI systems, hardware acceleration for evolutionary algorithms, and astrophysical modeling. His work demonstrates strong interdisciplinary connections between computer science and physical sciences. Recent publication trends show increasing focus on interpretability frameworks and domain-expert validation in symbolic regression, with notable applications in cosmology and engineering systems. His 2025 publications emphasize practical benchmarking methodologies and hardware acceleration techniques. Burlacu actively supervises research through two documented supervised works and contributes to major collaborative projects. He leads research activities within the Center of Excellence for Smart Production HEAL and participates in the Josef Ressel Center for Symbolic Regression. His work integrates distributed intelligence systems with rapid prototyping methodologies for industrial applications.
