Juan Jose Ochoa Duque serves as a Researcher in the Department of Data Science at the Faculty of Informatics, Vienna University of Technology, holding the position of PreDoc Researcher (Projektassistent) within research group E194-04 of the Institute of Information Systems Engineering. His research centers on Data Science methodologies, with current work focused on applied data engineering within academic research frameworks. As a PreDoc researcher, he operates at the intersection of doctoral training and project-based scientific inquiry. Contact: juan.duque@tuwien.ac.at .
Anton Arnold is a Professor of Mathematical Analysis at the Vienna University of Technology (TU Wien). He holds the position of Professor since 2005 and has served as Deputy Director and Head of the Institute for Analysis and Scientific Computing. His primary affiliation is with the Institute of Analysis and Scientific Computing, part of TU Wien's Faculty of Mathematics and Geoinformation. Education: Born in 1963, Arnold earned his Diploma in Technical Mathematics (1989) and Doctor of Technical Sciences (1990) from TU Wien. He completed his Habilitation in Mathematics at TU Berlin (1996). Professional roles include visiting professorships at institutions like Purdue University and Université de Provence. Research interests focus on applied mathematics, partial differential equations (PDEs), entropy methods, hypocoercivity, and quantum kinetic equations. He has led projects such as the Erasmus Mundus Master Program 'InterMaths' and the FWF-funded SFB 'Taming Complexity in Partial Differential Systems'. Teaching includes advanced courses on PDEs, variational calculus, and kinetic equations. Supervision of over 30 PhD, Master, and Bachelor students underscores his academic mentorship. His work bridges theoretical analysis with computational methods, particularly in quantum transport and wave propagation.
Prof. Michael Kohlegger is a Professor of Data Science & Engineering at MCI | Die Unternehmerische Hochschule and holds concurrent roles as Senior Lecturer in Data Science at FH Kufstein Tirol and Information Systems at WU Wien. His academic leadership includes serving as Vice-Rector of FH Kufstein Tirol (until 2024) and Director of Studies. He focuses on data storage/analysis, holistic data processing frameworks, and industry collaboration in data-driven projects. Education: Earned a Mag. (2008) and Dr. (2013) in Business Informatics from the University of Innsbruck. Prior to academia, he worked as a Business Analyst (SOS Children's Villages International) and Research Assistant (University of Innsbruck). Research emphasizes mobility systems (e.g., Vision2Move multi-object tracking, ALMODA anomaly detection) and human-technology interaction (voice assistants usability studies). Projects also include digital tourism initiatives and SME-focused data analysis models. Scientific Contributions: Co-authored works on AR work instructions, autonomous driving validation, railway monitoring systems, and process improvement frameworks. Leadership: Spearheaded projects like Digital Tourism Expert (2018–2021) and Usability of Voice Assistants (2018–2021).
Tobias Kofler is affiliated with the Management Center Innsbruck (MCI), where he contributes to teaching and research in mechanical process engineering and particle technology. He has held roles such as lecturer, tutor, and laboratory instructor, focusing on subjects like mechanical elements, process technology, and fluid mechanics. His research emphasizes hydrocyclone optimization, waste treatment systems, and energy-efficient separation technologies. Education: Matura – Höhere technische Schule für Maschinenbau (HTL Innsbruck) DI (FH) – Management Center Innsbruck (MCI) Diplomstudium für Verfahrens- und Umwelttechnik Research Interests: Kofler’s work centers on improving hydrocyclone designs for industrial and environmental applications, including wastewater treatment, biowaste management, and energy efficiency. He explores particle separation mechanisms, flow dynamics, and process optimization through experimental and computational methods. Recent studies address challenges like upcycling glass waste, optimizing screw press separators, and analyzing cyclone performance under varying conditions. Key Contributions: Developed empirical models for cyclone pressure drop and swirl optimization. Co-inventor of patented separation technologies (EP3117904, US Patent 10,668,485B2). Recipient of the MCI Competitive Research Award and merit scholarships for academic excellence. Advising & Grants: Supervised over 20 bachelor’s and master’s theses on topics ranging from hydrocyclone design to energy-efficient processes. Collaborated on funded projects involving laser anemometry, plasma actuation, and renewable energy storage systems. Labs & Teams: Active in MCI’s Fluid Systems and Particle Technology research group, contributing to initiatives like the ‘Robot-LDA’ measurement platform and mobile wastewater testing facilities.
Franz Theuretzbacher is affiliated with the Faculty of Engineering, focusing on biogas technology, renewable energy systems, and techno-economic assessments of energy infrastructure. He holds a Dipl.-Ing. and Dr. (PhD) degree. His research explores biogas plant integration into electricity markets, biomass pretreatment for enhanced biogas production, and flexible power generation scenarios. He actively participates in projects like RefurMO (2025–2026), investigating lifecycle aspects of mobile phone technology. His recent work emphasizes optimizing biogas plant operations for grid stability and cost efficiency, with contributions to energy market policies and sustainable biomass utilization. His articles analyze biogas plant integration into energy markets, pretreatment methods for reed biomass, and control energy reserves. He has supervised three academic works but specific student names are not disclosed. No scientific awards were mentioned in the provided text.
Martin Kjäer is a PreDoc Researcher at the Institute of Computer Technology within the Faculty of Informatics at Vienna University of Technology (TU Wien). His work focuses on blockchain applications in business process management, circular economy, and construction engineering through multiple Austrian Research Promotion Agency (FFG) funded projects. His educational background includes a BSc and Diploma in Engineering (Dipl.-Ing.) from TU Wien, where he completed his thesis on MakerDAO's liquidation system. Research interests span blockchain implementation trade-offs, decentralized business processes, circular economy systems, and BIM-integrated construction technologies. Analysis of his 2021-2024 publications reveals consistent blockchain innovation across domains: 40% address business process management (including choreography execution and inter-organizational trade-offs), 30% focus on circular economy and construction applications, and 30% analyze DeFi protocols like MakerDAO. Key trends include moving beyond pure blockchain solutions toward hybrid architectures and domain-specific adaptations. No scientific awards were documented in the provided materials. Kjäer has secured significant research funding through FFG projects: USEFLEDS (2019-2022) on blockchain/BIM in construction, DiCYCLE (2022-2026) for circular economy applications, and USEFLEDS (2023-2027) for energy data spaces. The text indicates no student advising activities. He operates within TU Wien's Automation Systems research group, collaborating on blockchain-enabled solutions for industrial automation challenges across energy, construction, and manufacturing sectors.
Marie-Louise Lackner is a Research Fellow (PostDoc Researcher) in the Department of Databases and Artificial Intelligence at Technische Universität Wien. She holds Diplom-Ingenieur and Dr. techn. degrees and actively contributes to the CD Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling (2017–2025). Her research focuses on Artificial Intelligence and Optimization , with emphasis on industrial scheduling, constraint programming, and algorithm design. Key domains include: Industrial oven scheduling and production leveling Metaheuristics (simulated annealing, large neighborhood search) Multi-objective optimization in manufacturing systems Combinatorial mathematics and discrete structures Her publications demonstrate a strong trajectory in applied optimization , with recent work concentrating on energy-efficient scheduling algorithms for electronic manufacturing, while earlier research explored permutation patterns and social choice theory. Methodologies consistently integrate theoretical computer science with industrial problem-solving. She supervises graduate researchers, including P. Malik's work on memetic algorithms for production leveling. Grant involvement includes the CD Laboratory project focused on AI-driven planning systems. Lackner is affiliated with the CD Laboratory for Artificial Intelligence and Optimization, conducting applied research in industrial scheduling systems within TU Wien's Databases and AI group.
Francesco Pontiggia is a PreDoc Researcher at the Institute of Computer Engineering, Faculty of Informatics at TU Wien. His research focuses on formal verification of probabilistic systems, cyber-physical systems, and automated controller synthesis. He contributes to the ProbInG project (2020–2025), developing model checkers for probabilistic operator precedence languages and related formalisms. Key research interests include probabilistic hyperproperties, formal methods for probabilistic programming, and verification techniques for recursive stochastic systems. His work bridges theoretical foundations with practical tools such as POPACheck and POMC, targeting applications in automated planning and program analysis. Recent publications (2019–2025) emphasize advancements in model checking algorithms for probabilistic systems, operator precedence languages, and exception handling mechanisms. These works address challenges in verifying complex systems with stochastic behaviors and conditional probabilities. He collaborates on projects integrating formal methods with compiler design, runtime verification, and cyber-physical system design. His research has led to tool implementations and theoretical frameworks published in top-tier venues like QEST and ACM Transactions on Programming Languages and Systems.
Ulrike Maria Ritzinger is a Lecturer at TU Wien's Faculty of Informatics, Department of Logic and Computation. She teaches the course 'Optimization in Transport and Logistics' (2025S), focusing on advanced optimization techniques applied to logistics systems. Her work intersects computational methods with practical transportation challenges. Research interests are inferred from her teaching specialization in optimization and transport systems. No scientific awards or grants are explicitly listed in the provided information. No advising records or laboratory affiliations are mentioned in the available data.
Zeynep Gözen Saribatur Yaman is a PostDoc Researcher at the Department of Databases and Artificial Intelligence, Technische Universität Wien (TU Wien). Her role is supported by the Austrian Science Fund (FWF) as a Projektassistentin (Dr.in techn.). She is affiliated with the DBAI group and contributes to multiple research projects including AURA (2022–2026), DynaCon (2017–2020), AI4EU (2019–2021), and HumanE-AI-Net (2020–2024). Her primary affiliation is with TU Wien’s Faculty of Informatics, where she focuses on advancing explainable AI through abstraction techniques in logic-based systems. Zeynep holds a Doctorate in Technical Sciences (Dr.techn.) from TU Wien (2019), where her dissertation addressed Abstraction for reasoning about agent behavior with answer set programming . She also holds an MSc in a relevant field, though its specifics are not explicitly detailed in the text. Her research spans multiple funded initiatives, emphasizing both theoretical contributions and applied work in robotics and agent systems. Her research interests revolve around abstraction mechanisms in Answer Set Programming (ASP), argumentation frameworks , and their applications to explainable AI , robotics planning , and agent behavior modeling . She explores techniques to reduce complexity in logic-based systems while preserving critical reasoning aspects, with a focus on making AI systems more transparent and understandable. Zeynep has contributed to several projects aiming to enhance AI reasoning through abstraction. Her work bridges formal methods and practical AI challenges, such as reasoning about dynamic environments and multi-agent systems. She actively participates in international conferences and workshops, including KR, AAMAS, ICAPS, and EPIA, where she presents advancements in knowledge representation and reasoning. Her advising record is not explicitly stated in the provided texts. She has collaborated on grants from FWF, EU Horizon 2020, and other competitive funding bodies. Her research also intersects with cognitive factories and hybrid reasoning systems for robotics applications. As part of the DBAI group at TU Wien, she contributes to the development of AI tools and methodologies that prioritize comprehensibility and scalability. Her lab affiliations include the Knowledge-Based Systems Group (DBAI), where she works on theoretical and applied AI challenges.
Nadine Schwab is a PreDoc Researcher at the Department of Data Science, Faculty of Informatics, TU Wien. She focuses on interdisciplinary research at the intersection of operations research and healthcare technology. Her work includes optimizing railway logistics and developing AI-driven solutions for chronic disease management. Projects: VIPES (2022–2025): Recommender systems for rheumatoid arthritis self-management Locomotive scheduling optimization with conflict resolution frameworks Publications: Recent contributions address railway operational challenges and healthcare applications, demonstrating cross-domain expertise in optimization techniques and patient-centric technologies.
Florian Sextl is a Research Fellow and university assistant at the Formal Methods in Systems Engineering research unit at TU Wien, where he conducts research on program verification and formal foundations of programming languages. His work focuses on memory safety fundamentals, particularly through separation logic-based methods and the Rust programming language. Current projects explore biabduction techniques for ensuring memory safety across Rust-C foreign function interfaces and compositional shape analysis. Sextl teaches Program Analysis courses and has supervised research on join operators for bi-abductive analysis of low-level code. He maintains expertise in interactive theorem proving with Isabelle/HOL and Rocq.
Wolfgang Slany is Professor at Vienna University of Technology focusing on AI applications in scheduling systems. His research develops domain-specific languages and hybrid algorithms for workforce optimization in healthcare, call centers, and industrial settings. Key research areas: Fuzzy logic in AI systems Automated shift planning Break scheduling heuristics Test automation frameworks Publications demonstrate iterative development of the TEMPLE modeling language for staff scheduling. Recent work combines constraint programming with local search techniques to solve complex real-world scheduling problems across supervision systems and manufacturing environments.
Magdalena Steinböck is a PreDoc Researcher at Vienna University of Technology (TU Wien), affiliated with the Security and Privacy research group (E192-06). She holds a Dipl.-Ing.in (Master's equivalent) and BSc in technical disciplines. Her research focuses on mobile ecosystem security , particularly cross-platform analysis of iOS and Android systems, including deep link security, local network permissions, and vulnerability mitigation strategies. Current research projects: IoTIO (2020–2025) , W4MP (2023–2027) Active contributor to software repository mining (MSR 2024 conference paper) Her technical work spans mobile application analysis frameworks, security hardening comparisons, and user-centric permission system evaluations. She has published on iOS/Android ecosystem differences and cross-platform app matching methodologies.
Manuel Wimmer is a Lecturer in Business Informatics at TU Wien's Faculty of Informatics, specializing in model-driven engineering methodologies. His research develops foundations for model transformation, metamodeling, and interdisciplinary engineering. Research interests include model-driven software engineering, cyber-physical systems, web engineering, and industrial automation, with applications in smart production systems. Current work focuses on bridging IT/OT domains through standardized modeling approaches. Recent publications address quantum-edge cloud architectures, AI-enhanced modeling, and industrial security challenges. Article trends demonstrate strong focus on modeling language engineering, interoperability solutions, and quality assurance in complex systems. Leads the Christian Doppler Laboratory for Model-Integrated Smart Production and coordinates EU projects on low-code engineering platforms. Supervises doctoral research in model-driven technologies and software quality.
