Thomas Eiter is a Professor at TU Wien's Institute of Logic and Computation. His research focuses on declarative programming paradigms, knowledge representation, and artificial intelligence. He leads projects in neurosymbolic systems, answer set programming (ASP), and stream reasoning, with applications in visual question answering, scheduling optimization, and semantic scene generation. Eiter has contributed to foundational work in ASP semantics, computational complexity, and hybrid reasoning frameworks. His work bridges logical formalisms with practical AI challenges, emphasizing explainability and scalability. Projects like ALASPO and neurosymbolic integration showcase his focus on advancing both theoretical and applied aspects of AI. Projects: HumanE AI Network, WASP, REWERSE Research Themes: Neurosymbolic AI, Answer Set Programming, Stream Reasoning Notable achievements include pioneering work on semiring-based reasoning frameworks and developing efficient ASP solvers like Alpha. His contributions span over 471 publications, emphasizing interdisciplinary applications in computer vision, robotics, and automated planning.
Tomáš Skřivan serves as a Research Fellow at the Hoskinson Center for Formal Mathematics , Carnegie Mellon University. His work bridges formal mathematics with practical scientific computing through the development of the SciLean library in Lean 4, targeting enhanced reliability in machine learning and simulation software. Skřivan's research spans interdisciplinary domains with core emphases on: Physics-based simulation of fluid dynamics and wave phenomena Computer graphics algorithms for light transport and rendering Formal verification techniques applied to numerical methods Mathematical modeling of viscoelastic materials His publication trajectory since 2016 reveals evolving expertise from computational fluid dynamics (water wave simulation, viscoelastic modeling) toward formal methods in scientific computing, consistently merging theoretical rigor with practical implementation. Recent work on SciLean represents a strategic pivot toward verified software foundations. As a key contributor to the Hoskinson Center's mission, Skřivan collaborates on projects leveraging proof assistants to eliminate errors in scientific code. The center, established through Charles Hoskinson's support, pioneers mathematically guaranteed correctness in computational science through formal verification frameworks.
Sebastian Schrittwieser is a Researcher in the Research Group Security and Privacy, part of the Faculty of Computer Science. His work focuses on cybersecurity, code obfuscation, malware analysis, and machine learning applications in security. He leads and contributes to projects like INODES (Cyber Defense Strategies) and EMRESS (Resilience Evaluation Models). His research bridges theoretical foundations and practical applications, addressing challenges in software protection and threat detection. Key research interests include: Code Obfuscation Techniques and Resistance Adversarial Machine Learning and Risk Assessment Malware Analysis and Program Simulation User Behavior in Cybersecurity Contexts Recent publications emphasize empirical studies on IT/OT infrastructure security, graph neural network vulnerabilities, and quantum-inspired machine learning. He actively collaborates with institutions like SBA Research and presents at international conferences. Grants include Research Funding for projects on optimal cyber defense strategies (INODES) and software resilience evaluation (EMRESS). His work aligns with interdisciplinary efforts in security engineering and privacy-preserving technologies.
Andreas Uhl is a University Professor in Artificial Intelligence and Human Interfaces at the Department of Computer Science, University of Salzburg. With a prolific research career spanning from 1996 to present, he has authored or co-authored 534 publications and led or participated in 59 research projects. His work demonstrates sustained academic productivity with recent publications and projects extending through 2025. Professor Uhl's research interests span multiple domains at the intersection of artificial intelligence and practical applications. His primary focus areas include computer vision, biometrics, biomedical imaging, and digital forensics, with significant contributions to pattern recognition and image analysis. His work bridges theoretical computer science with practical applications in cultural heritage preservation, medical diagnostics, and security systems, demonstrating a versatile research portfolio that addresses both fundamental challenges and real-world problems. His recent publications reveal a strong emphasis on temporal image forensics, biomedical image analysis, and biometric security. The research shows a clear trajectory toward increasingly sophisticated applications of AI in specialized domains, with particular attention to validation methodologies and limitations of current approaches. His work on cultural heritage applications demonstrates an innovative application of computer vision techniques to historical artifacts. Best paper award @ 25th ACM Symposium on Applied Computing (Applications Track), 2010 Best Paper award @ 2nd European Workshop on Visual Information Processing (EUVIP'10), 2010 IEEE Biometrics Council Best Paper Award (TBIOM), 2022 Kurt Zopf Preis, 2023 Professor Uhl actively leads multiple significant research initiatives, including the CDL-POSA project on People and Object Surface Authentication (2025-2032), Artificial Intelligence driven Biomedical Imaging Innovation (2025-2029), and the AIBIA Research and Transfer Junior Lab (2023-2025). His research group maintains active collaborations with institutions like Carnegie Mellon University, as evidenced by his recent research stay there in September 2024. The scope and duration of his current projects indicate substantial grant funding and institutional support for his research agenda. His laboratory activities focus on AI applications in biomedical imaging, border security through vehicle-integrated technologies (AutoBorder project), and cultural heritage analysis. The research environment appears to integrate academic inquiry with practical transfer through initiatives like the FFG Student Internships program, suggesting a strong commitment to both fundamental research and real-world implementation.
Manfred Dorninger serves as Associate Professor in the Department of Meteorology and Geophysics at the University of Vienna's Faculty of Earth Sciences, Geography and Astronomy. His research spans meteorological modeling, renewable energy systems, and atmospheric observation techniques. His primary research interests focus on weather forecasting in complex terrain , cloud physics and classification , lightning phenomena , and renewable energy integration . Recent work leverages machine learning for ground-based cloud observations and improves photovoltaic efficiency under variable cloud conditions. His fingerprint analysis reveals significant contributions to Complex Terrain (100%), Lightning (72%), and Weather Forecast (42%) research domains. Analysis of his 111 publications shows increasing emphasis on AI-driven meteorological analysis (2024-2025), with recent papers developing neural network ensembles for cloud classification and novel metrics for ensemble forecast verification. His work bridges fundamental atmospheric science with practical applications in renewable energy and weather impact assessment. He has led significant research projects including NWP-Modellverifikation (2008-2011) and actively participates in the MesoVICT (Mesoscale Verification in Complex Terrain) initiative. His 120 recorded activities include 78 scientific talks and 38 poster presentations, demonstrating extensive knowledge dissemination. Dorninger maintains strong collaborative networks with researchers like Markus Rosenberger and Martin Weißmann, focusing on computational meteorology and atmospheric observation systems. His laboratory work centers on ground-based optical radar systems and neural network applications for weather parameter analysis.
Bernhard Aichernig serves as a University Professor at the Institute of Formal Models and Verification at Johannes Kepler University Linz. His academic career focuses on bridging theoretical computer science with practical software verification techniques. He actively contributes to the international research community through publications, program committees, and doctoral examinations. Professor Aichernig's research primarily centers on formal methods and model verification, with significant contributions to automata learning and software testing methodologies. His work explores the intersection of theoretical computer science and practical verification techniques, particularly in state-merging approaches for passive learning systems. His research has direct applications in improving software reliability through formal testing frameworks. His recent publication trends indicate a strong focus on advancing automata learning techniques, particularly extending the AALpy framework with passive learning capabilities. This work represents the cutting edge of model inference and formal verification, addressing challenges in state-merging algorithms for complex software systems. His research bridges theoretical foundations with practical testing applications. Professor Aichernig serves as an active member of the academic community through various roles including doctoral examination committees and program committees for major conferences like the NASA Formal Methods Symposium. He has examined PhD theses on advanced reasoning techniques for quantified Boolean formulas, learning Mealy machines with local timers, and deep integration of SAT solving with model checking. He currently participates in the Cluster of Excellence 'Bilateral Artificial Intelligence' project as a Principal Investigator, working alongside prominent researchers in the AI field. This active research project, funded by the Austrian Science Fund (FWF), runs from October 2024 through September 2029 and represents a significant collaborative effort in artificial intelligence research at JKU Linz.
