جست‌وجوی استاد و پژوهشگر

Cezary Kaliszykمشاهده پروفایل

استاد

• Theoretical Computer Science
• Automated Reasoning
• Formal Methods
• +۱۳ مورد دیگر

Cezary Kaliszyk is a Professor in Theoretical Computer Science at the University of Melbourne. His research focuses on automated reasoning, formal methods, and learning for reasoning, particularly in the context of interactive theorem proving and formalized mathematics. He leads the ERC project 'FormalWeb3' and has been involved in several other significant research initiatives. Theoretical Computer Science Automated Reasoning Formal Methods Interactive Theorem Proving Machine Learning for Theorem Proving Formalized Mathematics Proof Guidance Learning for Reasoning His research explores the integration of machine learning techniques with formal reasoning systems to enhance automation in theorem proving. This includes developing systems like CoqHammer and Tactician, advancing premise selection, proof guidance, and learning-based proof search strategies. His work bridges logical foundations with practical AI-driven tools for formal verification. The recent publications demonstrate a consistent focus on advancing automated and interactive theorem proving through learning techniques, formalization of mathematical concepts (like surreal numbers), and improving reasoning systems (e.g., Prover9, tableaux methods). There is a strong emphasis on practical system development, formalization projects, and learning-based enhancements to reasoning. ERC project 'FormalWeb3' - Principal Investigator Cost Action EuroProofNet - WG5 Leader until 2024 FWF project P26201 - developing HOL(y)Hammer Other projects: JSPS P10044, NWO MathWiki, SURF WebDed, ProofWeb He has advised several PhD students to completion, including Michael Färber, Thibault Gauthier, Yutaka Nagashima, Stanisław Purgał, and Liao Zhang, and is currently supervising Daniel Ranalter and Neil Vyas. He has not received any explicitly mentioned scientific awards in the provided text. His work involves leadership in collaborative systems such as ProofWeb and HOL Import, and participation in major formalization efforts including the Mizar library integration with Isabelle. He is actively involved in the development of tool ecosystems for formal mathematics and automated reasoning.

Manuel Wimmerمشاهده پروفایل

استاد

• Model-Driven Software Engineering
• Digital Twins
• Model-Driven Engineering
• +۵ مورد دیگر

Manuel Wimmer is a Full Professor and Head of the Department of Business Informatics – Software Engineering at Johannes Kepler University Linz, Austria. He also serves as the Program Director for the Business Informatics master's program since 2019. His academic leadership extends to representing JKU Linz in the AutomationML society and leading significant research initiatives. Dr. Wimmer received his Ph.D. and Habilitation from TU Wien. His academic journey includes: Research associate at the University of Malaga, Spain Visiting professor at the University of Marburg, Germany Visiting professor at TU Munich, Germany Assistant professor at the Business Informatics Group (BIG), TU Wien, Austria Professor Wimmer's research focuses on Model-Driven Software Engineering and its applications, particularly in the emerging field of Digital Twins . His work bridges theoretical foundations with practical industrial applications, with special emphasis on model transformations, runtime modeling, and the integration of artificial intelligence techniques into model-driven approaches. More recently, he has been exploring the intersection of model-driven engineering with quantum computing, investigating how modeling principles can be applied to quantum software development. His recent publications reveal a strong trend toward Digital Twin engineering, with approximately 40% of his 2023-2025 publications focusing on various aspects of Digital Twin technology. Another significant strand of his work involves the application of AI and machine learning techniques to enhance model-driven engineering processes. The emergence of quantum software engineering as a research direction is also notable in his most recent publications, demonstrating his ability to identify and explore cutting-edge research frontiers. From 2017-2023, Professor Wimmer led the Christian Doppler Laboratory on Model-Integrated Smart Production (CDL-MINT), where he developed engineering approaches for digital twins. He is also the co-author of the influential book "Model-driven Software Engineering in Practice" (2nd edition, 2017). Professor Wimmer is actively involved in the organization of major scientific events including the IEEE International Conference on Quantum Software (QSW) and the International Conference on Engineering Digital Twins (EDTconf), demonstrating his leadership in these emerging research communities. His research has practical applications across various domains including smart cities, industrial automation, tunneling/construction, and quantum computing. The MATISSE project represents a significant multi-partner effort to develop a framework for federated digital twins of industrial systems.

Tom Henzingerمشاهده پروفایل

استاد

• Formal Methods
• Software Engineering
• Computer Science
• +۵ مورد دیگر

Tom Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA), leading the Tom Henzinger Group. His research focuses on formal methods to enhance software quality, addressing challenges in concurrent systems, real-time and embedded systems, quantum computing, neural networks, and biochemical reaction networks. He has advised numerous PhD students and postdoctoral researchers, contributing to advancements in these fields. His work emphasizes mathematical rigor in software analysis and verification. Current projects include VAMOS (Middleware for best-effort third-party monitoring) and SPyCoDe (Semantic and Cryptographic Foundations of Security and Privacy by Compositional Design). Past projects involved model checking and runtime verification techniques. Research interests span concurrent software synthesis, quantitative modeling of reactive systems, predictability of real-time systems, and formal methods applied to quantum computation and neural networks. He is also engaged in exploring hardware-optimal solutions for quantum algorithms. Administrative Assistant Ksenja Harpprecht supports the group. Courses taught include 'Formalisms Every Computer Scientist Should Know,' focusing on logics, automata, and semantics. The group hosts regular seminars, such as the Tuesday group seminar and the Wednesday joint formal methods seminar with TU Wien's FORSYTE group.

Jeffrey Kramerمشاهده پروفایل

استاد

• Software engineering
• Distributed computing
• Requirements engineering
• +۵ مورد دیگر

Professor Jeffrey Kramer is a distinguished academic at Imperial College London, where he currently serves as Professor in Distributed Computing within the Department of Computing, Faculty of Engineering. With a career spanning over four decades at Imperial College, he has held numerous leadership positions including Senior Dean (2009-2012), Dean of the Faculty of Engineering (2006-2009), and Head of the Department of Computing (1999-2004). Professor Kramer's research focuses on software engineering, particularly in distributed computing, requirements engineering, software architecture, and self-managing adaptive systems. His work on the Darwin Software Architecture led to commercial implementation by Philips in consumer television products. He has been a principal investigator in research projects developing the CONIC and DARWIN environments for distributed programming. His publication portfolio includes over 200 refereed journal and conference papers along with two influential books: 'Distributed Systems and Computer Networks' (1986) and 'Concurrency: State Models & Java Programs' (1999, 2nd ed. 2006). His most cited works address software architecture, dynamic reconfiguration, and adaptive systems, demonstrating a consistent research trajectory focused on foundational aspects of distributed software systems. FREng Fellow of the Royal Academy of Engineering (2008) ACM SIGSOFT Outstanding Research Award (2005, joint with Prof. Magee) ACM SIGSOFT Distinguished Service Award (2011) Editor-in-Chief of IEEE Transactions on Software Engineering (2006-2009) Most Influential Paper Award at ICSE 2003 IEE Informatics Premium prize (1998/99) Professor Kramer has served on over 50 international conference committees in the last decade and has delivered numerous keynote addresses globally. His industrial collaborations include work with BP, BT, NATS, Fujitsu, Barclays Capital, QinetiQ, Kodak, Microsoft, and Philips. He has also served as an expert witness for major law firms and has been involved in numerous research collaborations and consulting engagements.

• Cybersecurity
• Game Theory
• Quantum Cryptography
• +۵ مورد دیگر

Stefan Rass is a Professor at the Institute of Networks and Security within the Faculty of Engineering & Natural Sciences at Johannes Kepler University Linz (JKU), where he leads the LIT Secure and Correct Systems Lab. As Principal Investigator for FFG-funded projects including reSilienz (digital supply chain resilience, 2023–2025) and ITPUK (AI signature verification, 2022–2024), he bridges theoretical game theory with practical cybersecurity solutions for critical infrastructures and robotics systems. His research spans game-theoretic security models (patrolling games, defense-in-depth strategies), quantum cryptography (QKD network architectures), and cyber deception frameworks like Honeyquest for measuring honeypot effectiveness. Recent work addresses robotics security benchmarking (RobotPerf), cryptographic instruction chaining for control flow protection, and risk assessment methodologies for interdependent infrastructures. His mathematical decision-making approach integrates bounded rationality and stochastic modeling to solve real-world security challenges. Professor Rass actively shapes the field through program committee roles (ARES 2023), peer reviews, and invited talks on security transparency. His current projects focus on cost-benefit-aware monitoring for cyber-physical systems and quantum key distribution standardization, reflecting Austria’s strategic priorities in digital resilience. The LIT Secure and Correct Systems Lab under his direction develops foundational theories while deploying tools for industrial applications, particularly in critical infrastructure protection and secure robotics workflows.

Thomas Gärtnerمشاهده پروفایل

استاد

• Machine Learning
• Graph Neural Networks
• Geometric Deep Learning
• +۳ مورد دیگر

Thomas Gärtner is a Professor at the Institute of Logic and Computation within the Faculty of Informatics at Vienna University of Technology, leading the Machine Learning research group (E194-06). His work bridges theoretical machine learning with practical applications in chemistry, biology, and network analysis. His primary research focuses on graph neural networks (GNNs) and geometric deep learning, with significant contributions to GNN expressivity, graph transformations, and kernel methods for structured data. He explores fundamental questions about the limitations of message-passing architectures while developing practical enhancements like path-based extensions and expectation-complete representations. His chemical informatics work applies these techniques to binding affinity prediction, reaction classification, and solvent selection, demonstrating real-world impact in computational chemistry. Analysis of his 15 most recent publications (2023-2025) reveals three dominant research thrusts: theoretical GNN advancements (35% of articles), chemical informatics applications (40%), and novel learning frameworks (25%). The theoretical work increasingly addresses expressivity limitations through graph transformations and path-based approaches, while chemical applications show growing sophistication in molecular representation. Recent publications also indicate expanding interest in foundation models for graphs and robustness verification. He actively supervises master's students including Fabian Traxler (binding affinity prediction), Maximilian Plattner (SGD optimization), Fabian Jogl (graph transformations), and Thomas Schmied (reinforcement learning). His research is conducted through the Network Lab at TU Wien, where he serves as Principal Investigator for the Structured Data Learning with Generalized Similarities project.

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +۳ مورد دیگر

Marlies Temper is a Professor at St. Pölten University of Applied Sciences, serving as Academic Director for the Data Intelligence (MA) and Data Science and Artificial Intelligence (BA) programs. She is Deputy Head of the Department of Computer Science and Security and a member of the UAS Board from 2023 to 2026. Her roles include leading educational initiatives, conducting research, and managing continuing education programs. Academic Director, Data Intelligence (MA) Academic Director, Data Science and Artificial Intelligence (BA) Deputy Head, Department of Computer Science and Security Continuing Education Officer Marlies Temper's research focuses on Artificial Intelligence , Cybersecurity , and Big Data Analytics . She explores the ethical implications of AI, develops gamified models for security training, and investigates continuous biometric authentication methods. Her work bridges the gap between theoretical advancements and practical applications in AI-driven security solutions. Her recent publications highlight trends in AI Security (e.g., risk frameworks), Biometric Authentication (e.g., touch dynamics), and Cybersecurity Education (e.g., gamified simulations). She emphasizes the responsible use of AI and Big Data while addressing technical and ethical challenges.

Franz Wotawaمشاهده پروفایل

استاد

• Model-Based Reasoning
• Qualitative Reasoning
• Theorem Proving
• +۶ مورد دیگر

Franz Wotawa is a Professor of Software Engineering at Graz University of Technology. He holds a M.Sc. (1994) and PhD (1996) from Vienna University of Technology. He has served as head of the Institute for Software Technology from 2003–2009 and since 2020. His research focuses on model-based reasoning, software testing, autonomous systems, and diagnosis, with over 390 peer-reviewed publications. He founded Softnet Austria (2006) to bridge research and industry. He leads the Christian Doppler Laboratory for Quality Assurance Methodologies for Autonomous Cyber-Physical Systems since 2017 and has supervised 90+ master and 36+ PhD students. His awards include the 2016 Lifetime Achievement Award from the International Diagnosis Community. He is a member of Academia Europaea, IEEE, and AAAI. **Education**: M.Sc. in Computer Science, Vienna University of Technology, 1994 PhD, Vienna University of Technology, 1996 **Research Interests**: Model-based reasoning, qualitative reasoning, theorem proving, mobile robotics, verification/validation, software testing/debugging, AI, and autonomous systems. **Notable Projects**: A-IQ Ready (2022–2026): Quantum sensing for autonomous systems. ALFA (2024–2027): AI for smart diagnosis in building automation. Bilateral AI (2024–2029): Combining symbolic and sub-symbolic AI. VARCOS (2025–2028): Vehicle-road cooperative systems for autonomous driving. **Awards & Memberships**: Lifetime Achievement Award (2016, International Diagnosis Community) Senior Member, AAAI Member of Academia Europaea, IEEE, ACM, and Austrian Computer Society **Labs/Teams**: Christian Doppler Laboratory for Quality Assurance Methodologies (since 2017). Active in Cluster of Excellence “Bilateral AI” at TU Graz.

• Software Engineering
• Probabilistic Programming
• Security and Penetration Testing
• +۵ مورد دیگر

Jürgen Cito is an Associate Professor in the Department of Software Engineering at the Faculty of Informatics, TU Wien, where he leads research in probabilistic programming, security, and configuration management. His work is supported by major grants from the Austrian Science Fund (FWF), European Commission, and Meta Platforms, Inc., with active projects spanning 2022-2027. His research focuses on the intersection of software engineering and machine learning, particularly in static analysis of probabilistic programs, AI-driven penetration testing, and infrastructure security. Key contributions include identifying secret exposure in configuration files, grammar inference for ad hoc parsers, and performance prediction from source code, often combining empirical studies with tool development. Analysis of his 15 most recent publications (2020-2024) reveals three dominant trends: (1) Security vulnerabilities in configuration management systems, especially secret leakage in dotfiles; (2) Application of large language models to offensive security testing; and (3) Machine learning techniques for performance prediction and AutoML optimization in software contexts. Cito has supervised 22 Master's students on cutting-edge topics including AI security, infrastructure as code, and program analysis. His current research portfolio includes: Types4Strings (FWF, 2024-2027): Type systems for string processing Cloud Open Source Research Mobility Network (EU, 2023-2026): Open-source cloud infrastructure Software Assistants for Probabilistic Programming (Meta, 2022-2026): AI tools for probabilistic code He is embedded in TU Wien's Institute of Software Technology and Interactive Systems (E194), collaborating on cross-institutional projects focused on software security and developer tooling, with particular emphasis on empirical validation of security practices and configuration management systems.

Ramin Hasaniمشاهده پروفایل

پژوهشگر

• Machine Learning
• Robotics
• Neural Networks
• +۳ مورد دیگر

Ramin Hasani is a researcher at TU Wien's Cyber-Physical Systems department. He holds a Dr.techn. (Doctor of Engineering) and specializes in machine learning applications for robotics, control systems, and biologically-inspired neural networks. His work focuses on developing interpretable neural architectures like Liquid Time-Constant Networks and Neural Circuit Policies, emphasizing safety and stability in autonomous systems. Hasani's research bridges neural network theory with practical robotics challenges, including autonomous racing, medical data analysis, and adversarial robustness. Key research areas include continuous-time neural networks, formal verification of neural ODEs, and bio-inspired control mechanisms derived from biological neural circuits (e.g., Caenorhabditis elegans). He collaborates extensively with institutions like MIT and ETH Zurich, contributing to projects in health-monitoring systems and end-to-end robot learning frameworks. His publications consistently address real-world challenges such as sepsis prediction via reinforcement learning and robust CNN architectures for image classification. Recent work highlights include developing stable recurrent networks through Gershgorin loss functions and advancing zero-shot transfer learning for autonomous systems. Hasani's interdisciplinary approach integrates principles from neuroscience, control theory, and machine learning to create auditable, high-performance AI solutions for cyber-physical environments.

Warner Marzocchiمشاهده پروفایل

استاد

• Seismic Hazard Analysis
• Volcanic Hazard Analysis
• Earthquake Forecasting
• +۳ مورد دیگر

Warner Marzocchi is Professor of Geophysics and Natural Hazard Forecasting at the University of Naples Federico II and Scuola Superiore Meridionale. He holds a PhD in Physics (1992) and a degree in Earth Sciences (1987) from the University of Bologna. Previously, he served as Chief Scientist at the Istituto Nazionale di Geofisica e Vulcanologia (2003-2018) and held visiting positions at the University of Southern California and the Institute of Statistical Mathematics in Tokyo. His research focuses on seismic/volcanic hazard analysis, earthquake forecasting, statistical seismology, and hazard model validation. He coordinates international projects in natural hazard assessment and serves on advisory panels including the International Atomic Energy Agency, US National Seismic Hazard Model, and Global Volcano Model. Marzocchi's recent publications demonstrate advances in probabilistic forecasting models, operational earthquake forecasting systems, and statistical validation methods. His work frequently applies machine learning and complex systems theory to seismic and volcanic processes across Italy, California, and New Zealand. Honors: Ranked among world's top 2% scientists (2019-present) Member of Academia Europaea (2018) Editorial awards from Geophysical Journal International (2010) and Geophysical Research Letters (2003) He teaches graduate courses in geophysics, statistics, and hazard forecasting, and has advised numerous PhD students. He leads research teams focused on seismic risk mitigation and volcanic unrest management.

• Remote Sensing
• Forestry
• Vegetation Monitoring
• +۴ مورد دیگر

Markus Immitzer is a Senior Scientist at the Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU). His research focuses on land cover classification, vegetation monitoring, and biophysical parameter modeling using remote sensing data from various platforms (UAV, aircraft, satellite) and sensors (multispectral to hyperspectral, LiDAR). He teaches courses in remote sensing, geodata management, and photogrammetry. Education includes a Ph.D. (2017) on mapping tree species using satellite imagery, Diplomingenieur in Forestry Science (2013), and M.Sc. in Wildlife Ecology and Wildlife Management (2011), all from BOKU. Professional experience includes positions at Swiss Federal Research Institute WSL (2023) and Geotree Ltd./Mantle Labs Ltd. (2022). Research interests center on developing remote sensing methodologies for forestry applications, habitat modeling, and vegetation monitoring. Key focus areas include tree species classification, disturbance detection, biomass estimation, and multi-sensor data integration for environmental monitoring. Recent publications demonstrate strong emphasis on operational forest monitoring using multi-platform remote sensing data, with applications in carbon project verification, biodiversity assessment, and fire danger modeling. Research increasingly incorporates machine learning techniques and multi-temporal analysis for improved accuracy in vegetation characterization. Awards and honors include: BOKU Best Paper Award (2024) BOKU Sustainability Award nomination (2020) Multiple dissertation awards (2018-2019) Research prizes for forest remote sensing (2017-2018) Performance scholarships (2005-2009)

Stefan Rassمشاهده پروفایل

استاد

• Security and Risk Management
• Decision and Game Theory for Security
• Security Infrastructures
• +۹ مورد دیگر

Stefan Rass is a full Professor at Alpen-Adria-Universität Klagenfurt (AAU), with additional affiliation at Johannes Kepler University Linz (JKU). He holds the academic title Univ.-Prof. (Universitätsprofessor) and possesses advanced degrees including PD (Privatdozent), Dipl.-Ing. (Diplom-Ingenieur), and Dr. (Doctor). His research spans multiple institutions and projects, with a focus on security and risk management through game theory applications. Professor Rass's research interests center around Security and Risk Management, Decision and Game Theory for Security, Security Infrastructures (including Key Distribution and Management, PKI, and Authentication), Unconditional and network security, Applied Quantum Cryptography, and Complexity Theory and Statistics in Security. His work bridges theoretical computer science with practical security applications, particularly in quantum networks and critical infrastructure protection. His recent publications demonstrate a strong trend toward interdisciplinary security research, combining game theory with quantum cryptography, robotics security, and AI-powered penetration testing. The articles reveal increasing focus on practical applications of theoretical security concepts, with notable work in quantum networks, deniable encryption techniques, robotics security benchmarking, and AI-assisted security testing. His research shows consistent evolution from theoretical foundations toward real-world implementation challenges. Professor Rass leads multiple ongoing research projects including Machine Learning for Risk Management, Safe and Secure Robotic Systems Engineering (SEEROSE), Simulation and analysis of critical network infrastructures in cities (ODYSSEUS), and security for cyber-physical value networks Exploiting smaRt Grid systems (synERGY). These projects, primarily funded by FFG (Austrian Research Promotion Agency), demonstrate his leadership in securing critical infrastructure and developing next-generation security frameworks.

• Formal Methods
• Cyber-Physical Systems
• Probabilistic Programming
• +۴ مورد دیگر

Ezio Bartocci is a Full Professor in Formal Methods for Cyber-Physical Systems Engineering at TU Wien's Faculty of Computer Science. He leads the Trustworthy Cyber-Physical Systems (TrustCPS) Group within the Cyber-Physical System Research Unit. His research focuses on formal verification, probabilistic systems, and runtime monitoring, with applications in autonomous systems, safety-critical software, and embedded systems. Roles & Affiliations: Full Professor, TU Wien (100% research focus) Principal Investigator in projects funded by EU, WWTF, FFG, and industry partners Chair of the Curriculum Commission for Computer Engineering Editor-in-Chief of the Formal Methods in Outer Space series Research Interests: Formal methods for CPS: verification, synthesis, and runtime monitoring Probabilistic programming and loop analysis Temporal logic specifications and mining Automated tools for safety-critical systems (e.g., Polar, MoonLight) Applications in healthcare, robotics, and autonomous vehicles Key Projects: ProbInG (2020–2025): Analyzing probabilistic loops ARTIST (2021–2026): AI and robotics safety EdgeAI (2022–2025): Optimizing embedded processing TAIGER (2023–2027): Trustworthy AI and CPS Grants & Funding: €10M+ secured from EU Horizon 2020, WWTF, FFG, and industry partners like TTTech Auto AG. Academic Leadership: Teaches courses on logical methods, CPS engineering, and scientific research at TU Wien. Supervises PhD students in formal methods and CPS domains. Tools Developed: Polar (probabilistic loop analyzer), MoonLight (spatio-temporal monitoring), and FIM (fault injection tool).

• Artificial Intelligence
• Database Systems
• Neurosymbolic Reasoning
• +۳ مورد دیگر

Alexander Beiser is a Researcher at TU Wien's Department of Databases and Artificial Intelligence, part of the Faculty of Informatics. His work focuses on neurosymbolic reasoning, logic programming systems like Answer Set Programming (ASP), and hybrid grounding techniques to optimize computational workflows. He teaches the 2025S Algorithms and Data Structures course (186.866) as a VU lecture. His research bridges formal methods with practical applications in AI, emphasizing user interaction frameworks (e.g., clinguin) and improving efficiency in logic-based systems. Key areas include advancing neurosymbolic integration with LLMs, optimizing ASP-driven systems through hybrid grounding, and developing tools for interactive logic programming. Recent contributions address bottlenecks in grounding processes via splitting and rewriting strategies, aiming to enhance scalability in complex reasoning tasks. Alexander is based at Favoritenstrasse 9, Room HA0302, and can be reached via alexander.beiser@tuwien.ac.at. His work reflects a blend of theoretical advancements and applied systems engineering in AI and database technologies.