جست‌وجوی استاد و پژوهشگر

• 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).

• Cyber-Physical Systems
• Runtime Verification
• Formal Methods
• +۵ مورد دیگر

Dejan Nickovic is a Professor affiliated with the Department of Cyber-Physical Systems at TU Wien. His research focuses on runtime verification, formal methods for cyber-physical systems (CPS), and software testing. He leads projects such as TAIGER (2023–2027) and ARTIST (2021–2026), advancing CPS safety and AI robustness. His work bridges theoretical formalisms with practical applications in robotics, IoT, and embedded systems. Key research themes include specification mining, fault localization, and automated verification. He has developed tools like DeepRIoT for robotic-IoT integration and FIM for Simulink fault analysis. His professorial dissertation (2020) formalized runtime verification techniques for CPS. Recent publications emphasize adaptive architectures, backdoor detection in AI, and hyperproperties for security. He collaborates extensively with academia and industry, addressing challenges in CPS testing and verification through grants like ZK 35-G (2019–2024). His lab supports platforms like CPS/IoT Ecosystem for research and education. Projects: TAIGER, ARTIST, EdgeAI, ProbInG Supervised students: N. Manjunath (PhD, 2021), Florian Exenberger (2019), Markus Heindl (2017)

Ilir Murturiمشاهده پروفایل

پژوهشگر

• Edge Computing
• Internet of Things (IoT)
• EdgeAI
• +۳ مورد دیگر

Ilir Murturi is a PostDoc Researcher at the Distributed Systems Group, TU Wien, Vienna. He holds a PhD from TU Wien (2022) and B.Sc./M.Sc. degrees from the University of Prishtina, Kosova. His research focuses on Edge Computing, IoT, EdgeAI, and privacy in distributed systems. He has supervised multiple theses and contributed to projects like DECENT (a decentralized elasticity controller) and edge planning frameworks using AI. His work spans academic activities including reviewing for IEEE Transactions on Services Computing and ACM journals. Research interests include resource management in edge networks, federated learning, and zero-trust architectures. Recent publications explore elasticity frameworks, federated domain generalization, and distributed computing challenges. He has also co-edited a special issue on learning-driven edge computing for IoT networks.

• Cyber-Physical Systems
• Biological Systems
• Control Systems
• +۶ مورد دیگر

Radu Grosu is a Full Professor and Head of the Cyber-Physical Systems Research Unit at TU Wien. His research focuses on modeling, analysis, and control of cyber-physical and biological systems, with applications in robotics, autonomous systems, medical imaging, and formal verification. He leads the Scuderia Segfault team for autonomous F1TENTH racing and has extensive collaborations with industry partners like TTTech Auto AG and FFG. His work integrates machine learning, control theory, and formal methods to address challenges in safety-critical systems and autonomous decision-making. Roles: Full Professor, Head of Research Unit, Faculty Council Substitute Member Affiliations: TU Wien, Scuderia Segfault, Austrian Science Fund (FWF) projects Research interests span cyber-physical systems (CPS), cardiac-cell networks, genetic regulatory networks, and AI-driven solutions for healthcare and manufacturing. He has pioneered methods in neural circuit policies, flocking control, and real-time reinforcement learning. His projects include EdgeAI for embedded systems, radiation treatment optimization in glioblastoma, and autonomous vehicle testing frameworks. Key contributions include: Developing Lagrangian reachability analysis for safety verification Advancing neuromorphic IoT architectures and sensor networks Creating tools like DeepSTL for translating requirements into specifications Grants include FFG-funded projects on autonomous driving examiners and energy-efficient neuromorphic systems. His work bridges theoretical foundations with practical implementations in CPS resilience, medical diagnostics, and industrial automation.

• Computational Social Choice
• Algorithmic Game Theory
• Formal Methods
• +۶ مورد دیگر

Jie He is a PreDoc Researcher at the Department of Cyber-Physical Systems, Technische Universität Wien. His research spans computational social choice, algorithmic game theory, and formal methods in robotics and IoT systems. He works on multidisciplinary problems involving complexity analysis, fair division, and preference modeling. Current projects: EdgeAI (2022–2025), TAIGER (2023–2027), ADEX (2020–2024) Key collaborations: Research with R. Grosu, E. Bartocci, D. Nickovic Research interests focus on computational aspects of collective decision-making , including fair division, matching problems, and preference modeling. He works on both theoretical foundations (e.g., parameterized complexity) and practical applications (e.g., robotic-IoT systems). His publication history reveals deep expertise in computational complexity of social choice problems, with recent work on 3D stable roommates , fair division in graph-structured settings , and preference modeling through Euclidean and Manhattan geometries. As an advisor, he supervised diploma theses on: Optimization strategies for 5G transceivers Dynamic object detection in multi-agent systems His work appears in top conferences like ACM/IEEE DAC, ICSE, and various computational social choice venues.