جستجوی استاد راهنمای دانشگاهی

• Audio Signal Processing
• Music Information Retrieval
• Deep Learning
• +۳ مورد دیگر

Alexander Schindler is an External Lecturer at the Department of Information Systems Engineering at Technische Universität Wien. His research focuses on audio signal processing, music information retrieval, and deep learning applications in multimodal analysis. He coordinates the approacH project (2010–2013) funded by the European Commission, exploring audio-visual search engines. His academic background includes a Dipl.-Ing. in Technical Engineering and a Dr.techn. from TU Wien. Key research areas include acoustic scene classification, deepfake detection, and music video analysis. He has supervised students on topics like bird song identification and machine outage prediction. Notable publications span from unsupervised cross-modal learning (2020) to multi-modal MIR frameworks (2019). He holds a Bakk.techn. and advanced technical qualifications. Recent work includes advancements in deepfake audio detection (2025) and audio-visual surveillance systems (2024). His projects bridge theoretical research with practical applications in forensic analysis and industrial predictive maintenance. He contributes to international conferences like ACM SAC and DCASE, focusing on neural network architectures for audio analysis.

Johanna Schmidtمشاهده پروفایل

مدرس

• Data Visualization
• Visual Analytics
• Time Series Analysis
• +۳ مورد دیگر

Johanna Schmidt is a Lecturer at the Vienna University of Technology (TU Wien) and the Head of the Visual Analytics Research Group at VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH. She holds a PhD in data visualization from TU Wien (2016) and has extensive experience in project management and industry collaboration. Her primary research focuses on visual analytics for large datasets, particularly time-series data from industrial and energy sectors, as well as immersive analytics for decision support. She leads the Visual Analytics group at VRVis, a COMET research center, and has contributed to projects such as ArtVis, GuidedVA, and KnoVA. Her work integrates human perception studies with visualization techniques to enhance data interpretation. She has been recognized as the FEMtech Expert of the Month (2021) for promoting women in technology. Her academic roles include teaching at TU Wien and the Salzburg University of Applied Sciences. Key research interests span data visualization literacy, exploratory data analysis, and the application of visual methods in healthcare and energy sectors. Notable collaborations include developing tools like Visplore for industrial data analysis and advancing immersive analytics for hydropower turbine lifecycle predictions. Her work bridges academia and industry, emphasizing practical solutions for complex data challenges.

• Answer Set Programming
• Knowledge Representation and Reasoning
• Cyber-Physical Systems
• +۳ مورد دیگر

Dr. Peter Schüller is a Professor affiliated with the Department of Knowledge-Based Systems at TU Wien (Vienna University of Technology). He holds the academic title of Privatdozent (Priv.-Doz.) and has a background in Technical Engineering (Dipl.-Ing. Dr.techn. / Bakk.techn.). His roles include academic research, consulting services for intelligent automation, and partnership with Potassco Solutions. He is based at Favoritenstrasse 11, Room HG0312, and can be contacted via peter.schueller@tuwien.ac.at or contact@peterschueller.com. Education : Master's Thesis: 'Reconstructing borders of manually torn paper sheets using integer linear programming' (2008) PhD (Dr.techn.) in Informatics Habilitation (Privatdozent) qualification Research Interests : Schüller specializes in declarative problem solving through Answer Set Programming (ASP), with focus areas including hybrid knowledge integration systems, inconsistency management, and applications in robotics, traffic optimization, and industrial automation. His work bridges theoretical advances in computational logic with practical implementations in enterprise software architecture and database systems. Projects & Grants : He has led projects funded by the Austrian Research Promotion Agency (FFG), Austrian Science Fund (FWF), and Vienna Science and Technology Fund (WWTF). Key projects include: 'Dynamic knowledge-based (re)configuration of cyber-physical systems' (2017–2020) 'Integrated Evaluation of Answer Set Programs' (2015–2018) 'Inconsistency Management for Knowledge-Integration Systems' (2009–2012) Consulting & Partnerships : Provides services in enterprise software design, database optimization, GDPR compliance, and hybrid knowledge systems. Official partner of Potassco Solutions. Collaborates on initiatives like AI4EU and HumanE-AI-Net. Labs & Teams : Active in TU Wien's Knowledge-Based Systems Group. Involved in developing the DLVHEX and Hexlite solvers.

Renata Raidouمشاهده پروفایل

دانشیار

• Medical Visualization
• Visual Analytics
• Uncertainty Visualization
• +۳ مورد دیگر

Renata Raidou is an Associate Professor in Biomedical Visualization and Visual Analytics at the Research Unit of Computer Graphics, Institute of Visual Computing & Human-Centered Technology, TU Wien, Austria. She previously held positions at the University of Groningen and TU Wien as a postdoc. Her research focuses on medical visualization, uncertainty visualization, and data physicalization for healthcare applications. Raidou has received prestigious awards including the Dirk Bartz Prize (2017) and EuroVis Young Researcher Award (2022). She coordinates TU Wien's BSc in Digital Health and MSc in Medical Informatics. Education: PhD in Medical Visualization (Eindhoven University of Technology, 2017), MSc in Biomedical Engineering (TU Delft), Diploma in Electrical and Computer Engineering (NTUA). Professional Roles: Editorial Board member of Computer & Graphics , Steering Committee member of EG VCBM, and member of EASAC's AI in Healthcare working group. Research Interests: Medical visualization strategies for P4 medicine, anatomical edutainment through physicalization, and uncertainty-aware visual analytics in radiotherapy. Her work bridges visual computing, machine learning, and medical applications, emphasizing clinical decision support and public health education. Awards: Over 10 awards including Best Paper recognitions at IEEE Vis and EuroVis, and the EuroVis Best PhD Award. Her contributions span visualizing anatomical variability, ensemble data exploration, and interactive medical training tools. Grants & Projects: Leads the Health Virtual Twins project (2024–2028) for personalized stroke management. Active in interdisciplinary collaborations, including the Shonan Meeting on Formalizing Biological Visualization. Labs/Teams: Head of the Visualization Group at TU Wien, developing tools like Vologram (holographic medical data physicalization) and Slice and Dice anatomical crafts. Collaborates on edutainment systems and AI-driven healthcare visualization.

Katharina Kröslمشاهده پروفایل

مدرس

• Computer Graphics
• Virtual Reality
• Augmented Reality
• +۴ مورد دیگر

Katharina Krösl is a Lecturer at Vienna University of Technology, affiliated with the Institute of Computer Graphics and Algorithms within the Rendering and Modeling workgroup. Her research bridges computer graphics and assistive technologies through immersive virtual reality (VR) and augmented reality (AR) applications. PhD in Computer Science (2016-2020), supervised by Michael Wimmer Master's thesis on interactive photon tracing for lighting design Her work focuses on simulating vision impairments (e.g., cataracts), developing real-time rendering techniques, and creating VR-based training systems for disaster management and attention disorders. Publications span path tracing optimization, XR accessibility, and perceptual modeling. Scientific recognitions include the Young Experts Award 2021 and IEEE VR 2020 Best Research Demo Award. She contributes to IEEE and ACM publications while integrating luminaire design with VR/AR workflows.

• Data Management
• Information Retrieval
• Machine Learning
• +۳ مورد دیگر

Moritz Staudinger is a PreDoc Researcher at the Data Science department of Technische Universität Wien . His research focuses on reproducibility in machine learning and information retrieval, with particular emphasis on query generation, data citation, and evolving database schemas. Current projects: FAIR-AI (2024–2026) , HumRec (2021–2025) , and DoSSIER (2019–2024) Collaborations: Works with Andreas Hanbury , Andreas Rauber, and others Research interests include large language models for scientific applications, temporal information retrieval, and FAIR data principles. His recent publications examine reproducibility challenges across machine learning, systematic literature reviews, and environmental data management. Supervisions : Mentors students working on topics like data sovereignty, multilingual fact-checking, and quality indicators for data management plans. Collaborates on the DBRepo semantic repository framework.

Annalena Ulschmidمشاهده پروفایل

پژوهشگر

• Physically-based Rendering and Simulation
• Statistics / User Studies
• Incremental Path Tracing for Scene Editing
• +۴ مورد دیگر

Annalena Ulschmid is a PreDoc Researcher at the Vienna University of Technology , affiliated with the Faculty of Informatics and the Computer Graphics group. She holds an MSc and contributes to research in physically-based rendering , user studies , and real-time graphics algorithms . Research spans incremental path tracing , alternative rendering datastructures , and probabilistic models for participating media . Key projects include ACD (Automated Prioritization for Context-Aware Re-rendering) (2020–2028) and IVILPC (Single-Exemplar Lighting Style Transfer) (2023–2026). She teaches courses like Computer Graphics , Fundamentals of Computer Graphics , and Rendering . Publications focus on real-time rendering , VR accessibility , and lighting style transfer . Her work has been recognized with the Best Student Paper Award at VISIGRAPP 2025 . Notable collaborations include Michael Wimmer and Katharina Krösl . Further details are available via her ORCID profile and GitHub repository .

• model-based reasoning
• qualitative reasoning
• software engineering
• +۴ مورد دیگر

Franz Wotawa is an Associate Professor at the Institute of Information Systems Engineering, Faculty of Informatics, TU Wien. His primary research areas include model-based and qualitative reasoning, software engineering, configuration, verification and validation, intelligent agents, and theorem proving. He is affiliated with the Databases and Artificial Intelligence research group (E192-02) and can be contacted at franz.wotawa@tuwien.ac.at. Wotawa's research spans model-based reasoning for diagnosis and testing in service-oriented architectures, software verification techniques, and intelligent agent applications. His work bridges theoretical theorem proving with practical SOA validation challenges, emphasizing configuration management and qualitative analysis methods within distributed systems. His 2013 publications reveal a strong focus on risk-aware SOA testing methodologies, integrating criticality metrics with business impact analysis to optimize testing frameworks. These works demonstrate consistent exploration of grey-box testing approaches and reliability assessment in complex service compositions. Wotawa has supervised the following Master's theses: Hybrid approach for model-based random testing (J. P. Wallner, 2010) Model transformation from UML state machines to input/output symbolic transition systems (C. Thurnher, 2008) Entwurf und Implementierung eines wertebasierten Diagnose-Modells für VHDL-Programme (F. Bruckner, 2003) Constraint satisfaction problems: hybrid decomposition and evaluation (M. Hutle, 2002) Framework für generische Suche in einem komplexen Objektmodell am Beispiel von See Your Client (B. Schmidt, 2002) System analysis and monitoring (H.-P. Petek, 2001) He has led significant research projects: D-Flat (2013-2017): Dynamic programming via tree decompositions START (2014-2022): Argumentation semantics analysis Audit 4 SOAs (2011-2014): Criticality-based SOA testing INDENICA (2010-2013): SOA testing frameworks

• Health Services Research
• Perioperative Nursing
• Health Care
• +۵ مورد دیگر

Prof. Nadja Nestler is a full professor at the Institute of Nursing Science and Practice at Paracelsus Medical University. Her research focuses on health services research, perioperative nursing, pain management, and the application of sensor technologies in long-term care. She leads major projects like SensiCare (fall/pressure ulcer prevention) and PhideLA (phytotherapy in elderly care), involving interdisciplinary teams across nursing, medicine, and engineering. Recently presented at international conferences on topics like digital health literacy and non-pharmacological pain management strategies. Holds editorial roles in pain research journals and actively promotes evidence-based practice implementation in clinical settings. Research interests include: Interprofessional healthcare collaboration Sensor-based care innovations Herbal medicine in long-term care Postoperative pain education Evidence-based practice implementation Recent projects (2023-2026) focus on: Sensor technology adoption in nursing homes Standardizing perioperative pain services Optimizing herbal medication protocols Patient health literacy improvement Has over 129 publications and 15 active research projects. Active in both EU and national funding initiatives.

• Computational Sustainability
• Cloud Computing
• Edge Computing
• +۳ مورد دیگر

Ivona Brandic is a Full Professor of High Performance Computing Systems at TU Wien's Institute of Information Systems Engineering, leading the HPC Research Group. She specializes in Computational Sustainability, Cloud/Edge Computing, and Quantum-Classical Systems. Her roles include Head of the Computational Sustainability Research Unit and membership in TU Wien's Faculty Council. She teaches courses such as AI/ML in Climate Change and Hybrid Quantum-Classical Systems. Her research focuses on sustainable IT, energy-efficient systems, and hybrid quantum-classical workflows. Projects include computational sustainability initiatives funded by the Austrian Science Fund (FWF) and industry partnerships like the Virtual Shepherd project. She has contributed to over 50 publications, emphasizing edge computing, quantum algorithms, and HPC optimization. Notable contributions include developing frameworks like RIGOLETTO for hybrid scientific workflows and FRESCO for edge offloading. Her work bridges theoretical advancements with practical applications in environmental monitoring and energy efficiency.

• Automated Reasoning
• Knowledge Representation and Reasoning
• Computational Logic
• +۵ مورد دیگر

Uwe Egly is an Associate Professor at the Department of Knowledge-Based Systems, Faculty of Informatics, Technische Universität Wien (TU Wien). His research focuses on automated reasoning, proof theory, knowledge representation, and computational logic, with a strong emphasis on quantified Boolean formulas (QBFs), argumentation frameworks, and applications of AI in engineering. He leads projects funded by the Austrian Science Fund (FWF) and the Vienna Science and Technology Fund (WWTF), including the Boolean project (2011–2019) and FAME (2011–2014). Egly is known for developing QBF solvers like DepQBF and contributing to SAT-solving techniques. He teaches courses such as Abstract Argumentation , Formal Methods in Computer Science , and Quantum Computing . His research interests span proof complexity, satisfiability checking, and AI-driven algorithms for path planning. He has advised numerous students on theses involving quantum algorithms, QBF solver optimizations, and argumentation frameworks. Egly’s work bridges theoretical computer science with practical applications, including contributions to deformation monitoring systems and circuit synthesis using SAT-based methods. He is involved in international workshops and conferences, such as SAT, FMCAD, and Dagstuhl Seminars, and has edited proceedings for events like SAT 2014 . His collaborations include projects on scenario-based testing of UML diagrams and semantics-aware model versioning. Egly’s interdisciplinary approach integrates logic, artificial intelligence, and computational methods to solve complex theoretical and applied problems.

• 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.

Clemens Heitzingerمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۶ مورد دیگر

Associate Professor Clemens Heitzinger holds a position at the Technical University of Vienna (TU Wien) within the Department of Informatics (Computer Science), specifically in the Research Unit Machine Learning under the Institute of Information Systems Engineering. His research focuses on machine learning, reinforcement learning, Bayesian methods, and their applications in healthcare, energy systems, and nanotechnology. He leads the TUW Center for Artificial Intelligence and Machine Learning (CAIML) and has received prestigious awards including the START Prize 2013 and an Erwin Schrödinger Fellowship. Education and Academic Background: Clemens earned his doctoral degree (Dr.techn.) in technical mathematics and holds a Diplom-Ingenieur (Master’s equivalent). His academic career includes roles at the University of Cambridge, Purdue University, and Arizona State University. Research Interests: Combines mathematical modeling with machine learning to address challenges in nanoscale sensor design, medical decision support systems, and energy optimization. Key areas include stochastic differential equations, PDE-based modeling, and reinforcement learning for critical applications like sepsis treatment and renewable energy control. Publications: His work spans high-impact journals such as Nonlinear Dynamics , EClinicalMedicine , and Sensors , with a focus on interdisciplinary applications of AI. Recent trends show strong contributions to reinforcement learning in healthcare and energy systems, as well as novel methods in fingerprint recognition and cryogenic sensor control. Awards: Recognized for his START Prize project on PDE models for nanotechnology and his contributions to AI ethics and medical AI. The 2024 nomination for the Austrian National Technology Prize highlights his work on energy system optimization via reinforcement learning. Advising and Grants: Supervises over 20 graduate and undergraduate students, with notable projects in reinforcement learning for sepsis therapy, cryogenic sensor control, and fingerprint analysis. Leads funded initiatives like the FFG-backed RELY project on sustainable energy systems and the WWTF-funded Mathematics and Nanosensors initiative. Labs and Teams: Co-directs CAIML and collaborates with the Medical University of Vienna on AI-driven healthcare solutions. Active in international collaborations through the AIDA doctoral academy and the Vienna Center for Partial Differential Equations.

• Model Engineering
• Electronic Data Interchange
• E-Business
• +۶ مورد دیگر

Christian Huemer is an Associate Professor and Dean of Academic Affairs in the Department of Business Informatics at TU Wien. His research focuses on inter-organizational systems, BIM implementation in tunnel construction, and model-driven integration of manufacturing standards. He holds dual academic titles (Ao.Univ.Prof. Mag.rer.soc.oec. Dr.rer.soc.oec.) and leads the TU Wien's academic affairs while maintaining active research in information systems engineering. Education & Roles: Dean of Academic Affairs, Faculty of Informatics Associate Professor for Business Informatics (E194-03) Research Interests: Prof. Huemer's work spans electronic data interchange (EDI), service sciences, and BIM-based digital transformation in construction. His recent projects include TransIT (tunnel BIM integration) and HoVer (standards framework for Industry 4.0). He emphasizes interdisciplinary collaboration, particularly in integrating BIM with construction processes and manufacturing systems. Publications & Projects: Over 100+ publications since 2004, with recent focus on tunneling digitalization (e.g., BIM compliance checks, data drops) and manufacturing integration (ISA-95/OPC UA). Active in EU-funded projects like BIM im Tunnelbau (2020–2023) and InteGra 4.0. Advising & Grants: Supervised 20+ theses on topics like AR in tunneling, BIM automation, and resilient manufacturing. Key grants include Austrian Research Promotion Agency (FFG) projects on ERP systems ( REAlist ) and tunnel documentation tools ( ebTransfer ). Labs/Teams: Leads TU Wien's Business Informatics Group, collaborating with industry partners such as ÖBB (Austrian Federal Railways) and FMA (Financial Market Authority). Involved in BIM standardization efforts with UN/CEFACT and ISO.

• Blockchain Technology
• Distributed Systems
• Coordination Middleware
• +۴ مورد دیگر

Eva Kühn is an Associate Professor at TU Wien's Faculty of Informatics, specializing in Coordination Systems and Space-Based Computing. She leads the Compilers and Languages department and focuses on Blockchain Technology, Distributed Systems, and Swarm Intelligence. Roles & Affiliations: Associate Professor, TU Wien Research Lead in Coordination Middleware Principal Investigator in projects like ITSM-PEER and Formal Verification of DNNs Research Interests: Blockchain-based systems for decentralized applications Swarm intelligence algorithms for P2P networks Space-based computing architectures for distributed systems Coordination patterns for multi-cloud auto-scaling Projects & Grants: "Formal Verification of Deep Neural Networks" (2024-2027, OeAD) "ITSM-PEER" (2014-2015, FFG) "LOPONODE Middleware" (2012-2014, FFG) Students & Supervision: Advised over 30 students in areas like blockchain frameworks, swarm algorithms, and distributed systems. Labs & Teams: Leads the Peer Model research group developing coordination middleware and space-based computing frameworks.