Find Academic Supervisors

Bernhard HollausView profile

Lecturer

• Sports Technology
• Machine Learning
• Sensor Systems
• +3 more

Bernhard Hollaus is a Lecturer at the Management Center Innsbruck (MCI), where he teaches and conducts research in mechatronics, health technologies, and sports engineering. He leads the Health Tech Research Cluster at MCI and has held roles such as CEO of Auto Hollaus and Project Manager at Siemens AG. His academic affiliations include the University of Innsbruck, where he earned a PhD in Sport Science, and FH Technikum Wien for his MSc and BSc in Industrial Elec and Elec. His research focuses on applying machine learning and sensor systems to sports technology, including golf club impact detection, cycling cadence analysis, and American football training automation. He has supervised over 100 bachelor and master theses on topics ranging from VR sledding simulators to neural networks in sports analytics. Key awards include the Cluster Award Mechatronics (2016), Higher Degree Research Student Prize (2020), and distinctions from the University of Innsbruck (2021) and Technikum Wien (2011–2012). His work bridges engineering and sports science, with notable contributions to wearable sensor applications and sports safety education. Notable projects include developing the VRodel sledding simulator, a golf club impact detection system, and advanced passing machines for American football training. Collaborations span academic institutions and industry partners, reflecting his dual expertise in technical innovation and sports performance.

• Sustainable Consumption
• Food Waste Reduction
• Climate Change Mitigation
• +3 more

Karin Dobernig is a Researcher at the Institute for Sustainability, specializing in sustainable consumption, food waste reduction, and climate change mitigation. Her work focuses on interventions to reduce meat consumption, household food waste practices, and policy implications of environmental behaviors. She has led and collaborated on projects like 'bewusstkaufen' and 'biscuit4all,' aiming to foster carbon-friendly diets and sustainable living practices. Her research spans interdisciplinary topics including consumer behavior, environmental policy, and urban agriculture. Dr. Dobernig has received the prestigious Ars Docendi Staatspreis für exzellente Hochschullehre (2019) for excellence in university teaching, reflecting her contributions to pedagogical innovation. Her publications analyze systemic approaches to sustainability, such as cloud computing’s energy impact and the intersection of consumption patterns with economic growth. Recent projects include developing web portals for climate-friendly living and exploring grassroots initiatives to mitigate food waste. Collaborations span academic and policy domains, addressing both behavioral and structural challenges in achieving environmental sustainability.

• Security
• Intrusion Detection
• Malware Detection
• +2 more

Christopher Krügel is a Professor at Technische Universität Wien, affiliated with the Department of Automation Systems (E191-03). His research focuses on Security, Intrusion Detection, Malware Detection, Operating Systems, and Networks. He leads multiple research projects and has supervised 20+ graduate students. His research explores malware analysis techniques, web security vulnerabilities, hardware-level attacks (e.g., Rowhammer), and privacy threats in mobile/social systems. Recent work examines ARM TrustZone security and machine learning limitations in malware detection, while earlier publications cover P2P networks, phishing defenses, and binary analysis. Awards: Wirtschaftskammerpreis (2005) EIB Scientific Award for Excellent Diploma Thesis (2000) Grants & Projects: Includes SECoverer (web app security, 2008-2011), WOMBAT (malware observatory, 2008-2011), Pathfinder (malware analysis, 2007-2009), and 5+ other funded initiatives focusing on binary analysis, pervasive security, and malware detection. He leads the Automation Systems research group, collaborating with institutions like European Commission and Austrian Science Fund.

Jan MalyView profile

Assistant Professor

• Computational Social Choice
• Logic and Knowledge Representation
• Participatory Budgeting
• +3 more

Jan Maly is an Assistant Professor at the Institute for Data, Process and Knowledge Management (DPKM) of the Vienna University of Economics and Business (WU Wien) and a postdoctoral researcher in the Database and Artificial Intelligence Group (DBAI) of TU Wien. He is currently on parental leave until September 15, 2024. His educational background includes mathematics and philosophy studies at Würzburg University, specialization in mathematical logic at Vienna University, and a PhD in computer science from TU Wien completed in 2020. Dr. Maly's research focuses on Computational Social Choice (COMSOC), Logic and Knowledge Representation, with particular emphasis on Participatory Budgeting and Fairness in Online Decision Making. His work aims to develop tools that help people make better decisions, individually or as a group, by investigating non-standard voting frameworks and computational complexity questions. His recent work addresses the critical problem that simple majority voting in online environments marginalizes minority opinions and reinforces filter bubble effects. Analysis of his publication record reveals a strong focus on developing fair voting mechanisms for participatory budgeting and online decision-making. His work bridges theoretical computational social choice with practical applications, particularly in developing algorithms that ensure proportional representation while maintaining computational feasibility. The publications demonstrate increasing sophistication in handling multi-issue decisions, perpetual voting scenarios, and complex budget allocation problems. netidee SCIENCE grant (2024, ~400,000 euros) Erwin Schrödinger Fellowship from FWF Dr. Maly has co-supervised PhD students including Simon Rey and Michael Bernreiter. His collaborative work spans multiple institutions, including the University of Amsterdam where he worked from September 2021 to April 2023 as a member of the COMSOC Group. He is also the co-founder of the European Digital Democracy Network, which organized its first conference in April 2024 bringing together over sixty scientists and practitioners.

• Artificial Intelligence
• Multi-Agent Systems
• Computational Social Choice
• +3 more

Oliviero Nardi is a Universitätsassistent (Researcher) at the Vienna University of Technology (TU Wien) , affiliated with the Faculty of Informatics and the Department of Databases and Artificial Intelligence . His research focuses on computational social choice, multi-agent systems, and fair allocation mechanisms. He holds an MSc and currently pursues doctoral studies (PreDoc Researcher role). Research Interests : Nardi explores interdisciplinary topics including argumentation frameworks, voting systems, cybersecurity, and algorithmic fairness. His work bridges theoretical computer science with practical applications in decision-making processes and multi-agent environments. Recent Projects : HolisticPB (2021–2024) : Investigates fair allocation of indivisible resources in repeated settings. SuDeMa (2019–2025) : Examines free-riding behaviors in multi-issue decision-making scenarios. Teaching : He instructs courses like Formal Methods in Computer Science and Preferences in Artificial Intelligence , reflecting his expertise in theoretical foundations and AI applications. Key Research Trends : His publications emphasize algorithmic solutions for collective decision-making, cybersecurity etiology, and graph-based justification mechanisms. Recent work merges voting systems with argumentation theory to model online discussions. Grants & Lab Affiliations : Engaged in funded projects HolisticPB and SuDeMa, contributing to collaborative research environments addressing societal challenges in AI and decision science.

• Cyber-Physical Systems
• Runtime Verification
• Formal Methods
• +5 more

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)

• Machine Learning
• Graph Neural Networks
• Recommender Systems
• +3 more

David Penz is a PreDoc Researcher at Technische Universität Wien (TU Wien), affiliated with the Faculty of Informatics and the Department of Machine Learning. He teaches courses such as Introduction to Machine Learning , Machine Learning Algorithms and Applications , and Theoretical Foundations and Research Topics in Machine Learning . His research focuses on advancing Graph Neural Networks (GNNs), particularly for outerplanar graphs, and developing ethical AI systems in recommendation engines. His work emphasizes data privacy, fairness in AI, and music information retrieval. Key projects include the StruDL initiative (2023–2027), exploring maximally expressive GNN architectures. He has supervised a diploma thesis on antibody-antigen binding affinity prediction using geometric deep learning. Recent publications highlight contributions to unlearning sensitive user attributes in recommendations and emotion-aware music recommendation systems. His academic output spans conferences like NeurIPS, SIGIR, and ICMR, demonstrating expertise in both theoretical and applied machine learning domains. He actively participates in academic challenges, such as the ACM Recommender Systems Challenge 2021, showcasing lightweight XGBoost approaches.

Majid SalimibeniView profile

Research Fellow

• High Performance Computing
• Parallel Computing
• Message Passing in Large-scale HPC systems
• +6 more

Majid Salimibeni is a PostDoc Researcher and Research Fellow at Vienna University of Technology (TU Wien) , affiliated with the Parallel Computing research unit. He previously held a Postdoc position at the Department of Computer Science at the University of Salerno, Italy (2024), and has been a visiting researcher at TU Wien (2023). Currently based in Vienna, Austria, his work focuses on High Performance Computing (HPC) and its interdisciplinary applications. Education: Ph.D. in Computer Science, University of Salerno, Italy (2020–2024) M.Sc. in Computer Engineering (Software), Shiraz University, Iran (2017–2020) B.Sc. in Computer Engineering (Software), University of Birjand, Iran (2012–2016) Salimibeni's research explores High Performance and Parallel Computing , with a focus on optimizing GPU communication via NCCL , improving energy efficiency in heterogeneous computing environments , and enhancing MPI collective algorithms for large-scale systems. His work bridges HPC infrastructure with emerging applications in Distributed Deep Learning and AI . Recent publications highlight his expertise in GPU frequency scaling , NCCL profiling , and process arrival pattern analysis to improve distributed system performance. These works appear at top conferences like IPDPS , Cluster Computing , and ASHPC . Scientific Awards: Best Paper Award, Bench 2022 Salimibeni contributes to academic committees as a program committee member for IEEE ICPP 2025, ISC 2025, and BigHPC 2024, and serves on artifact evaluation committees for ASPLOS and CF conferences.

• Multimedia Systems
• News Distribution Networks
• Information Systems Engineering
• +3 more

Markus Schranz is an External Lecturer in the Information Systems Engineering department at TU Wien. His expertise focuses on multimedia systems, news distribution networks, and collaborative network architectures. He has contributed to foundational works on pan-European news distribution systems and multimedia content management. He teaches the course Web Application Engineering & Access Management (2024W). His research emphasizes scalable media distribution, cross-platform adaptation, and collaborative network design. While no awards are explicitly listed, his work reflects significant contributions to media infrastructure engineering. His publications span technical encyclopedias and conference proceedings, addressing both theoretical frameworks and practical implementations in multimedia news systems.

• Music Recommender Systems
• Machine Learning
• Artificial Intelligence
• +3 more

Shahrzad Shashaani is a PreDoc Researcher at Vienna University of Technology , affiliated with the College of Engineering. Her work spans multiple domains including artificial intelligence, recommender systems, and biomedical applications. Research Focus : Explainability in music recommender systems, sequential recommendation algorithms, and AI-driven diagnostics Projects : HumRec (2021–2025) - Explainability in Music Recommender Systems Technical Expertise : Machine learning, network security, autonomous vehicles, and biomedical sensor analysis Recent publications highlight her contributions to contrastive learning in music recommendation, self-supervised intrusion detection, and Bayesian frameworks for graphene transistor-based enzyme activity analysis. She also explores AI applications in rapid pandemic screening protocols. Her academic career combines engineering rigor with interdisciplinary applications, particularly in health analytics and intelligent systems. Current responsibilities include course instruction in "Experiment Design and Execution" (VU 194.192).

• Computer Networks
• Network Engineering
• Pervasive Computing
• +2 more

Manfred Siegl serves as University Lecturer in Information Systems Engineering and Computer Engineering at Technische Universität Wien. His teaching portfolio includes Computer Networks, Network Engineering, and Pervasive and Mobile Computing courses. Siegl supervises student research on server application optimization for renewable energy data centers and ITIL configuration management systems. Professional interests focus on network infrastructure and distributed computing frameworks.

• Fault Tolerance
• Safety-Critical Systems
• Distributed Algorithms
• +4 more

Wilfried Steiner is an Associate Professor at the Institute for Computer Engineering within the Faculty of Informatics at TU Wien. His research focuses on fault tolerance, safety-critical systems, distributed algorithms, and formal analysis in cyber-physical systems. He actively teaches courses and supervises master's theses, with a bachelor thesis course scheduled for the 2025 winter semester. Steiner's research centers on dependable communication systems, particularly time-triggered architectures for safety-critical applications. He has pioneered solutions for fault containment, system startup, and Byzantine faults in distributed real-time systems. His work extends to fog computing for industrial IoT through the FORA project (2017-2021), addressing resource constraints in automotive and industrial environments while maintaining deterministic behavior. His publication timeline reveals consistent innovation: early foundational work (2002-2007) established protocols for fault-tolerant time-triggered communication, evolving toward fog computing platforms (2020-2021) for industrial IoT. Recent publications emphasize automotive applications (TTEthernet/AUTOSAR) and IoT channel synchronization, demonstrating sustained relevance in real-time system design. Steiner has supervised nine master's theses between 2018-2024, including Walser (2024) on time-aware container virtualization, Reisinger (2022) on distributed clock synchronization, and Schorn (2021) on automotive fail-operational strategies. His research is supported by projects like FORA, which developed fog computing solutions for industrial IoT constraints. As a core member of the Cyber-Physical Systems research group (E191-01), Steiner contributes to TU Wien's leadership in safety-critical embedded systems, focusing on integration of computing, networking, and physical processes for automotive and industrial applications.

• Computer Vision
• Machine Learning
• Flow Cytometry
• +5 more

Lisa Magdalena Weijler is a PreDoc Researcher at the Computer Vision Lab, Faculty of Informatics, Technical University of Vienna, with secondary affiliation to Immunological Diagnostics at St. Anna Children’s Cancer Research Institute. She leads research at the intersection of computer vision and medical diagnostics, specializing in flow cytometry data analysis for leukemia. Institutional Affiliations : Technical University of Vienna (Principal), St. Anna Children’s Cancer Research Institute (Collaborative) Academic Focus : Applying UMAP, transformers, and anomaly detection algorithms to automate minimal residual disease quantification in AML Research Interests span computer vision and machine learning applications in medical imaging , particularly for hematological malignancies . Her work addresses challenges in automated cell classification , dimensionality reduction , and medical data interpretation . Scientific Contributions include: Developing UMAP-based anomaly detection frameworks for AML MRD Creating transformer architectures for flow cytometry analysis Advancing self-explainable medical AI systems Project Involvement includes the MyeFLOW (2020–2025) and AutoFLOW initiatives, which integrate machine learning with clinical hematology for automated diagnostics.

Martin WeiseView profile

Researcher

• Secure Data Infrastructures
• Virtual Research Environments
• FAIR Data Principles
• +3 more

Martin Weise is a PreDoc Researcher at the Data Science Research Unit (DS-IFS) within the Department of Information Systems Engineering at Vienna University of Technology (TU Wien). His work focuses on secure data infrastructures, virtual research environments, FAIR data principles, and research data repositories. MSc in Software Engineering & Internet Computing (2022) BSc in Software & Information Engineering (2019) Research interests include: FAIR Data Implementation Trusted Research Environments Secure Data Infrastructures Virtual Research Environments Data Preservation Interoperability Solutions Recent publications highlight his contributions to: DBRepo: Semantic Repository Systems Trusted Research Environments Cyber Situational Awareness FAIR Principle Implementation Secure Data Visiting Data Preservation Frameworks

Stefan SchulteView profile

Associate Professor

• Data Engineering
• Elastic Computing
• Internet of Things (IoT)
• +6 more

Stefan Schulte is an Associate Professor at TU Wien's Faculty of Informatics, holding a habilitation in Computer Science. His roles include Vice Dean of Academic Affairs for Business Informatics. He earned a diploma in economics, bachelor's in Computer Science from the University of Oldenburg, and master’s from the University of Newcastle. He completed his PhD at TU Darmstadt and habilitation at TU Wien. His research focuses on data engineering, elastic computing, IoT, QoS/QoE, fog computing, and blockchain for data provenance. He leads projects like SIMPLI-CITY and CREMA, coordinating EU-funded initiatives. Education: Diploma in Economics (Oldenburg), BSc Computer Science (Oldenburg), MSc Information Technology (Newcastle), PhD (TU Darmstadt, 2010), habilitation (TU Wien, 2018). Research Interests: Data stream processing in IoT and smart systems Elastic computing frameworks for cloud/fog environments Blockchain applications for verification and interoperability QoS optimization in distributed systems Smart mobility and manufacturing systems Publications: Over 100 refereed papers in top venues like IEEE Transactions on Services Computing and VLDB. Recent work emphasizes fog computing architectures and cross-blockchain solutions. Awards: Multiple best paper awards (ESOCC 2020, Blockchain-2020), teaching recognition, and prestigious awards for student theses (OCG Incentive Award, Vienna City Awards). Grants/Advising: Coordinated EU projects SIMPLI-CITY and CREMA, supervised over 30 theses. Active in PhD supervision for Cyber-Physical Production Systems. Labs/Teams: Director of TU Wien's Distributed Systems Group (DSG), leading Fog Computing and Blockchain research initiatives.