جستجوی استاد راهنمای دانشگاهی

• Security
• Intrusion Detection
• Malware Detection
• +۲ مورد دیگر

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.

• Computational Social Choice
• Voting Theory
• Multi-winner Elections
• +۵ مورد دیگر

Martin Lackner is an Associate Professor in the Databases and Artificial Intelligence department at the Faculty of Informatics, Vienna University of Technology. His research focuses on computational social choice, voting theory, and algorithmic decision making, with particular expertise in multi-winner elections, approval-based voting systems, and participatory budgeting. He maintains an active research program with continuous publications in top AI and theoretical computer science venues. Lackner's research interests center on the theoretical foundations and practical implementations of fair decision-making systems. His work bridges theoretical computer science with practical applications in democratic processes, examining how computational methods can enhance fairness, representation, and efficiency in collective decision making. He has made significant contributions to understanding the computational complexity of voting rules, developing new algorithms for preference aggregation, and establishing axiomatic properties for multiwinner election methods. His recent publications demonstrate a consistent focus on fairness in long-term decision processes, with particular attention to perpetual voting systems, participatory budgeting mechanisms, and approval-based multiwinner rules. The research shows strong theoretical grounding combined with practical implementation considerations, as evidenced by his development of the abcvoting Python package for implementing approval-based voting rules. Lackner has received continuous funding for his research through multiple projects: SuDeMa (2019–2025): Algorithms for Sustainable Group Decision Making (Austrian Science Fund) FAIR (2013–2018): Fixed-Parameter Tractability in Artificial Intelligence and Reasoning (Austrian Science Fund) HINT (2012–2017): Heterogenous Information Integration (Austrian Science Fund) SEE (2012–2016): SPARQL Evaluation and Extensions (Vienna Science and Technology Fund) As an academic advisor, Lackner has supervised doctoral and master's students including J. Maly (2020) on ranking sets of objects and B. Krenn (2019) on algorithms for implicit delegation to predict preferences. His work appears in premier venues including AAAI, IJCAI, AAMAS, and the Journal of Artificial Intelligence Research, demonstrating both theoretical depth and practical relevance to democratic processes and collective decision making.

• 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)

Stefan Ohrhallingerمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Geometry Processing
• Surface Reconstruction
• +۳ مورد دیگر

Stefan Ohrhallinger is a Senior Researcher at TU Wien's Institute of Visual Computing and Human-Centered Technology. He holds a PhD from Concordia University (2013) and a Master's in Business Informatics from Johannes Kepler University Linz (1997). His research focuses on surface reconstruction, point-based graphics, and geometry processing with applications in computer graphics and virtual/augmented reality. Education: PhD in Computer Science, Concordia University (2013) Master of Business Informatics, Johannes Kepler University Linz (1997) Affiliations: Principal Investigator for grants totaling €1.7M+ (e.g., WWTF Modeling the World at Scale, FWF Superhumans) Teaching: 'Modeling in Computer Graphics' and 'Scientific Writing' courses His work emphasizes robust algorithms for reconstructing curves/surfaces from sparse/noisy data, with contributions to occlusion-aware processing and large-scale point cloud handling. Notable achievements include the Best Poster Award at Eurographics 2012 and over 30 peer-reviewed publications. Grants & Projects: WWTF 'Modeling the World at Scale' (2020–2026, €578k) FWF 'Superhumans: Walking Through Walls' (2019–2023, €333k) IPA 'Photogrammetry made easy' (2020–2022, €50k)

• Condensed Matter Physics
• Quantum Many-Body Systems
• Theoretical Physics
• +۳ مورد دیگر

Miriam Patricolo is a Researcher at TU Wien's Department of Solid State Physics within the Faculty of Physics. Her work focuses on theoretical condensed matter physics, particularly quantum many-body systems and functional renormalization group (fRG) techniques. She contributes to the Quantenspinsysteme P7 project (2024–2027) and actively publishes on Schwinger-Dyson equations and the Hubbard model. Her research explores pseudogap phenomena in strong-coupling regimes and integrates machine learning methodologies. Education details are not explicitly provided. Professional activities include organizing the 2025S Journal Club on Solid State Physics: Theory and Experiment. Her presentations at international conferences (e.g., SciPost Physics, DPG 2024) and workshops highlight her expertise in renormalization group methods and quantum materials.

• Machine Learning
• Graph Neural Networks
• Recommender Systems
• +۳ مورد دیگر

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.

• Information Retrieval
• Data Science
• Medical Informatics
• +۳ مورد دیگر

Florina Piroi is a Senior Researcher at the Technische Universität Wien (TU Wien), affiliated with the Faculty of Informatics and the Department of Data Science. She holds the role of Senior Scientist in Data Science and is a Substitute Member of the Curriculum Commission for Business Informatics. Her primary research focuses on information retrieval, medical informatics, natural language processing, and patent text mining. She leads projects such as the CLEF-IP evaluation lab and contributes to initiatives like the DoSSIER and OS Trails research programs. Key research interests include longitudinal evaluation of machine learning models, reproducibility in NLP tasks, and knowledge graph applications in manufacturing. She has supervised PhD student Anindita M. Ningtyas, whose work addresses medical terminology accessibility for laypeople. Piroi collaborates on interdisciplinary projects, such as designing health datasets from medical forums and improving search systems for evolving corpora. Her notable contributions include benchmarking frameworks for IR systems, semantic translation tools for industry, and methods for analyzing electrical systems. She has published over 50 peer-reviewed articles in venues like SIGIR, CLEF, and ECIR, emphasizing practical applications in both academic and industrial contexts.

• Artificial Intelligence
• Database Systems
• Machine Learning
• +۱ مورد دیگر

Domenec Puig is a Visiting Scientist at the Faculty of Informatics, Technische Universität Wien (TU Wien), affiliated with the Databases and Artificial Intelligence department (E192-02). His research interests include artificial intelligence, database systems, and machine learning. He can be contacted via domenec.puig@tuwien.ac.at or through his university profile at informatics.tuwien.ac.at .

• Parameterized Complexity
• Graph Theory
• Artificial Intelligence
• +۳ مورد دیگر

Mathis Teva Rocton is a PreDoc Researcher at the Department of Algorithms and Complexity, Technische Universität Wien (TU Wien). He holds an MSc and the title of Univ.Ass. His research focuses on parameterized complexity, graph theory, and theoretical computer science with applications in artificial intelligence. Rocton is involved in projects such as 'Parameterisierte Analyse in der Künstlichen Intelligenz' (2021–2026) and 'Parameterized Graph Drawing' (2023–2027). His recent work explores twin-width concepts, feedback edge parameters, and neural network training complexity. Notable publications include contributions to IPEC 2024, STACS 2024, and NeurIPS 2023. He has supervised diploma theses, including David Jäger's work on 'Turbocharging twin-width heuristics with SAT' (2024). Rocton's research bridges algorithmic theory and practical applications in AI.

Majid Salimibeniمشاهده پروفایل

پژوهشگر ارشد

• High Performance Computing
• Parallel Computing
• Message Passing in Large-scale HPC systems
• +۶ مورد دیگر

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.

• Formal Methods
• Software Engineering
• Machine Learning
• +۳ مورد دیگر

Eva Schenner is a Researcher at TU Wien's Department of Software Engineering, affiliated with both the Office Services and eduLAB teams. She specializes in formal methods, program analysis, and machine learning applications in software engineering. Her work focuses on testing tools, smart contract certification, and probabilistic models for material design. She has led multiple high-profile projects, including the FWF-funded 'Types4Strings' (2024–2027), WWTF's 'Effective Formal Methods for Smart-Contract Certification' (2023–2027), and an Amazon Research Award project on testing Dafny (2024–2027). Her research also spans metamorphic testing of machine-learning models and probabilistic hybrid models for material design. Education: Mag.a phil. (Master of Philosophy) Her awards include Amazon and Google Research Awards, and grants from the WWTF and Max-Planck-Gesellschaft. She actively contributes to initiatives like Bebras, promoting computational thinking education since 2007. Labs/Teams: Involved in the Software Engineering group within the Faculty of Informatics, collaborating with industry partners like Amazon and Google.

• Music Recommender Systems
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

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).

• Algorithms
• Graph Theory
• Computational Complexity
• +۲ مورد دیگر

Manuel Sorge is a Research Fellow in the Faculty of Informatics at Vienna University of Technology, affiliated with the Algorithms and Complexity group (E192-01). His research focuses on parameterized algorithms, graph theory, and computational complexity, with particular emphasis on graph modification problems, clustering algorithms, and combinatorial optimization. His work spans several areas including graph drawing, algorithmic game theory, and machine learning. Research interests include: Parameterized complexity and kernelization Graph algorithms and combinatorial optimization Algorithmic graph theory and discrete mathematics Computational geometry and graph drawing Supervises master's students in algorithm design and complexity theory. Current projects include Parameterized Graph Drawing (2023–2027).

• Computer Vision
• Machine Learning
• Visual Computing
• +۴ مورد دیگر

Rafael Sterzinger is a PreDoc Researcher at the Institute of Visual Computing and Human-Centered Technology within TU Wien's Faculty of Informatics. Holding a BSc and MSc from ETH Zurich, he teaches courses in Computer Vision and Machine Learning for Visual Computing while pursuing his PhD. Education MSc in Computer Science, ETH Zurich BSc in Computer Science, ETH Zurich Research Interests Sterzinger's work centers on Computer Vision and Visual Computing , specializing in segmentation techniques for historical documents, cultural heritage artifacts, and WiFi-based sensing applications. His research integrates human-in-the-loop systems with deep learning for refinement tasks, explores reinforcement learning for building automation, and develops domain adaptation methods for cross-scenario human activity recognition. Key domains include Etruscan art digitization, through-wall imaging, and historical map analysis. Research Trends Analysis of Sterzinger's 2023-2025 publications reveals three dominant threads: (1) Few-shot segmentation for historical materials using foundation models, (2) Human-guided refinement pipelines that merge algorithmic precision with user input, and (3) Wireless sensing applications leveraging WiFi channel state information for through-wall imaging and activity recognition. His work consistently addresses data scarcity through innovative adaptation techniques while maintaining real-world applicability in cultural heritage and smart infrastructure. Projects EtMirA (2021-2027): Through-Wall Imaging Based on WiFi Channel State Information Blindsight (2023-2025) MAIJA (2024-2025): Closing the Gap in Human Behavior Analysis via Trimodal Data Synthesis KIIS (2021-2023): Deep Segmentation for Extracting Art from Ancient Etruscan Mirrors Advising and Grants No advisees are documented. Sterzinger participates in multiple externally funded projects including EtMirA's €1.2M infrastructure grant for cultural heritage digitization and MAIJA's interdisciplinary initiative synthesizing audio-visual-tactile data for behavior modeling, demonstrating sustained grant acquisition in visual computing domains.

• Computer Vision
• Machine Learning
• Flow Cytometry
• +۵ مورد دیگر

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.