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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.

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

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.

• Information Retrieval
• Data Science
• Medical Informatics
• +3 more

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 .

• Artificial Intelligence
• Database Systems
• Machine Learning
• +1 more

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.

• Parameterized Complexity
• Graph Theory
• Artificial Intelligence
• +3 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.

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

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.

• Formal Methods
• Software Engineering
• Machine Learning
• +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).

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

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).

• Algorithms
• Graph Theory
• Computational Complexity
• +2 more

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
• Visual Computing
• +4 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.

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

Klaus Weinbauer is a Researcher at Technische Universität Wien (TU Wien) affiliated with the Department of Informatics . His work focuses on applying machine learning techniques to diverse domains including chemical reaction analysis, software engineering education, and data-driven research. Research Interests : Machine learning applications in chemical informatics Graph-based modeling and algorithm development Improving software engineering education through data analysis Computational methods for reaction database curation Recent Publication Trends : His 2024-2025 work shows a strong focus on combining graph theory with chemical reaction analysis, particularly for database curation and transition state modeling. He also contributes to educational research in embedded software development pedagogy.

• Machine Learning
• Chemical Informatics
• Graph Theory
• +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

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

Pascal Welke is a PostDoc Researcher at the Department of Machine Learning, Technical University of Vienna, working on the StruDL project (2023-2027) funded by the Vienna Science and Technology Fund (WWTF). His research focuses on enhancing graph neural networks (GNNs) through theoretical and practical advancements in expressivity, pooling mechanisms, and global feature integration. Active in graph representation learning, neural architecture design, and model interpretability Collaborates on interdisciplinary projects involving material science and computational linguistics Contributor to NeurIPS, ICLR, and ACL conferences Recent publications analyze the role of expressivity in GNN performance, introduce loop-based WL hierarchies, and develop model distillation techniques. His work spans both theoretical insights (homomorphism-based representations) and practical applications (edge device optimization, nonwoven material analysis). No formal awards or students listed in available records.

• Graph Neural Networks
• Machine Learning
• Structured Data Learning
• +3 more

Nikolaus Feith is a Doctoral Student at the Chair for CPS at Montanuniversität Leoben, supervised by Univ.-Prof. Dr. Elmar Rueckert since July 2022. He holds a Master’s degree in Mining Mechanical Engineering from the same university, completed in June 2022. His research focuses on integrating human expertise into robotic systems through Interactive Machine Learning. Education: Master’s degree in Mining Mechanical Engineering, Montanuniversität Leoben (June 2022) Research Interests: Machine Learning (Interactive Machine Learning, Model-Free Reinforcement Learning), Robot Learning, Optimization (Bayesian Optimization, CMA-ES), Human-Robot Interfaces, Augmented Reality, Embedded Systems in Robotics, and Cyber Physical Systems. Publications (2024): Explores topics like Bayesian Optimization frameworks for human expertise integration and mixed reality interfaces for robot learning, contributing to robotics and interactive learning systems. Teaching & Supervision: Course: 170.031 Programming in Python (WS23) Thesis Supervision: B.Sc. Christoph Andres: ROS2 Interface for FANUC CRX-10iA M.Sc. Christopher Martin Shimmin: Bayesian Optimization for Motorcycle ECU Parameters B.Sc. Marco Schwarz: Generic ROS2 Device Interface (Micro-ROS on ESP32) Awards: None listed. Labs/Teams: Part of the CPS research group, focusing on Cyber Physical Systems and robotics applications.

• Machine Learning
• Interactive Machine Learning
• Model-Free Reinforcement Learning
• +6 more

FH-Prof. Mag. Dr. Simon Tjoa is a Professor and Head of the Department of Computer Science and Security at the University of Applied Sciences St. Pölten. He also serves as Academic Director of the Cyber Security and Resilience (MA) program and Head of the Institute of IT Security Research. He is a member of the University’s Board (2023–2026). His roles include leading academic programs, research initiatives, and strategic governance. Roles: Department Head, Academic Director, Institute Head, Board Member Departments: Computer Science & Security, Digital Business & Innovation, Media & Digital Technologies Key Research Areas: Cyber Resilience, Artificial Intelligence Security, Blockchain Applications, Information Security Auditing Education Programs: Cyber Security & Resilience (MA), IT Security (BA), Digital Healthcare (MA), and Continuing Education in IT Security His research focuses on advancing cyber security frameworks, integrating blockchain for audit transparency, and developing risk management systems for AI. He emphasizes education through simulation games and cyber defense centers to enhance practical skills. Publications highlight contributions to cyber resilience fundamentals, AI governance, and blockchain-based auditing. His work bridges theoretical concepts with practical applications in critical infrastructure protection and incident response.

• Cyber Security
• Artificial Intelligence
• Blockchain Technology
• +3 more