Martin Schmiedecker is an External Lecturer at the Department of Information Systems Engineering, Technische Universität Wien (TU Wien), Austria. He specializes in digital forensics, cybersecurity, and privacy technologies. His research focuses on filesystem analysis, tracker-blocking tools, real-time forensic methods, and optimizing anonymity networks like Tor. His work includes projects such as TRUDIE (Trust Relationships in Underground IT Economies) funded by the Austrian Research Promotion Agency (FFG). Key contributions include detecting manipulated timestamps on NTFS, analyzing tracker-blocking tool effectiveness, and improving forensic endpoint visibility. Schmiedecker has published extensively in conferences like ARES, Euro S&P, and USENIX Security. No scientific awards are explicitly listed in the provided texts. He collaborates with researchers such as Edgar Weippl, Stefan Huber, and Stefan Neuner. His research integrates technical innovations with practical applications in cybersecurity defense mechanisms.
Martina Lindorfer is an Associate Professor in the Security and Privacy Research Unit at TU Wien, leading the Secure Systems Lab (SecLab). She is also a key researcher at SBA Research. Her expertise spans systems security, privacy, mobile app analysis, and malware research. Prior to her current role, she was a Postdoc at UC Santa Barbara under Christopher Kruegel and Giovanni Vigna. Lindorfer holds a PhD from TU Wien (2016) with honors, and has received awards including the ERCIM Cor Baayen Award (2018) and the ACM Early Career Award (2020). She advises students on topics like IoT security and privacy, and contributes to academic service roles like Program Co-Chair for ACSAC and WOOT. Education: PhD in Computer Science (2016, TU Wien), Master's in Software Engineering (TU Wien), Bachelor's in Computer and Media Security (Hagenberg). Research focuses on mobile and IoT security, privacy protections, and APT analysis. Her team has published impactful work on topics like Android security models, iOS permission systems, and smart TV vulnerabilities. Awards highlight her contributions to cybersecurity research, including recognition for work on HbbTV privacy (Best Dutch Cyber Security Paper, 2024) and early-career excellence. Lindorfer also engages in outreach, mentoring, and policy discussions around digital humanism and academic cloudification risks. Labs/Teams: Heads the SecLab at TU Wien, collaborates with SBA Research, and leads projects like the TU Wien Cybersecurity Center (CySec). Active in organizing conferences and mentoring students through initiatives like the Austria Cyber Security Challenge.
Florian Kleber is a senior scientist at the Computer Vision Lab within the Institute of Visual Computing and Human-Centered Technology at Vienna University of Technology (TU Wien), Austria. His research spans two primary domains: document analysis for cultural heritage preservation and medical data analysis. He has been actively involved in lecturing at TU Wien, particularly in Document Analysis courses, and has served as a substitute member of the Curriculum Commission for Informatics. Dr. Kleber's research focuses on the intersection of computer vision and practical applications. His work in cultural heritage includes multi-spectral acquisition and restoration of ancient manuscripts, development of tools for historical document analysis, and work on writer identification and retrieval systems. In medical data analysis, he has specialized in flow cytometry analysis, particularly for minimal residual disease assessment in acute lymphoblastic leukemia. His research demonstrates a consistent pattern of applying advanced computer vision techniques to solve domain-specific challenges in both humanities and medical fields. His recent publications reveal a strong trend toward synthetic data generation for training document analysis systems, self-supervised learning approaches for writer retrieval, and explainable AI techniques for medical data analysis. The publications show increasing sophistication in transformer-based architectures applied to both document analysis and medical imaging domains. His work bridges theoretical computer vision advances with practical applications in cultural heritage institutions and medical settings. Dr. Kleber has supervised multiple diploma theses at TU Wien, including work on automated analysis of herbarium collections, transparency techniques for neural networks, synthetic data for document analysis, and physical layout analysis of newspaper images. He has been involved in numerous research projects including the Vienna City Library poster collection analysis, digitization of museum holdings in Lower Austria, and the AutoFlow project for medical data analysis. His laboratory work centers around the Computer Vision Lab (E193-01) at TU Wien, where he collaborates on projects involving document image analysis, cultural heritage preservation, and medical data processing. His team has developed tools for manuscript analysis, document reconstruction, and flow cytometry data interpretation, often in collaboration with cultural institutions and medical facilities.
Rudolf Mayer is an Lecturer at the School of Logic and Computation and School of Information Systems Engineering at Vienna University of Technology . He is affiliated with the university through his roles in teaching and research, focusing on Machine Learning , Digital Preservation , and Privacy-Preserving Data Analysis . His work spans both academic research and applied projects, with a strong emphasis on collaborative and secure data management . His research interests include Machine Learning , Privacy-Preserving Techniques , Digital Preservation , and Music Information Retrieval . Over his career, Mayer has contributed to Developing frameworks for digital preservation of scientific and business processes Advancing privacy-preserving data analysis through platforms like WellFort Exploring adversarial machine learning and model stealing attacks Integrating semantic web technologies into data management systems His publications highlight trends in collaborative cybersecurity , model robustness , and digital archiving with applications in federated learning, self-organizing maps, and music analysis. Mayer has supervised 15+ graduate students , covering topics such as adversarial attacks , model watermarking , and privacy-preserving anomaly detection . Notable projects he has been involved in include the NEWSROOM initiative for cybersecurity automation, the Research Studio Digital Memory Engineering , and contributions to the PLANETS project for long-term digital access.
Dr. Tomasz Miksa is a Senior Scientist at TU Wien's Department of Data Science, specializing in machine-actionable data management plans (maDMPs), reproducibility frameworks, and data stewardship. His work bridges semantic web technologies, automated workflows, and interdisciplinary data governance. He leads projects like OS Trails (2024–2027) and FAIR-AI , focusing on synchronizing research data services and ensuring FAIR (Findable, Accessible, Interoperable, Reusable) principles. Miksa holds a PhD in Computer Science from TU Wien (2016) and has supervised numerous theses on topics like reproducible query processing and DMP integration. He actively contributes to initiatives such as FAIR Data Austria and openEO API standardization, emphasizing sustainable data practices across disciplines. Research interests include semantic representation of DMPs, automated validation tools, and frameworks for auditable privacy-preserving data analysis. His work on data citation mechanisms and evolving database schemas ensures long-term reproducibility in fields like environmental science and earth observation. Miksa collaborates internationally, co-editing special collections on data management planning and publishing in journals like ACM Transactions on Management Information Systems and IEEE eScience . Key contributions include the DCSO ontology for DMPs, the WellFort platform for privacy-aware data analysis, and frameworks for identifying software dependencies in biomedical workflows. His teaching focuses on data stewardship, with courses like Data Stewardship VO/UE addressing national and institutional strategies. Ongoing efforts aim to integrate machine-actionable DMPs with institutional repositories and cloud-based research infrastructures.
Maath Musleh is a Researcher and PhD candidate at the Vienna University of Technology (TU Wien), affiliated with the Faculty of Informatics and the Department of Computer Graphics. His academic roles include serving as a University Assistant and teaching courses such as Methods for Data Generation and Analytics in Medicine and Information Visualization . He holds a BSc and MSc, with his Master’s thesis focusing on industrial multivariate time series analysis. Research interests center on Visual Analytics , Medical Visualization , and Uncertainty Visualization , with applications in healthcare, manufacturing, and agriculture. His work emphasizes explainable AI, user confidence measurement, and decision-support systems. Notable contributions include the TrustME model for explainable guidance and the ConAn framework for quantifying user confidence in uncertain analysis scenarios. Key achievements include the Best Short Paper Award at VINCI 2021 for industrial time-series visualization research. His ongoing PhD, supervised by Prof. Renata Raidou, explores Guided Visual Analytics for Decision-Making under Uncertainty . He collaborates on projects like Agritology , a multilingual decision-support system for farmers, and has developed dashboards for industrial and medical data analysis. Maath’s interdisciplinary approach integrates visualization, machine learning, and human-centered design to address challenges in complex decision-making environments.
Anouk Michelle Oudshoorn is a PhD candidate and researcher at the Department of Knowledge-Based Systems, Institute of Logic and Computation, Technical University of Vienna. Her research focuses on integrating SHACL constraints with Description Logics and Ontology Engineering. She holds an MSc in Logic from the University of Amsterdam (ILLC) and dual BSc/BA degrees in Mathematics and Philosophy from Radboud University, Nijmegen. Education: PhD Candidate (2022–present), Technical University of Vienna MSc in Logic (2020–2022), University of Amsterdam BSc Mathematics & BA Philosophy (2017–2020), Radboud University Her research explores formal validation mechanisms for semantic technologies, emphasizing interoperability between SHACL and OWL ontologies. She has conducted research visits at Umeå University (2023) and TU Dresden (2024). Notable contributions include work on SHACL-Description Logic combinations and Vossian antonomasia generation for Wikidata. Awards: Netidee Stipend (2023) 2nd Prize Best Poster Award (ISWC 2023) Teaching: She has served as a teaching assistant for courses including 'Logic and Reasoning in Computer Science', 'Description Logics and Ontologies', and 'Knowledge-Based Systems' at TU Wien. Additional experience includes mathematics tutoring and logic instruction. Sustainability: Prioritizes train travel for international conferences to reduce environmental impact, undertaking extensive rail journeys across Europe.
Alexander Schatten is an External Lecturer at the Vienna University of Technology (TU Wien), Faculty of Informatics, Department of Information Systems Engineering. He holds a PhD in Sustainable Web-Based Systems and has extensive experience in research and industry, focusing on resilient IT systems, cybersecurity, and sustainable software engineering. His roles include managing director at biac/Twinformatics (2015-2017), senior researcher at TU Wien (2013-2015), and leadership programs at the European School of Management and Technology (2017). Education highlights include a PhD in Software Technology (2003), studies in analytical chemistry (Dipl.-Ing., 1996), and philosophy (2001-2004). Research interests span event-driven systems, agile development, and corporate sustainability. He has led projects like CSRmap for sustainability risk analysis (2013-2015) and contributed to cloud security guidelines for enterprises. Key achievements include finalist status in the Mercur Innovation Award (2014) and second place in the ZIT project competition (2013). His advising spans over 20 students, with notable works on resilient IoT systems, software aging analysis, and bionic software systems. Current activities include podcasting on future sustainability (Zukunft Denken) and consulting for Fortune 500 firms on digital sustainability strategies. His work bridges academia and industry, emphasizing practical applications of resilient IT frameworks and event-based architectures.
O.Univ.Prof. Michael Schrefl is a Professor in the Department of Databases and Artificial Intelligence at TU Wien (Technische Universität Wien). His research focuses on data security, semantic encryption, and healthcare informatics. He has contributed to advancements in electronic health record protection and privacy-preserving query processing techniques. Supervision includes works like 'Classifying air traffic scenarios' (2020) and 'Maintaining consistency of data on the web' (2004). Contact him via email . Publications span hybrid encryption-pseudonymization systems and semantic-based encrypted query frameworks. No scientific awards explicitly mentioned.
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.
Markus Schütz is a Researcher at the Department of Computer Graphics, Faculty of Informatics, TU Wien. He holds a Dipl.-Ing. Dr.techn. and BSc. His work focuses on real-time rendering of massive point clouds, GPU acceleration, and interactive visualization. Key projects include 'Bringing Point Clouds to WebGPU' and 'Instant Visualization and Interaction for Large Point Clouds'. He has developed the Potree library for web-based point cloud visualization. Education: Bachelor of Science (BSc) Diplom-Ingenieur (Dipl.-Ing.) Doctor of Technical Sciences (Dr.techn.) from TU Wien Research Interests: His research emphasizes real-time rendering techniques for large-scale point clouds, GPU optimization, and efficient data processing. He explores areas such as level-of-detail generation, compute shader utilization, and web-based visualization tools like Potree. Recent work includes software rasterization of 2 billion points and simultaneous LOD generation for point clouds. Awards: Best Paper Award at EGPGV2024 High-Performance Graphics 2022 Best Paper Award Second Place in SIGGRAPH Poster Student Research Competition (2018) AGEO AWARD 2017 Projects & Grants: Bringing Point Clouds to WebGPU (2024–2025, netidee Foundation) Instant Visualization and Interaction for Large Point Clouds (2023–2026, WWTF) IVILPC (Interactive Visualization of Large Point Clouds) project Labs & Teams: Active in TU Wien's Computer Graphics Group, collaborating on GPU-accelerated rendering and real-time visualization systems.
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.
Manuel Wimmer is a Lecturer in Business Informatics at TU Wien's Faculty of Informatics, specializing in model-driven engineering methodologies. His research develops foundations for model transformation, metamodeling, and interdisciplinary engineering. Research interests include model-driven software engineering, cyber-physical systems, web engineering, and industrial automation, with applications in smart production systems. Current work focuses on bridging IT/OT domains through standardized modeling approaches. Recent publications address quantum-edge cloud architectures, AI-enhanced modeling, and industrial security challenges. Article trends demonstrate strong focus on modeling language engineering, interoperability solutions, and quality assurance in complex systems. Leads the Christian Doppler Laboratory for Model-Integrated Smart Production and coordinates EU projects on low-code engineering platforms. Supervises doctoral research in model-driven technologies and software quality.
Allan Hanbury is a Full Professor for Data Intelligence and Head of the Data Science Research Unit at the Faculty of Informatics, TU Wien, Austria. He serves as Faculty Representative responsible for financial affairs and internationalization within the faculty. He is also a faculty member at the Complexity Science Hub Vienna. Roles: Faculty Representative (financial/internationalization), Data Science Research Unit Head Key Projects: Coordinator of EU-funded Khresmoi (medical search), DoSSIER (Marie Curie ITN training network), VISCERAL (big data evaluation), KConnect (medical text analysis) Spin-off: Co-founder of contextflow (radiology image search) Research focuses on information systems engineering and visual computing, with emphasis on: Information retrieval, data mining, text classification Medical data intelligence, health informatics Domain-specific systems for information extraction/retrieval Publications span over 180 refereed works, with recent contributions on LLMs in query generation, clinical trial matching, and systematic review automation. His work bridges academic research with industry applications in healthcare, legal text analysis, and patent mining. Grants include EU Horizon projects (e.g., DoSSIER, BRISE-Vienna), FFG-funded innovation initiatives, and collaborations with organizations like Siemens, Deutsche Telekom, and the Austrian Ministry of Finance.
Mantas Simkus is an Assistant Professor at TU Wien's Institute of Logic and Computation, affiliated with the Database and Artificial Intelligence Group. He previously held an Associate Professor position at Umeå University (Sweden) within the Wallenberg AI, Autonomous Systems and Software Program (WASP). He leads the FWF-funded project 'KtoAPP: Compiling Knowledge into Applications' and contributes to the Cluster of Excellence 'Bilateral Artificial Intelligence'. His research focuses on logic-based data management, knowledge representation, and nonmonotonic reasoning, with applications in semantic web technologies and ontology engineering. Education: Bachelor's in Computer Science from Vilnius University. Research interests include logic programming, computational complexity, description logics, and their integration with databases. He explores techniques for efficient query answering, schema validation (e.g., SHACL), and reasoning under incomplete information. His work bridges theoretical foundations (e.g., complexity analysis, formal semantics) with practical systems (e.g., ontology-mediated query processing). Key projects include 'KtoAPP' (2018–2025), investigating automated knowledge compilation, and contributions to the 'SemDat' and 'OMEGA' initiatives. He teaches courses on deductive databases and semi-structured data at TU Wien. He actively participates in academic service: co-chair of RuleML+RR 2024, editorial board member of the Artificial Intelligence journal, and former co-chair of DL 2019. His research group collaborates on topics like graph databases, answer set programming, and hybrid knowledge representation systems.
