Gabriele Kotsis is a Full Professor at the Institute of Telecooperation, Johannes Kepler University Linz, with extensive contributions to Artificial Intelligence research. Her institutional presence spans multiple departments through interdisciplinary projects while maintaining her primary affiliation with JKU's engineering-focused research units. Her research expertise encompasses: Natural Language Processing and Neural Machine Translation systems Reinforcement Learning applications for Smart Grid optimization Mobile Computing and Multimedia Intelligence frameworks Big Data Analytics and Database Systems innovation Human-centered AI development methodologies Recent publications (2024-2025) reveal a strategic focus on practical AI implementations addressing multilingual communication barriers and energy efficiency challenges. Her work consistently bridges theoretical AI advances with real-world applications across multiple domains. Professor Kotsis maintains an active supervision record with thesis guidance and leads multiple funded research initiatives. Her current portfolio includes: 'Enhancing Neural Machine Translation' project (2025-2026) for Southeast Asian languages 'INTES' regulatory compliance ecosystem (2023-2026) 'Human-centered Artificial Intelligence' initiative (2022-2026) Through her leadership in international conferences (iiWAS, MoMM, DEXA) and editorial roles for major proceedings, she maintains significant influence in the global computer science research community while advancing JKU's research profile.
Michael Schrefl is a Full Professor at Johannes Kepler University Linz, affiliated with the Institute of Business Informatics - Data & Knowledge Engineering. He serves as Principal Investigator on multiple research projects including AWARE (2024-2026) and AISA (2020-2022), and is actively involved in European research collaborations as both PI and researcher on funded projects. His research interests span across several key areas in modern data science and business informatics: Business Informatics and Digital Transformation Data Science and Knowledge Engineering Database Systems and Data Mining Semantic Technologies and Information Systems Predictive and Prescriptive Analytics Enterprise Computing and Information Systems Professor Schrefl's recent publication activity shows a strong focus on reliability assessment in predictive analytics, conceptual modeling for business intelligence, and applications of data science in Industry 4.0 contexts. His work bridges theoretical foundations with practical applications in enterprise information systems, particularly examining data quality and reliability in predictive models. As an active member of the academic community, Professor Schrefl serves on program committees for major conferences including BI-WEEK, EDOC, and DATA. He has supervised 129 students throughout his career and contributes to educational technology through projects like eTutor++. His research spans multiple fields of science including Knowledge Engineering, IT Security, Web Engineering, and Distributed Systems. Professor Schrefl maintains strong engagement with both academic and public discourse, having given invited talks on AI topics in 2024 and scheduled media appearances including an Ö1 interview on autonomous weapons systems for 2025, demonstrating his commitment to science communication and societal impact of technological developments.
Jan Vrablicz serves as a Junior Researcher at the Josef Ressel Center for Knowledge-Assisted Visual Analytics for Industrial Manufacturing Data within the University of Applied Sciences St. Pölten, Austria. His role focuses on developing visual analytics methodologies that integrate domain knowledge to enhance data-driven decision-making in industrial manufacturing contexts, particularly addressing challenges in injection molding processes through semantic technologies and interactive causal analysis frameworks. His research interests span Visual Analytics, Industrial Manufacturing Data, Knowledge-Assisted Systems, Causal Analysis, Injection Molding, and Semantic Systems. This work emphasizes practical applications where visual interfaces bridge complex manufacturing data and operational insights, targeting transparency in AI-driven industrial systems. Current projects investigate knowledge-assisted causal modeling to optimize production quality and efficiency in metal casting environments. Recent 2025 publications demonstrate a clear research trajectory: integrating semantic technologies with visual analytics for industrial data interpretation, specifically in injection molding. Key trends include problem characterization methodologies for causal analysis, knowledge-assisted interactive systems, and semantic transparency frameworks—all targeting real-world manufacturing data challenges with applied computer science solutions. No information regarding student advising or research grants was found in available sources. The Josef Ressel Center functions as his primary research ecosystem, a dedicated unit advancing visual analytics for industrial manufacturing through collaborations between computer scientists, engineers, and industry partners. The center specializes in developing knowledge-assisted tools that transform complex production data into actionable visual insights for manufacturing professionals.
Andreas Steiner is a Research Associate and Doctoral Student at the Vienna University of Technology, affiliated with the Faculty of Mechanical Engineering and Industrial Management. He holds a Dipl.-Ing. and B.Sc. in Industrial Engineering and Mechanical Engineering from the same institution. Education: Dipl.-Ing. in Industrial Engineering and Mechanical Engineering B.Sc. in Industrial Engineering and Mechanical Engineering Research Interests: Data-driven maintenance management Data mining and knowledge-based optimization Predictive data analytics and machine learning Research Projects: Industrial Data Science, BEHAVE, WIN A3R. Publications: Focus on rail vehicle maintenance, thin film mechanics, and scene awareness in multi-machine scenarios.
Theresa Madreiter is a Lecturer & Doctoral Student at the Institute of Management Sciences within the Faculty of Mechanical Engineering and Industrial Management at Vienna University of Technology (Technische Universität Wien). Her research focuses on Production and Maintenance Management, where she combines engineering expertise with data science approaches to advance industrial maintenance practices. Her educational background includes: Dipl.-Ing. in Industrial Engineering and Mechanical Engineering from the Faculty of Mechanical Engineering and Industrial Management, Vienna University of Technology BSc. in Industrial Engineering and Mechanical Engineering from the Faculty of Mechanical Engineering and Industrial Management, Vienna University of Technology Madreiter's research interests center on knowledge-intensive approaches to industrial maintenance. She explores how knowledge-based maintenance , predictive and prescriptive maintenance systems , and knowledge discovery from text can transform traditional maintenance practices. Her work leverages semantic technology and Natural Language Processing to extract valuable insights from maintenance documentation, and applies predictive data analysis and machine learning techniques to anticipate equipment failures before they occur. This interdisciplinary approach bridges the gap between industrial engineering and data science, positioning her at the forefront of Maintenance 4.0 research. Her publications demonstrate a strong focus on applying text mining and AI techniques to industrial maintenance challenges. The trend in her work shows increasing sophistication in combining multiple data sources (both structured sensor data and unstructured text documentation) to create comprehensive maintenance solutions. Her research spans both theoretical development of algorithms and practical implementation in manufacturing environments, with a particular emphasis on discrete manufacturing systems. Madreiter's scientific achievements have been recognized with several prestigious awards: Schnieder Prize YOUNG MAKER 2021 from acatech Industrial Management - Thesis Award 2020 from Austrian Association for the Promotion of Business Research and Education Best Paper Award for "Combining process monitoring with text mining for anomaly detection in discrete manufacturing" at the Conference on Learning Factories 2022 As a doctoral student and lecturer, Madreiter is actively involved in academic mentoring and education. Her master's thesis on "Design and Development of a Prototype of a Text Understanding Tool for Maintenance 4.0" has served as the foundation for her current doctoral research and multiple research projects including TU-MARS, True_Usage, DigiMain 4.0, and DigiTS-ME. Beyond her formal academic role, she demonstrates significant commitment to social causes through her work with the Computerclubhouse Vienna (CCV), where she leads technology workshops for children from disadvantaged backgrounds. Madreiter is part of research teams working on the intersection of industrial engineering and data science, particularly focused on how AI and text analytics can transform maintenance practices in manufacturing. Her work connects closely with Industry 4.0 initiatives and represents an important bridge between traditional engineering disciplines and emerging data-driven approaches.
Steffen Nixdorf is a Consultant Lecturer at the Vienna University of Technology , affiliated with the Faculty of Mechanical Engineering and Operations Science and the Department of Production and Maintenance Management . His work bridges academic research and industrial applications. Ph.D. in Industrial Engineering (Mechanical Engineering specialization) Dipl.-Ing. and B.Sc. in Industrial Engineering (Mechanical Engineering) Research Interests : Knowledge Management in Production Systems Reciprocal Learning in Human-Robot Interaction Predictive Data Analytics for Maintenance Digital Twins in Cyber-Physical Systems Work-Based Learning Frameworks Recent Research Trends : Focus on adaptive learning systems for robotics training, reciprocal learning mechanisms in Industry 5.0 environments, and predictive analytics for maintenance optimization. Publications emphasize collaboration between humans and machines in smart manufacturing contexts. Scientific Recognition : Best Paper Award (2020) at IFAC Conference on Advanced Maintenance Engineering Teaching Activities : Leads courses on Production Information Management Systems at TU Wien since Wintersemester 2022.
Konstantin Schekotihin is an Associate Professor at the Department of Artificial Intelligence and Cybersecurity, Alpen-Adria University of Klagenfurt. His research focuses on artificial intelligence, machine learning, and semantic technologies with applications in industrial systems and semiconductor manufacturing. Reinforcement learning for industrial scheduling Answer Set Programming (ASP) and stream reasoning Failure analysis automation and ontology engineering Neuro-symbolic AI integration Knowledge-based systems in manufacturing Recent publications emphasize AI-driven optimization in semiconductor production, decomposition strategies for scheduling problems, and multi-agent systems for workflow management. His work combines symbolic reasoning with machine learning to address complex industrial challenges. Contact: Konstantin.Schekotihin@aau.at
Assoc.-Prof. Dipl.-Ing. Dr. Erich Christian Teppan is affiliated with the Department of Artificial Intelligence and Cybersecurity at Alpen-Adria-Universität Klagenfurt. His research areas include artificial intelligence, computer sciences, data & knowledge engineering, human-computer interaction, mathematical logic, and knowledge representation, processing, and management. Affiliation: Department of Artificial Intelligence and Cybersecurity University: Alpen-Adria-Universität Klagenfurt Research Interests: Focuses on interdisciplinary domains spanning artificial intelligence, cybersecurity, and human-computer interaction, with emphasis on knowledge representation, expert systems, and mathematical logic applications in data engineering.
