Armin Dadras serves as a Junior Researcher at the University of Applied Sciences St. Pölten within the Media Computing Research Group of the Institute of Creative Media Technologies and Department of Media and Digital Technologies. His academic qualifications include: Bachelor of Arts (BA) Bachelor of Science (BSc) Master of Science (MSc) His research bridges computer vision and biomedical engineering, specializing in interpretable geometric feature extraction for photography composition analysis and deep learning applications in medical imaging. Current work focuses on rule-of-thirds detection algorithms and glottis segmentation failure identification in endoscopic videos. Publications reveal a strong interdisciplinary trajectory merging media technologies with healthcare solutions, particularly through computational photography and speech pathology diagnostics. Dadras actively contributes to the Media Computing Research Group, driving innovation in media technology applications through advanced computing methodologies. No documented information exists regarding student supervision or research grant acquisitions.
Fabián Figueroa Valle serves as a Junior Researcher at the Carl Ritter von Ghega Institute for Integrated Mobility Research within the Department of Rail Technology and Mobility at the University of Applied Sciences St. Pölten, Austria. His research spans Rail Technology , Sustainable Transport , and Intermodal Transport Chains , with emphasis on optimizing rail processes and analyzing human factors in railway operations. Recent work investigates digital innovations like Digital Automatic Coupling and their impact on worker safety and efficiency. Publications reveal a concentrated focus on sustainable freight solutions through rail optimization, reflecting the institute's mission to advance eco-friendly mobility infrastructure. Key trends include intermodal logistics integration and digital transformation in railway systems. Figueroa Valle actively contributes to the Carl Ritter von Ghega Institute's research team, collaborating on projects addressing modern transport challenges and future railway innovations.
FH-Prof. Dr. Hirut Grossberger is a Senior Researcher at the Carl Ritter von Ghega Institute for Integrated Mobility Research and International Coordinator for the Department of Rail Technology and Mobility at University of Applied Sciences St. Pölten. She teaches in three programs: Rail Technology and Management of Railway Systems (MA), Rail Technology and Mobility (BA), and Rail Vehicle Technology (BA). Her research bridges railway engineering, sustainable infrastructure, and digital mobility solutions. Education includes: BSc in Agricultural Engineering (Debub University, Ethiopia) MSc in Water Management and Environment (BOKU Vienna) MSc in Land Management, Infrastructure and Civil Engineering (BOKU Vienna) Doctoral research at Institute of Structural Engineering (BOKU Vienna) Research focuses on: Railway Technology : Infrastructure lifecycle assessment, noise reduction, and sensor-based monitoring Sustainable Mobility : Eco-materials (e.g., clay noise barriers), emission reduction, and circular economy applications Digital Transformation : Mobile inspection tools, digital product passports, and acoustic sensing systems Recent publications (2019-2025) emphasize sustainability in transport infrastructure, with trends toward: Environmental action programs (CLEA) Eco-material innovation for noise control Distributed Acoustic Sensing for rail safety Digital tools for infrastructure management Leads/contributes to 20+ EU projects including: STAFFER (rail skill development) ZeroEmissionCityBahn (sustainable infrastructure) DPP4ALL (digital product passports) Smart Inspection (AI-assisted bridge maintenance) ECO-TCO (operational efficiency)
Sakeena Muntaha serves as a Junior Researcher at the University of Applied Sciences St. Pölten, affiliated with the Institute of Creative\Media/Technologies and the Department of Media and Digital Technologies since 2017. Currently on leave, she contributes to the institution's research mission through interdisciplinary projects spanning computer vision and applied machine learning. Her academic foundation includes a Master's degree in Computer Engineering from the National University of Sciences and Technology (NUST), Pakistan (2016) and a Bachelor's degree in Computer System Engineering from the NFC Institute of Engineering and Technology (NFCIET), Pakistan (2012). These qualifications underpin her technical expertise in visual computing systems. Dr. Muntaha's research program centers on machine learning and computer vision with dual application tracks: medical diagnostics (skin lesion segmentation, dermoscopy analysis) and environmental/urban systems (building footprint extraction, flood monitoring, real estate analysis). Her methodological approach integrates deep learning architectures with classical image processing techniques like level sets and Gabor filters, demonstrating versatility across domains from cultural heritage preservation to cybersecurity. Recent work shows increasing focus on robustness evaluation and real-world deployment challenges in vision systems. Analysis of her 15 most recent publications reveals strong thematic continuity in computer vision applications, with growing sophistication in handling real-world data constraints. Early work focused on medical imaging and malware detection, while recent publications emphasize urban infrastructure analysis and environmental monitoring, reflecting strategic alignment with societal challenges. The consistent use of deep learning frameworks across diverse domains highlights her technical agility. As an active member of the Media Computing Research Group, she contributes to projects including IMREA (Intelligent Multimodal Real Estate Assessment), Scribe ID AI (cultural heritage analysis), and ImmBild (location assessment via computer vision). Her collaborative research involves partnerships with institutions across Austria and Pakistan, though specific grant details and advising activities are not documented in available sources.
Johann Heinzelreiter serves as an FH-Prof. DI at the University of Applied Sciences Hagenberg, actively contributing to the Assistive Technology Lab and Center of Excellence for Smart Production . His research spans industrial automation, smart factory systems, and cloud computing applications in production environments. His research interests focus on assembly task modeling , low-cost tracking systems for industrial environments, and human-centered workplace design . Heinzelreiter develops practical solutions for real-world manufacturing challenges, particularly in optimizing human-machine collaboration on shop floors through the General Assembly Task Model (GATM) framework. Publication analysis reveals consistent contributions to industrial informatics, with recent work emphasizing smart factory implementation (2019-2020), cloud-based optimization (2014), and digital identity management (2015). His research demonstrates strong industry relevance through COIN Cooperation & Innovation projects. With an h-index of 34, Heinzelreiter has secured significant research funding through multiple COIN projects: Human Centered Workplace (2016-2021) - Co-Investigator Themis - Conserve Your Digital Life (2013-2015) - Principal Investigator BackmeUp - Offline Datensicherung Web 2.0 (2010-2012) - Principal Investigator He collaborates extensively with researchers including Pimminger, Kurschl, and Augstein across production environments and assistive technology domains. His work bridges academic research with practical industrial applications through the Smart Automation and Robotics initiative.
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.
Dorian Achim Prill is a Researcher at the School of Information Technology and Systems Management, Salzburg University of Applied Sciences, specializing in digitization and digital transformation within Industry 4.0 contexts. He serves as Deputy Project Manager for DIH-West-Neu (2024-2028) and contributes to multiple funded research initiatives including Retailization 4.0 and RetailLab4.0, focusing on practical implementations of digital technologies in production and retail environments. His research integrates Machine Learning and Data Science with industrial maintenance systems, developing intelligent frameworks for predictive maintenance scheduling and service choreography. Key interests include Publish-subscribe architectures, Learning Systems for production optimization, and Planning Frameworks that bridge theoretical models with real-world manufacturing applications. His work emphasizes data-driven process monitoring and cloud-based intelligence for industrial IoT ecosystems. Recent publications demonstrate a clear trajectory toward applied machine learning in maintenance engineering, with increasing focus on smart factory implementations and service-oriented architectures. The research consistently connects broad computer science principles with specific industrial subfields like friction coefficient measurement for vehicle safety and retail digitalization. Prill actively participates in externally funded projects totaling 11 initiatives since 2017, securing grants for Digital Innovation Hubs and living labs. His collaborative approach involves interdisciplinary teams across academia and industry, particularly evident in RetailLab4.0's living lab environment and FriCamMod's vehicle safety research. He has shared expertise through invited lectures on data-driven maintenance planning and marine maintenance intelligence. He operates within dynamic project teams including RetailLab4.0's retail digitalization consortium and FriCamMod's vehicle safety collaboration, working closely with principal investigators like S. Kranzer and R. Zniva. Current projects emphasize scalable digital transformation frameworks for both manufacturing and retail sectors through the DIH-West-Neu initiative.
