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Michaela NeubauerView profile

Professor

• Musculoskeletal Physiotherapy
• Manual Therapy
• Neurodynamics
• +5 more

FH-Prof. Michaela Neubauer PT M. Physio serves as a Professor within the Department of Health Sciences at the Institute of Health Sciences, University of Applied Sciences St. Pölten. She has held this academic position since 2011 while maintaining active clinical practice. Her institutional affiliations include the Physiotherapy (BA) program and the Austrian Association for Manual Physiotherapy (ÖVMPT) where she serves on the board and training committee. Her research focuses on Musculoskeletal Physiotherapy with specialized expertise in Manual Therapy, Neurodynamics, and Interprofessional Education. Recent work examines the physiotherapeutic process implementation, neurodynamic techniques for cervical radiculopathy, and interprofessional teaching frameworks across European healthcare systems. Her publications demonstrate consistent contributions to orthopaedic manual therapy evidence, particularly regarding peripheral nerve disorders and clinical reasoning methodologies. Analysis of her 15 most recent publications reveals an evolving trajectory from specific manual therapy techniques (2016-2018) toward broader interprofessional education systems (2022-2024). Key thematic clusters include neurodynamic clinical applications, evidence-based orthopaedic rehabilitation, and innovative teaching methodologies for healthcare collaboration. The publications consistently bridge theoretical knowledge with clinical practice through case studies and critical appraisals. Her professional activities extend beyond academia through postgraduate instructor roles for the International Maitland Teachers Association (2003-2012), supervision of orthopaedic manual therapy trainees, and operation of a private physiotherapy practice. She holds a University Didactics Certificate from FH St. Pölten and maintains active engagement with professional organizations including ÖVMPT. Her educational background includes a Master of Manipulative Physiotherapy from the University of South Australia (2001) following clinical training at the Academy for Physiotherapy St. Pölten (1994-1997). Early career experience spans multiple Austrian hospitals and private practices, establishing the clinical foundation for her academic work in musculoskeletal rehabilitation.

• Software Engineering
• Formal Methods
• Automatic Test Generation
• +5 more

Prof. Maria Christakis is a full professor at TU Wien's Faculty of Informatics, leading the Rigorous Software Engineering Group and the Software Engineering Research Unit. Her research focuses on developing reliable software tools and improving developer productivity through formal methods, automatic test generation, and program analysis. She holds a PhD from ETH Zurich and a diploma from the National Technical University of Athens. Her work bridges verification and systematic testing, with notable contributions to fuzzing, static analysis, and neural network certification. Key projects include tools like Harvey (smart contract fuzzer), DeepSearch (neural network attack framework), and Nomos (specification language for machine learning models). Her research has received awards including the Best Presentation Award at FSE'20 and a Distinguished Paper Award at ICSE'16. She advises multiple PhD and master's students and teaches courses in advanced software engineering and formal methods. Current projects explore green fuzzer benchmarking, compositional smart contract verification, and testing code translation models. Grants: Austrian Science Fund (FWF) doctoral school on Automated Reasoning Labs/Teams: Rigorous Software Engineering Group, Software Engineering Research Unit

Martina SagederView profile

Professor

• Business Management
• SME Management
• Tourism
• +5 more

Martina Sageder is a Professor and Head of the Department of Business and Tourism at Salzburg University of Applied Sciences. Her work focuses on innovation, digitalization, and sustainability within business management, SME strategies, and tourism sectors. As department head, she oversees programs integrating practical business education with contemporary challenges faced by regional enterprises. Her research emphasizes family firm branding, fintech adoption in SMEs, and the impact of digital transformation on service industries. Her research interests include: Strategic innovation in SMEs Impact of digitalization on traditional industries Reputation management in family businesses Virtual learning environments post-COVID-19 Financial technology adoption challenges Recent publications analyze fintech implementation in SMEs, family firm internationalization strategies, and AI applications in banking risk management. She has contributed to understanding pandemic-driven shifts in higher education delivery models through studies on virtual teaching acceptance. Her academic leadership includes coordinating international collaborations to develop dual degree programs and specialized master's curricula focusing on innovation and sustainability. She actively engages with local businesses through applied research projects addressing digital transformation challenges in tourism and service industries.

Julia WaldeggerView profile

Lecturer

• Business Administration
• Online Education
• Digital Transformation
• +5 more

Julia Waldegger currently holds the position of Lecturer at MCI Entrepreneurial School, where she has also served as Teaching & Research Assistant (2018–2023) and Assistant & Project Manager (2016–2018). She is pursuing a PhD in the Doktoratskolleg 'Organizing the Digital' at the University of Innsbruck, alongside a part-time PhD Program Management role. Her educational background includes a Mag.iur. (Law Diploma, 2007–2014), a BA in Political Science (2013–2015), an MA in Organization Studies (2020–2023), and ongoing PhD studies since 2023. PhD Program Management, University of Innsbruck (2023–present) BA Political Science, University of Innsbruck (2013–2015) Mag.iur. Law Diploma, University of Innsbruck (2007–2014) Her research focuses on digital transformation in business education, sustainability integration in online learning environments, and the interplay between organizational culture and digital adoption. She examines employee dynamics in digital workplaces, including benefits satisfaction, leadership styles, and CSR-driven decision-making. Her work also addresses pedagogical innovations such as futures thinking for sustainable innovation and responsible management practices. Her academic contributions span topics like pandemic-era online teaching adaptation, social components in e-learning effectiveness, and cultural change in digital SMEs. Recent projects include conceptualizing educational curricula through co-creation methods and exploring predictive hiring algorithms' ethical implications. Dean's List 2022 (University of Innsbruck) Performance-based scholarships (2014/15, 2015/16, 2020/21) Waldegger has advised over 13 Bachelor and Master theses, focusing on employee motivation, organizational culture, CSR reporting, and digitalization challenges. She has led research projects on HR analytics software implementation and digital strategies in the crafts sector. Her current PhD work explores digitalization governance in higher education institutions. She collaborates with interdisciplinary teams such as the Doktoratskolleg 'Organizing the Digital' and contributes to initiatives like the Bündnis Nachhaltige Hochschulen (Alliance for Sustainable Universities). Future projects include a 2025 real estate taxonomy study and ongoing work on algorithmic governmentality in hiring practices.

Matthias JanetschekView profile

Associate Professor

• Computer Science
• Cybersecurity
• Distributed Systems
• +5 more

Matthias Janetschek holds the position of Associate Professor at the Management Center Innsbruck (MCI), Austria, since September 2020. Previously, he served as a Lecturer at MCI (2018–2020) and was a Co-Founder/Senior Developer at Felix Solution UG (2013–2015). He earned a PhD in Computer Science from the University of Innsbruck (2011–2019), alongside earlier degrees in Computer Science (BSc 2002–2007, MSc 2007–2011). His research focuses on distributed systems, workflow management, cybersecurity, and IoT , with notable contributions to scientific workflow execution on cloud/manycore architectures and AR/VR applications in education. He has supervised over 18 bachelor/master theses addressing topics like IoT security, reinforcement learning, and edge computing. Teaching responsibilities include courses such as Distributed Systems , Virtual Reality , and Computer Architecture at MCI. He has also completed further training in online education tools (e.g., Sakai, Adobe Connect) and corporate communication. His publications span journals like Multimodal Technologies and Interaction and conference proceedings on workflow systems, cloud computing, and AR usability. Current work emphasizes large-scale IoT security assessments and edge computing optimizations.

Thomas DilgerView profile

Senior Lecturer

• Information Systems
• Finance
• Accounting
• +7 more

Thomas Dilger is a Senior Lecturer at MCI Management Center Innsbruck and holds academic roles at the University of Nebraska Omaha and the University of Innsbruck. He specializes in Information Systems, Finance, and Technology Management with a focus on blockchain applications, AI ethics, and educational technology. His teaching spans courses such as Strategic Financial Planning, IT Investment Analysis, and Governance with IT. Education: He holds a Master's (2012) and Bachelor's (2010) in Management, Communication & IT from MCI Innsbruck. His research explores topics like blockchain in healthcare, agile project management frameworks, and the impact of dynamic pricing. Research Interests: His work bridges technology and business, addressing cybersecurity risks, ethical AI, and innovation governance. He frequently publishes in journals like Human Systems Management and Journal of Cloud Computing . Awards: He received the Ars Docendi Recognition Award 2021 and the MCI Teaching Award 2019 for excellence in teaching. His projects include the Integrated Learning Scenario for Operational Excellence funded by Land Tirol. Grants & Labs: He leads research on digital transformation, smart farming, and enterprise architecture. His team collaborates on frameworks like the Continuous Planning and Forecasting Framework (CPFF) for agile budgeting.

• Cyber-Physical Systems
• Machine Learning
• Biomedical Engineering
• +2 more

Elpiniki Maria Lygizou is a PreDoc Researcher at Vienna University of Technology (TU Wien), affiliated with the Cyber-Physical Systems research group (E191-01). Her research spans interdisciplinary domains with core interests in: Cyber-Physical Systems Machine Learning Biomedical Engineering Medical Diagnostics Artificial Intelligence She specializes in developing AI-driven diagnostic tools, particularly applying neural network architectures like set-transformers to hematological analysis. Her work bridges computational modeling and clinical medicine to improve early disease detection. Recent publication trends demonstrate a strong focus on translating machine learning innovations into practical medical solutions, exemplified by her 2024 IEEE EMBC conference paper on automated leukemia diagnosis from immunophenotyping data. As an active member of TU Wien's Cyber-Physical Systems team, she contributes to research integrating real-time computational algorithms with physical medical instrumentation systems.

• Computational Sustainability
• Federated Learning
• Machine Learning
• +3 more

Paul Joe Maliakel is a PreDoc Researcher at Technische Universität Wien, affiliated with the Computational Sustainability department (E191-05). His work focuses on addressing data incompleteness in federated learning systems and optimizing energy efficiency for AI applications. Research Interests: Computational Sustainability Federated Learning Edge Analytics Machine Learning Energy-Efficient Computing GAN Applications Recent Publications Trends: His research spans federated learning architectures, energy-efficient AI inference, and data sustainability in edge computing environments. Key contributions include the FLIGAN framework for handling incomplete data in federated systems and analyses of energy-performance trade-offs in LLM deployments. Projects: Active in FWF-funded initiatives like Themis (2024–2027) and TRITON (2023–2027), as well as the Virtual Shepherd project (2024–2026), focusing on sustainable edge analytics.

Andrea MorichettaView profile

Researcher

• Machine Learning
• Distributed Systems
• Edge Computing
• +7 more

Andrea Morichetta is a University Assistant (Postdoc) at the Institute of Information Systems Engineering, Technische Universität Wien, Austria. He leads research in distributed systems, focusing on the intersection of machine learning and systems design. His work emphasizes edge computing, cloud-native systems, and AI-driven resource management. Education: PhD in Electrical, Electronics, and Communications Engineering (Politecnico di Torino, 2020), supervised by Prof. Marco Mellia. Visiting researcher at Cisco (2017), AIT (2019). Research Interests: ML for cloud/edge systems, federated learning, predictive scaling, cybersecurity in distributed environments, and intent-based systems management. Recent work explores multi-dimensional elasticity in stream processing, asynchronous federated learning, and self-referential digital anatomies. Teaching: Courses include Distributed Systems (VO), Distributed Systems Technologies (VU), and project/seminar courses (SE/PR). Actively supervises bachelor/master theses on topics like federated learning, edge computing, and serverless architectures. Key Contributions: Developed Polaris scheduler for SLO-aware edge scheduling, FL-SERENADE for network anomaly detection, and RainCloud for IoT swarm coordination. Co-authored over 50 peer-reviewed papers in top venues (IEEE Cloud, SOSE, ICSOC). Awards: 2019 Best Poster Runner-Up (TMA Paris), 2018 Best Student Paper (ITC Vienna).

• Operations Research
• Logistics Optimization
• Combinatorial Optimization
• +3 more

Matthias Prandtstetter is an External Lecturer in the Department of Logic and Computation at TU Wien. His research focuses on optimization in logistics, transportation systems, and combinatorial problems. He has contributed to projects such as Algorithmic Discrete Optimization (EU-funded 2004–2008) and MyITS (2011–2013). His work integrates metaheuristics, integer programming, and semantic technologies to address challenges in warehouse logistics, home healthcare scheduling, and document reconstruction. Education: PhD in Hybrid Optimization Methods, TU Wien (2009) Diploma in Exact and Heuristic Methods for Car Sequencing, TU Wien (2005) Research interests include: Logistics optimization Metaheuristics and hybrid algorithms Multi-modal routing systems Document reconstruction from shredded fragments Production scheduling and car sequencing Key projects: Algorithmic Discrete Optimization (EU, 2004–2008) MyITS: Semantically Enriched Multi-Modal Routing (2011–2013) ASP and HEX-Programme (2008–2015) His research has addressed practical applications like cooperative delivery systems, spare parts warehouse routing, and cross-cut document reconstruction. Advising & grants: Supervised 8 students in optimization-related theses Received EU funding for Algorithmic Discrete Optimization Labs/teams: Active in TU Wien's operations research and logistics research groups, collaborating on projects involving semantic technologies and combinatorial optimization.

Michael ReiterView profile

Senior Lecturer

• Machine Learning
• Pattern Recognition
• Computer Vision
• +4 more

Michael Reiter is a Senior Lecturer at the Institute of Visual Computing & Human-Centered Technology, part of the Faculty of Informatics at TU Wien. His research focuses on Machine Learning, Pattern Recognition, and Computer Vision, particularly in medical applications such as flow cytometry data analysis for leukemia diagnosis and minimal residual disease (MRD) assessment. He leads the MyeFLOW project (2020–2025), which develops automated methods for immunophenotyping and MRD detection in pediatric leukemia. His work bridges machine learning and clinical diagnostics, emphasizing explainable AI in healthcare. Reiter collaborates with medical institutions to translate algorithms into clinical tools, such as the FlowMe decision support system. His research interests include transformer networks, UMAP visualization, and Gaussian mixture models for biomedical data. He has supervised diploma theses on topics like meta learning for blast cell classification and U-Net-based flow cytometry analysis. Reiter’s publications span over two decades, with recent emphasis on medical AI applications. His lab, the Computer Vision Lab, focuses on visual computing for healthcare, robotics, and cultural heritage digitization. Current projects address automated analysis of museum herbarium collections and empathic robotic systems for autism therapy.

• Automated Reasoning
• Formal Methods
• Theorem Proving
• +3 more

Johannes Schoisswohl is a PreDoc Researcher at TU Wien's Department of Formal Methods in Systems Engineering. His research focuses on automated reasoning, theorem proving, and formal methods in computer science. He is involved in projects such as ForSmart (2023–2027) and SFB SPyCoDe (2023–2026), exploring topics like quantifier elimination, unification algorithms, and decision procedures for arithmetic systems. His work bridges theoretical foundations with practical applications in automated deduction and formal verification. Education: Diplom-Ingenieur (Dipl.-Ing.) and Bachelor of Science (BSc). His academic contributions include seminal papers on conflict-driven quantifier elimination (VIRAS framework), superposition-based reasoning with delayed unification, and inductive benchmarks for automated systems. His research emphasizes improving automated reasoning tools for real-world verification challenges. Notable projects include contributions to the ALASCA system for quantified linear arithmetic and the development of reflection-based techniques for automating induction. His publications span conferences like CADE, LPAR, and CICM, showcasing advancements in formal methods and logic-based AI.

• Medical Informatics
• Healthcare Information Systems
• Database Systems
• +3 more

Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Ernst Schuster is an Associate Professor in the Department of Databases and Artificial Intelligence at Technische Universität Wien. His research focuses on healthcare information systems, medical informatics, and telemedicine infrastructure. He has supervised numerous theses on topics ranging from cancer documentation systems (e.g., HNOOncoNet) to mobile health applications and medical data security. His work emphasizes interdisciplinary collaboration between informatics and healthcare domains. Key research interests include: Database systems for medical applications Telemedicine and remote patient monitoring Software engineering for healthcare IT Decision support systems in clinical settings Recent publications highlight innovations in: Medical data version control systems Usability engineering for healthcare interfaces Telecommunication security frameworks AI-driven diagnostic tools Supervised over 30 graduate students since 2002, contributing to advancements in medical informatics and health technology. Active in developing web-based medical documentation systems and e-learning platforms for medical education.

• Algorithmic Game Theory
• Social Choice Theory
• Multi-Agent Systems
• +3 more

Sofia Henna Elisa Simola is a PreDoc Researcher and Researcher at the Vienna University of Technology (TU Wien), affiliated with the Department of Algorithms and Complexity within the Faculty of Informatics. She holds the role of Projektass.in (Research Assistant) and is engaged in advanced algorithmic research. Her research focuses on algorithmic aspects of social choice mechanisms, including multi-winner elections, coalition formation, and parameterized algorithms. Notable projects include investigations into refugee resettlement optimization and hedonic games with complex social dynamics. She contributes to courses like Advanced Research in Algorithmics and Algorithmic Social Choice. Education: MSc degree (specific field unspecified) Project: Structural and Algorithmic Aspects of Preference-based Problems in Social Choice (2019–2027) Her work bridges theoretical computer science with applications in social policy and multi-agent systems, emphasizing computational efficiency and real-world applicability.

• Distributed Systems
• Service Platforms
• Energy-Aware Computing
• +1 more

Margret Steinbuch is a Researcher in Distributed Systems at the Vienna University of Technology. Her work focuses on federated data systems, energy-aware computing, and performance optimization for heterogeneous architectures. Her research explores trustworthy data lakes, service platform engineering, and scalable computing solutions for modern distributed environments. Steinbuch has contributed to multiple European Commission projects including INDENICA (engineering virtual service platforms) and COIN (enterprise interoperability). Her articles consistently address optimization challenges in distributed and parallel computing systems. She coordinates laboratory resources and infrastructure for distributed systems research while collaborating on international technology initiatives.