Find Academic Supervisors

• Virtual Reality
• Augmented Reality
• Extended Reality (XR)
• +5 more

Lucas Schöffer serves as a Lecturer in the Department of Media and Digital Technologies at the University of Applied Sciences St. Pölten, Austria. He is affiliated with the Institute of Creative Media Technologies and contributes to multiple study programs including Creative Computing (BA), Media Technology (BA), Interactive Technologies (MA), and Smart Engineering (BA). His academic work spans both teaching and research in emerging digital technologies. Dr. Schöffer's research interests focus on the practical applications of extended reality technologies, particularly in healthcare, education, and industrial settings. His work explores how virtual and augmented reality can enhance user experiences in rehabilitation, training, and collaborative work environments. He has made significant contributions to understanding biomechanics in virtual environments and developing inclusive technologies for diverse user groups including elderly populations and young adults. His publication record demonstrates a clear evolution from early work on ambient assisted living and mobile technologies toward increasingly sophisticated XR applications. Recent publications highlight his expertise in quantitative gaze tracking, adaptive XR training systems, and biomechanical analysis in virtual environments. His collaborative research spans multiple disciplines including computer science, healthcare, industrial engineering, and human-computer interaction. Dr. Schöffer actively participates in numerous research projects including EyeQTrack, GreenTouch, IMPACT-sXR, MIRACLE, VeRgonomiX, and BRELOMATE. These projects demonstrate his commitment to applying media technologies to solve real-world problems in healthcare, ergonomics, industrial manufacturing, and social inclusion. He works within the Immersive Media Lab and ReMoCap Lab environments at the university, contributing to both theoretical research and practical implementations of emerging technologies.

• Health Sciences
• Digital Health
• Medical Education
• +3 more

Benjamin Roszipal is a Junior Professor at the Institute of Health Sciences at St. Pölten University of Applied Sciences, Austria. His work focuses on integrating eXtended Reality (XR) technologies into healthcare and nursing education, emphasizing digitalization, immersive learning, and gamified experiences. Research interests include: Digital transformation in nursing schools XR applications for medical training Immersive virtual reality for mass casualty incident simulations Gamification in anatomy education His recent publications address: XR's role in bridging theory-practice gaps Performance assessment in virtual simulations Challenges in implementing digital tools He is affiliated with the Department of Health Sciences and actively contributes to academic conferences as a keynote speaker.

• Human-Computer Interaction
• Information Visualization
• Web Usability
• +4 more

Keith Andrews is an Associate Professor at the Graz University of Technology , affiliated with the Department of Human-Centred Computing. His research spans Human-Computer Interaction (HCI), Information Visualization, and Web Usability, with a focus on improving interactive tools, responsive design, and accessibility in data visualization. Andrews' work emphasizes practical applications, including the development of responsive SVG chart libraries and pedagogical strategies for teaching HCI to large undergraduate cohorts. His research integrates principles from Computer Science, User Experience (UX), and Software Engineering. Scientific Awards Best Paper Award at IWAIT 2019 Certificate of Appreciation 2006 Heinz Zemanek Preis 1998 Steirische Vielfalt visualisiert 2016 Recent publications highlight trends in Information Visualization , Responsive Web Design , and Active Learning algorithms. His projects, such as DYONIPOS and UX Day Graz, demonstrate a commitment to interdisciplinary collaboration and real-world impact.

• Internet of Things
• Gamification
• Microcontroller
• +5 more

Peter Schattovich is a Research Associate in the Department of Electrical Engineering at the University of Applied Sciences Wiener Neustadt (FHWN), based at Campus 1 in Wiener Neustadt, Austria. His research focuses on Internet of Things applications, sensor data analysis, and gamification of sports. Key areas of interest include: Time-speed measurement systems LIDAR data analysis Person and object recognition Microcontroller-based systems Cloud-based evaluation systems Temperature range optimization for electronic systems Dr. Schattovich's recent publications demonstrate a strong focus on AI-based sensor data analysis, particularly in the domains of LIDAR technology and person recognition. His work bridges the gap between hardware development and intelligent data processing, with applications in sports measurement systems and movement gamification. He also shows interest in educational aspects of research, particularly in student participation in research projects. His research has resulted in the development of the Zelos time-speed measurement system, which aims to optimize IoT measuring stations for outdoor sports applications. Peter Schattovich actively collaborates with colleagues including Hochrainer, M., Wöber, W., and Leinweber, J. on various research projects.

Johann MitlöhnerView profile

Associate Professor

• Decision Support Systems
• Preference Aggregation
• Social Choice Theory
• +6 more

Johann Mitlöhner serves as an Associate Professor at the Vienna University of Economics and Business (WU), specifically within the Department of Information Systems and Operations Management. He is affiliated with the Institute for Data, Process and Knowledge Management, located in Building D2 on the third floor at Welthandelsplatz 1, 1020 Vienna, Austria. Professor Mitlöhner's research spans several interconnected areas within information systems and knowledge management. His primary focus lies in Preference Aggregation in Decision Support and Project Effort Estimation . He has extensively explored the application of Social Choice Voting Rules for preference aggregation and investigated the properties of voting rules through simulations. His work in text analysis encompasses Text Mining , Semantic Web technologies, Sentiment Detection , Product Feature Extraction , and Ontology Learning in restricted domains, with particular emphasis on grammar pattern-based approaches. Within his department, Professor Mitlöhner contributes to academic instruction and supervision of thesis topics related to decision support systems for IT system selection, effort estimation of IT projects, and various aspects of text mining and semantic technologies. His research bridges theoretical computer science with practical business applications, particularly in the domain of knowledge management systems. Professor Mitlöhner maintains an active research profile with publications cataloged in the WU Research database and participates in various research projects also documented through the university's research database. He collaborates with colleagues including Kaiser, Alexander; Kragulj, Florian; Polleres, Axel; Sabou, Marta; and others within the institute.

Philipp HungerländerView profile

Associate Professor

• semidefinite optimization
• conic optimization
• polynomial optimization
• +4 more

Philipp Hungerländer is an Associate Professor at the Institute of Mathematics, University of Klagenfurt. He is a member of the combinatorial optimization working group led by Prof. Franz Rendl, focusing on advanced optimization techniques for real-world industrial applications. Research Interests: His work spans three core areas: (1) semidefinite, conic, and polynomial optimization for nonlinear discrete problems, (2) quadratic programming and active-set algorithms for convex/nonconvex models, and (3) exact and heuristic methods for combinatorial optimization in routing and scheduling. Additionally, he explores game theory and dynamic games. Recent Publications: His 15 most recent articles reflect expertise in railway logistics, facility layout, home delivery services, and medical pandemic response. Topics include integrated freight routing, dial-a-ride optimization, and applications of mixed-integer programming. Contact: Email: Philipp.Hungerlaender@aau.at

• Mathematics
• Number Theory
• Fractal Geometry
• +3 more

Priv.-Doz. Dipl.-Ing.Dr. Paul Surer is a faculty member at the Institute of Mathematics , University of Natural Resources and Life Sciences, Vienna (BOKU). His research spans number theory, fractal geometry, and dynamical systems, with a focus on numeration systems and algebraic structures. Education: University Leoben (2005-2009), Universidade Estadual Paulista (UNESP) (2009-2011) Current Position: Associate Professor at BOKU's Institute of Mathematics (since 2014) Research Interests: Development of triangular labyrinth fractals and their geometric properties Rauzy fractals with measure-theoretic independence Innovations in substitutive number systems and coding prescriptions Representation theory for complex numbers using integer digits Applications in structural engineering via digital twin technology Publication Trends: 15 recent works (2014-2023) show expertise in fractal geometry, algebraic number theory, and interdisciplinary applications to structural analysis. Academic Service: Reviewer for journals like Bulletin of the Belgian Mathematical Society , Journal de Théorie des Nombres de Bordeaux , and Ukrainian Mathematical Journal .

• Applied Mathematics
• Computational Physics
• Scientific Machine Learning
• +10 more

Lukas Exl is a Senior Lecturer at the University of Vienna and a Research Director at the Wolfgang Pauli Institute (WPI), where he leads the Mathematical AI/ML Research Division. He holds a habilitation (venia docendi) in Computational Science from the University of Vienna, the first in this interdisciplinary field. Research Platform MMM Mathematics-Magnetism-Materials Wolfgang Pauli Institute (WPI), Vienna His research integrates Applied Mathematics, Computational Physics, and Scientific Machine Learning, focusing on numerical methods for PDE-based simulations, data-driven modeling, and reduced-order approaches. Key applications include computational micromagnetism for green energy materials and developing physics-informed neural networks (PINNs) with interpretable architectures. Recent publications emphasize machine learning techniques for magnetic material optimization, stray field computation, and trustworthy AI (TAI/XAI). He supervises students in Computational Science, Applied Mathematics, and Physics, with a focus on Extreme Learning Machines (ELMs), PINNs, and tensor decomposition methods. Data-driven Reduced Order Approaches for Micromagnetism (FWF Project, €484k, 2024-2028) Design of Nanocomposite Magnets by Machine Learning (FWF Project, €254k, 2022-2027) Reduced Order Approaches for Micromagnetics (FWF Project, €402k, 2018-2024) His team includes researchers like Dr. Sebastian Schaffer (PhD graduate), Kein Gjordeni, and Caroline Maitz. Collaborations span Danube University Krems and Technical University of Denmark's Energy Conversion and Storage department.

Lukas SteinbergerView profile

Associate Professor

• Statistical Machine Learning
• Differential Privacy
• High-dimensional Data Analysis
• +3 more

Lukas Steinberger serves as Associate Professor for Statistical Machine Learning in the Department of Statistics and Operations Research at the University of Vienna's Faculty of Business, Economics and Statistics. He is also affiliated with the university's Data Science research network. Prior to his current position, he was a post-doctoral researcher with Angelika Rohde at the University of Freiburg and held a temporary Full Professorship in the Department of Mathematics at the Technical University of Munich. Steinberger's research program centers on statistical inference under differential privacy, high-dimensional data analysis, and uncertainty quantification in statistical learning. His work bridges theoretical statistics with practical machine learning applications, particularly focusing on how to perform valid statistical inference while preserving data privacy. His research has significant implications for fields requiring sensitive data analysis including healthcare, finance, and social sciences where privacy constraints are paramount. His publication record demonstrates consistent contributions to top-tier statistics journals including multiple papers in the Annals of Statistics. His recent work shows a clear trajectory toward developing theoretically sound methods for privacy-preserving data analysis that maintain statistical efficiency. The research themes across his publications reveal strong connections between differential privacy mechanisms, high-dimensional statistical theory, and practical machine learning applications. Scientific Awards: Award for dissertations in the field of theoretical statistics (2016) Steinberger maintains an active research agenda with numerous conference invitations and seminar presentations internationally, reflecting his standing in the statistical community. His current research project "Classification - Preprocessed and high-dimensional data" (2021-2025) indicates ongoing funding and research direction. He serves as Vice-Director of Studies for the Business, Economics and Statistics program and teaches courses including Statistics 2, Computational Statistics, and Classification, Clustering and Discrimination at the master's level. As part of the Data Science research network at University of Vienna, Steinberger collaborates with interdisciplinary teams working at the intersection of statistics, computer science, and domain-specific applications. His research group focuses on developing theoretically grounded approaches to statistical machine learning problems under privacy constraints.

Dustin J. PennView profile

Adjunct Professor

• sexual selection
• communication
• behavioral biology
• +4 more

Dustin J. Penn is an Adjunct Professor at the Faculty of Life Sciences, Center for Organismal Systems Biology, University of Vienna, and a Senior Scientist and former Director (until 2014) of the Konrad-Lorenz-Institute of Ethology at the University of Veterinary Medicine Vienna. His research program focuses on sexual selection, communication, and behavioral biology, primarily using house mice as a model system. Dr. Penn's research interests include: Sexual selection and complex courtship signals Chemical communication through major urinary proteins (MUPs) Ultrasonic vocalizations in mice Evolutionary biology and host-pathogen interactions Darwinian medicine applications Conservation biology integration His laboratory investigates various aspects of animal communication, including individual recognition mechanisms, honest signaling principles, and neural integration of olfactory and acoustic cues that govern mating preferences. Current research projects include studies on adaptive functions of ultrasonic vocalizations, MUPs in chemical communication, and evolution of honest signaling, supported by grants from the Austrian Science Foundation (FWF) and Human Frontier Science Program. Dr. Penn's recent publications demonstrate a strong focus on mouse communication systems, inbreeding effects on reproductive traits, and signaling theory. His work shows how courtship vocalizations correlate with copulatory success, how chemical signals function in mate selection, and how trade-offs maintain honest signaling in animal communication systems. As an educator, Dr. Penn teaches courses on Darwinian Medicine, leads the institute's Journal Club on behavioral ecology topics, and offers practical courses in Experimental Design and Behavioral Ecology. He has also contributed to science communication through his 'Evolution Matters' guide for the European Society for Evolutionary Biology.

• Cryptocurrency
• Blockchain
• Ethereum
• +3 more

Aljosha Judmayer is affiliated with the Faculty of Computer Science and actively contributes to the Security and Privacy and Theory and Applications of Algorithms research groups. His work spans blockchain technology, cryptocurrency, and privacy mechanisms in software systems. Research Interests : Aljosha specializes in blockchain security, smart contract vulnerabilities, and interdisciplinary challenges between legal frameworks and decentralized technologies. His focus areas include privacy protocols, cryptocurrency economics, and secure messaging systems. Projects and Collaborations : He is part of the Christian Doppler Laboratory for Improving Security of Information Processes in Production Systems (CDL-SQI), collaborating with researchers like Weippl, Ullrich, and Stifter on blockchain-based security solutions. Recent Publications : His 2025 work on WhatsApp’s handshake mechanism addresses privacy risks in encrypted communication. In 2023, he explored miner extractable value in blockchain transactions and contributed to harmonizing blockchain terminology across disciplines. Activities : Aljosha has presented at events such as the Science to Science Blockchain Summit Mysterium and participated in panel discussions on decentralized finance (DeFi) and blockchain applications in healthcare.

• Natural Language Processing
• Machine Learning
• Data Mining
• +7 more

Pedro Henrique Luz de Araujo is a member of the Faculty of Computer Science affiliated with the Research Group Data Mining and Machine Learning . His work focuses on Natural Language Processing , Language Modeling , and Behavioral Learning , particularly in Brazilian legal text domains. He has contributed to advancements in named entity recognition , hate speech detection , and specification-driven AI systems . Active in 2022-2025 with publications in PLoS ONE and top NLP conferences Organized the KONVENS 2024 conference Research emphasizes LLM calibration , multimodal classification , and domain-specific datasets His recent publications show expertise in large language models (2025: persona effects; 2024: specification overfitting, text-guided image clustering), NER techniques (2024), and legal document processing (2023: multimodal classification; 2020: VICTOR dataset, LeNER-Br).

• Education
• Didactics
• Entertainment Computing

Tatjana Slavova is a researcher at the Faculty of Computer Science, affiliated with the Research Group Education, Didactics, and Entertainment Computing. Her work focuses on interdisciplinary research at the intersection of pedagogy and computational technologies. Research Interests: The Research Group Education, Didactics, and Entertainment Computing suggests expertise in educational methodologies, didactic frameworks, and the application of entertainment technologies in computational contexts. This aligns with broader interests in pedagogical innovation and technology-enhanced learning experiences.

• Computer Science
• Data Mining
• Machine Learning
• +3 more

Andrii Shkabrii is a researcher affiliated with the Faculty of Computer Science, actively contributing to fields such as Data Mining, Machine Learning, and Clustering. His work bridges computational methods with archaeological data analysis, particularly in historical time studies. Education: MSc in Computer Science Research Interests: Specializing in centroid-based deep clustering algorithms and non-redundant image clustering techniques, Shkabrii's work impacts both theoretical computer science and applied archaeology. He explores clustering quality metrics and domain expertise integration. Publications (2023-2024): Recent work includes advancements in reclustering barriers (2024) and medieval glass bead analysis (2023), reflecting interdisciplinary applications of machine learning.

Nils Morten KriegeView profile

Associate Professor

• Data Mining
• Machine Learning with Graphs
• Combinatorial Optimization
• +5 more

Dr. Nils Morten Kriege is an Associate Professor at the Faculty of Computer Science, University of Vienna, where he leads the Data Mining and Machine Learning research group. Previously, he served as Assistant Professor (2020-2023) at the same institution and held positions at TU Dortmund including Interim Professor (2019/2020) and Postdoctoral Researcher (2015-2020). His research focuses on graph-based machine learning methods with applications in cheminformatics and drug discovery. His educational background includes a Doctorate in Computer Science (2015) and Diploma in Computer Science (2009), both from TU Dortmund. He has also been a Visiting Researcher at the University of York, UK. Dr. Kriege's research centers on graph algorithms and machine learning with graphs, particularly focusing on graph neural networks, graph kernels, and their applications in cheminformatics and drug discovery. His work bridges theoretical computer science with practical applications, developing novel methods for graph similarity, graph classification, and network analysis. He has made significant contributions to understanding the expressivity and robustness of graph neural networks, as well as developing efficient algorithms for graph similarity search and molecular analysis. His recent publications (2023-2025) demonstrate a strong focus on graph neural networks, with particular attention to their expressivity, robustness against attacks, and practical applications in drug discovery. His work spans theoretical foundations (Weisfeiler-Leman hierarchy, graph isomorphism testing), practical implementations (efficient quantization, defense frameworks), and domain-specific applications (cheminformatics, drug discovery). Vienna Research Groups for Young Investigators (2019) - €1,466k funding for "Algorithmic Data Science for Computational Drug Discovery" Member of the Global Young Faculty V, Stiftung Mercator (2017) Dr. Kriege leads an independent research group funded through the Vienna Research Groups for Young Investigators program, focusing on computational drug discovery. He has served on program committees for major conferences including NeurIPS, ICML, IJCAI, AAAI, ICLR, and ICDM, and has reviewed for prestigious journals such as Transactions on Pattern Analysis and Machine Intelligence. His teaching portfolio includes courses on Data Mining, Graph Learning, and Introduction to Machine Learning. He leads the Machine Learning with Graphs work group within the Data Mining and Machine Learning Research Group at the University of Vienna, collaborating with researchers like Wilfried Gansterer and Petra Mutzel on graph-based methods for drug design and molecular analysis.