Find Academic Supervisors

Christoph PraschlView profile

Assistant Professor

• Wildlife Monitoring
• Computer Vision
• Machine Learning
• +3 more

Christoph Praschl is an Assistant Professor at the University of Applied Sciences Upper Austria, based at the Research Center Hagenberg. He holds a Master's and Bachelor's in Software Engineering from the same institution. His research focuses on ICT applications in wildlife monitoring, energy systems, and healthcare technology. Key projects include wildlife accident risk modeling (using Gaussian Mixture Models) and orthopedic aid customization leveraging AI. Education: Master's in Software Engineering (2014–2017), University of Applied Sciences Upper Austria Bachelor's in Software Engineering (2014–2017), University of Applied Sciences Upper Austria Research Interests: Computer Vision, Machine Learning, Wildlife Monitoring, Energy Systems, Biodiversity Conservation, and Health Technology. His work integrates AI and statistical methods to address real-world challenges in ecology and healthcare. Recent Projects: Wildschwein Project (2024–2025): Developing digital tools for wild boar carcass detection BAMBI (2022–2025): Airborne biodiversity monitoring via UAVs PASS (2017–2019): Plan analysis using self-learning solutions Advising & Grants: Collaborates on interdisciplinary projects, including wildlife risk assessment and energy storage systems. No formal advisees listed. Labs/Teams: Active in the Research Center Hagenberg and the Medical Engineering/TIMed Center.

Sascha SenckView profile

Assistant Professor

• Additive Manufacturing
• X-ray Computed Tomography
• Materials Science
• +3 more

Sascha Senck is an Assistant Professor at the University of Applied Sciences Upper Austria, affiliated with the Research Center Wels. He leads research initiatives in Additive Manufacturing, Materials Science, and Advanced Imaging Techniques. His work focuses on X-ray Computed Tomography (X-CT), phase contrast imaging, and defect analysis in advanced materials like carbon fiber reinforced polymers and magnesium alloys. Key projects include PEMOWE (energy transition testing), HyperMAT (hyperspectral material analysis), and TCA5 (diabetic foot imaging). He collaborates with regional industries and academic institutions across Austria and the Czech Republic. His research has been featured in media outlets for innovations in 3D-printed medical implants and non-destructive testing methods. Senck has supervised multiple interdisciplinary projects and holds an h-index of 16 with over 747 citations. His lab focuses on developing advanced manufacturing techniques and medical imaging solutions for industrial and clinical applications.

• Mobile Computing
• Human Activity Recognition
• Wearable Devices
• +4 more

Stephan Selinger is a Professor at FH Hagenberg, part of the University of Applied Sciences Upper Austria. He specializes in the Department of Information and Communications Technology (ICT), focusing on wearable devices, human activity recognition, and energy expenditure prediction. His research integrates machine learning, sensor technology, and data analysis to address challenges in health monitoring, sports science, and navigation systems. He is a Co-Investigator in the EDDIE project (2023-2025), a Horizon-funded initiative developing distributed energy data infrastructure. His work spans topics like Kalman filtering for positioning, smart sock-based power estimation, and comparative studies of wearable device performance. Dr. Selinger has organized academic events and peer-reviewed conferences such as IoT 2015 and MUM 2012. His research outputs include 13 publications since 2006, with notable contributions in energy expenditure modeling and activity recognition algorithms.

Wolfgang AignerView profile

Professor

• Information Visualization
• Visual Analytics
• Human-Computer Interaction
• +3 more

Wolfgang Aigner is a Professor and Deputy Head of the Faculty of Engineering and Business at the University of Applied Sciences St. Pölten. He leads the Institute of Creative Media Technologies and is affiliated with the Department of Media and Digital Technologies. His expertise spans Information Visualization, Visual Analytics, and Human-Computer Interaction, with a focus on time-oriented data. He holds a PhD from TU Wien and a Habilitation in Practical Computer Science. Education : PhD (Dr. techn.) in Computer Science, TU Wien (2006) Habilitation (Priv.-Doz.), TU Wien (2013) MSc (Dipl.-Ing.) in Computer Science, Johannes Kepler University Linz and TU Wien (1997-2003) Research : Aigner's work emphasizes visual analytics for time-series data and data journalism. Key projects include VALiD (Visual Analytics in Data-Driven Journalism) and VisOnFire (workflow visualization). He authored over 125 peer-reviewed articles and the book Visualization of Time-Oriented Data (Springer, 2011). Projects & Labs : Active in initiatives like KAVA-Time, IoT4LAC, and Trust AI. He leads research groups in Media Computing, focusing on areas like Data Science, Health Informatics, and Machine Learning.

• Statistical Genetics
• High Performance Computing
• Bioinformatics
• +3 more

Roles and Affiliations: Associate Professor of Applied Statistics at the Department of Mathematical Sciences, Purdue University Fort Wayne. Member of the College of Science. Active in Google Scholar and ResearchGate academic networks. Email: yorgovd@pfw.edu Research Interests: Focuses on statistical genetics, particularly in developing methods for analyzing human complex traits such as admixture mapping and association testing. Also engaged in kinship estimation in heterogeneous populations, high performance computing, and bioinformatics. Explores applications of R and Python in data science education and research. Teaching: Instructs courses like STAT242 (Introduction to Data Science with R) and provides free access to DataCamp courses for students. Known for creating R tutorials and computational tools for statistical analysis.

Andreas GartusView profile

Researcher

• Empirical Aesthetics
• Visual Perception
• Cognitive Neuroscience
• +3 more

Andreas Gartus is a researcher at the Faculty of Psychology , University of Vienna , affiliated with the Department of Cognition, Emotion, and Methods in Psychology . His work focuses on empirical aesthetics, exploring how visual features like symmetry and complexity influence aesthetic experiences in art and everyday life. He is responsible for managing the Cognition Research Lab , EEG Lab Vienna , and the EVA LAB (Empirical Visual Aesthetics). His research integrates computational methods, statistical analysis, and neurophysiological techniques to study emotional responses to art, embodied cognition, and decision-making processes. Key research areas include the psychological and neural mechanisms behind aesthetic judgments, the role of context in art perception, and the application of machine learning to analyze visual stimuli. He has contributed to over 28 peer-reviewed publications since 2011, with recent work examining movement behavior in art galleries, symmetry preferences, and emotional video analysis. His activities include international conference presentations and collaborations across disciplines. His studies often address how embodied experiences (e.g., facial muscle responses, movement) interact with aesthetic evaluation, reflecting a multidisciplinary approach to understanding human perception and emotion. Despite no listed awards, his extensive publication record highlights sustained contributions to empirical aesthetic research.

• Statistics
• Operations Research
• Machine Learning
• +3 more

Tatyana Krivobokova is a Professor in the Department of Statistics and Operations Research at the Faculty of Business, Economics and Statistics. Her research focuses on statistical methodologies, including spline-based approaches, partial least squares, linear mixed models, and Bayesian techniques. She actively contributes to interdisciplinary projects, such as modeling chemical reaction yields using high-throughput experimental data. Her work aligns with UN Sustainable Development Goals, emphasizing innovative statistical solutions for complex data problems. Recent research includes latent factor frameworks for ill-posed regression and model-free identification methods, reflecting her expertise in statistical inference and predictive modeling. Activities: Organized multiple ISOR Colloquia in 2025, demonstrating leadership in academic engagement. Publications: Over 30 publications since 2004, with recent focus on methodological advancements in statistics and data science. No scientific awards, grants, or advisees are explicitly listed in the provided information.

• Computational Chemistry
• Molecular Dynamics
• Machine Learning Applications
• +3 more

Maximilian Xaver Tiefenbacher holds dual appointments in the Department of Computational Biological Chemistry and Department of Organic Chemistry within the Faculty of Chemistry at University of Vienna. His research integrates machine learning with quantum chemical simulations to study excited-state molecular phenomena. Computational Chemistry (100% fingerprint match) Excited Electronic State Dynamics (88%) Machine Learning Applications in Chemical Engineering (66%) Molecular Dynamics Simulations (66%) Rare Event Sampling Techniques (22%) His publication record demonstrates a clear trajectory in developing neural network potentials for simulating photochemical processes in explicit solvent environments. The 2023 Journal of Chemical Theory and Computation article introduces novel flux sampling methods for excited-state rare events, while the 2022 Royal Society of Chemistry chapter synthesizes machine learning advances in electronic excited state dynamics. Both works emphasize solvent effects and quantum-classical hybrid methodologies. Collaborative research patterns are evident through 31 recorded activities including 26 poster presentations and 5 oral contributions. Key collaborators include Leticia Gonzalez Herrero, Philipp Marquetand, and Brigitta Bachmair across projects like "Excited-State Nonadiabatic Dynamics in Explicit Solvent" and "Furan Simulations in Water". The research network shows strong European institutional connections with citations appearing in Wikipedia and social media discussions.

Milosh JovanovikjView profile

Associate Professor

• Knowledge Graphs
• Data Engineering
• Semantic Web
• +8 more

Dr. Milosh Jovanovikj holds the role of Associate Professor at the Faculty of Computer Science and Engineering in Skopje, North Macedonia, and concurrently serves as a PostDoc Researcher at TU Wien in Vienna, Austria. He is also a Senior R&D Knowledge Graphs Engineer at OpenLink Software in London. His primary research focuses on Knowledge Graphs (KGs), including their lifecycle processes such as modeling, enrichment, and analytics-driven AI applications. He has contributed to over 10 international and 25 national research projects, authored 60+ publications, and co-authored 3 books. His expertise spans Data Engineering, Semantic Web technologies, Linked Data, and Applied AI. Significant contributions include developing frameworks like RDFGraphGen for RDF graph generation and PharmKE for pharmaceutical text analysis. He has led initiatives in geospatial semantic web standards (GeoSPARQL) and healthcare informatics, including predictive analytics for nephrology data. Education: PhD in Computer Science and Engineering His work bridges academia and industry, with projects like the 3DFed federation engine and Semantic Sky (a Gmail plugin). He has extensive experience in ontology development (e.g., SEOntology, VEO for CO2 emissions) and open data initiatives, including LinkedDrugs for global drug data consolidation. Active in dissemination, his articles span knowledge graph applications in music recommendation, finance sentiment analysis, and drug-disease relation discovery. He has collaborated on benchmarks like the GeoSPARQL Compliance Benchmark and Mighty Storage Challenge (MOCHA). Grants/Projects: HOBBIT, SAGE, 3DFed, LinkedDrugs He leads teams in advancing semantic web integration, cloud service platforms (Semantic Sky), and educational materials like Web Programming Basics . His labs and teams focus on AI-driven solutions for healthcare, geospatial analytics, and enterprise systems.

Martin SchälerView profile

Associate Professor

• Database Systems
• Scientific Data Management
• Hardware-sensitive Database Tuning
• +5 more

Associate Professor Martin Schäler is part of the Database Research Group at the Department of Computer Science, University of Salzburg. His research focuses on Scientific Data Management, Hardware-sensitive Database Tuning, Time Series Analytics, and Data Provenance. He holds a Dr.-Ing. (PhD) from Otto-von-Guericke University Magdeburg (2014), where he also completed his Master's degree in 2010. Prior to his current position, he was a post-doctoral researcher at the Karlsruhe Institute of Technology's DBIS group (2015-2020). His work emphasizes optimizing database systems through hardware-aware techniques and efficient query processing. Notable contributions include studies on main-memory storage structures, semantic change analysis in text data, and encryption methods preserving distance metrics. He has published in top venues such as IEEE TKDE, VLDB, and ICDE. His academic trajectory includes roles as a research assistant, scientific coordinator, and postdoc researcher before securing his tenure-track position at Salzburg University in 2020. Current research trends show a focus on bridging theoretical database advancements with real-world hardware constraints.

Bernhard HollausView profile

Lecturer

• Sports Technology
• Machine Learning
• Sensor Systems
• +3 more

Bernhard Hollaus is a Lecturer at the Management Center Innsbruck (MCI), where he teaches and conducts research in mechatronics, health technologies, and sports engineering. He leads the Health Tech Research Cluster at MCI and has held roles such as CEO of Auto Hollaus and Project Manager at Siemens AG. His academic affiliations include the University of Innsbruck, where he earned a PhD in Sport Science, and FH Technikum Wien for his MSc and BSc in Industrial Elec and Elec. His research focuses on applying machine learning and sensor systems to sports technology, including golf club impact detection, cycling cadence analysis, and American football training automation. He has supervised over 100 bachelor and master theses on topics ranging from VR sledding simulators to neural networks in sports analytics. Key awards include the Cluster Award Mechatronics (2016), Higher Degree Research Student Prize (2020), and distinctions from the University of Innsbruck (2021) and Technikum Wien (2011–2012). His work bridges engineering and sports science, with notable contributions to wearable sensor applications and sports safety education. Notable projects include developing the VRodel sledding simulator, a golf club impact detection system, and advanced passing machines for American football training. Collaborations span academic institutions and industry partners, reflecting his dual expertise in technical innovation and sports performance.

• Sports Analytics
• Machine Learning
• Statistical Modeling
• +3 more

Robert Bajons is a researcher at the Department of Statistics and Mathematics within the Faculty of Statistics and Mathematics at Vienna University of Economics and Business (WU Wien). His work bridges statistical theory with practical sports analytics applications across soccer and American football domains. His research focuses on Sports Analytics and Machine Learning , specializing in player evaluation systems, statistical modeling of game dynamics, and development of novel metrics like Expected Points and Plus-minus models. Key methodologies include adaptive logistic lasso regularization, curve clustering for movement analysis, and machine learning inference frameworks. Analysis of his recent publications reveals strong trends in soccer analytics (xG modeling, possession sequences) and American football strategy (tackle valuation, pass rush routes). His work consistently applies advanced statistical techniques to sports data, with emphasis on actionable insights for team performance optimization. Award Highlights: Best Presentation Award at ICoMS 2023 Bajons actively disseminates research through scientific lectures including "Machine learning based statistical inference in sports analytics" (May 2025) and "PEP: Evaluating Tackles in American Football" (May 2024), demonstrating strong engagement with the international sports analytics community. His research shows consistent publication output with three 2025 publications already in press.

• Computer Science
• Artificial Intelligence
• Robotics
• +3 more

Jürgen Kogler is a Senior Lecturer at Vienna University of Technology (TU Wien), affiliated with the Department of Theory and Logic. He specializes in advanced stereo matching techniques for silicon retina cameras and event-based vision systems, contributing to robotics and computer science through innovative sensor fusion methodologies. Education: Dipl.-Ing.(FH) Dr.techn., MSc Projects: Precise3D (2016–2023) Research Focus: His work explores dynamic vision sensors, belief propagation algorithms, and sparse data processing for 3D reconstruction. Recent publications highlight advancements in stereo calibration frameworks and ground truth evaluation metrics for event-based systems. Publications: Key contributions include 2024’s IEEE Robotics and Automation Letters article on dynamic calibration and 2017’s CVPRW paper on cooperative stereo matching. Earlier works (2016–2013) established foundational methods for silicon retina-based stereo imaging.

• Security
• Intrusion Detection
• Malware Detection
• +2 more

Christopher Krügel is a Professor at Technische Universität Wien, affiliated with the Department of Automation Systems (E191-03). His research focuses on Security, Intrusion Detection, Malware Detection, Operating Systems, and Networks. He leads multiple research projects and has supervised 20+ graduate students. His research explores malware analysis techniques, web security vulnerabilities, hardware-level attacks (e.g., Rowhammer), and privacy threats in mobile/social systems. Recent work examines ARM TrustZone security and machine learning limitations in malware detection, while earlier publications cover P2P networks, phishing defenses, and binary analysis. Awards: Wirtschaftskammerpreis (2005) EIB Scientific Award for Excellent Diploma Thesis (2000) Grants & Projects: Includes SECoverer (web app security, 2008-2011), WOMBAT (malware observatory, 2008-2011), Pathfinder (malware analysis, 2007-2009), and 5+ other funded initiatives focusing on binary analysis, pervasive security, and malware detection. He leads the Automation Systems research group, collaborating with institutions like European Commission and Austrian Science Fund.

• Computational Social Choice
• Voting Theory
• Multi-winner Elections
• +5 more

Martin Lackner is an Associate Professor in the Databases and Artificial Intelligence department at the Faculty of Informatics, Vienna University of Technology. His research focuses on computational social choice, voting theory, and algorithmic decision making, with particular expertise in multi-winner elections, approval-based voting systems, and participatory budgeting. He maintains an active research program with continuous publications in top AI and theoretical computer science venues. Lackner's research interests center on the theoretical foundations and practical implementations of fair decision-making systems. His work bridges theoretical computer science with practical applications in democratic processes, examining how computational methods can enhance fairness, representation, and efficiency in collective decision making. He has made significant contributions to understanding the computational complexity of voting rules, developing new algorithms for preference aggregation, and establishing axiomatic properties for multiwinner election methods. His recent publications demonstrate a consistent focus on fairness in long-term decision processes, with particular attention to perpetual voting systems, participatory budgeting mechanisms, and approval-based multiwinner rules. The research shows strong theoretical grounding combined with practical implementation considerations, as evidenced by his development of the abcvoting Python package for implementing approval-based voting rules. Lackner has received continuous funding for his research through multiple projects: SuDeMa (2019–2025): Algorithms for Sustainable Group Decision Making (Austrian Science Fund) FAIR (2013–2018): Fixed-Parameter Tractability in Artificial Intelligence and Reasoning (Austrian Science Fund) HINT (2012–2017): Heterogenous Information Integration (Austrian Science Fund) SEE (2012–2016): SPARQL Evaluation and Extensions (Vienna Science and Technology Fund) As an academic advisor, Lackner has supervised doctoral and master's students including J. Maly (2020) on ranking sets of objects and B. Krenn (2019) on algorithms for implicit delegation to predict preferences. His work appears in premier venues including AAAI, IJCAI, AAMAS, and the Journal of Artificial Intelligence Research, demonstrating both theoretical depth and practical relevance to democratic processes and collective decision making.