جست‌وجوی استاد و پژوهشگر

• Computational Chemistry
• Molecular Dynamics
• Machine Learning Applications
• +۳ مورد دیگر

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 Jovanovikjمشاهده پروفایل

دانشیار

• Knowledge Graphs
• Data Engineering
• Semantic Web
• +۸ مورد دیگر

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 Schälerمشاهده پروفایل

دانشیار

• Database Systems
• Scientific Data Management
• Hardware-sensitive Database Tuning
• +۵ مورد دیگر

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 Hollausمشاهده پروفایل

مدرس

• Sports Technology
• Machine Learning
• Sensor Systems
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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
• +۲ مورد دیگر

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
• +۵ مورد دیگر

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.

• Cyber-Physical Systems
• Runtime Verification
• Formal Methods
• +۵ مورد دیگر

Dejan Nickovic is a Professor affiliated with the Department of Cyber-Physical Systems at TU Wien. His research focuses on runtime verification, formal methods for cyber-physical systems (CPS), and software testing. He leads projects such as TAIGER (2023–2027) and ARTIST (2021–2026), advancing CPS safety and AI robustness. His work bridges theoretical formalisms with practical applications in robotics, IoT, and embedded systems. Key research themes include specification mining, fault localization, and automated verification. He has developed tools like DeepRIoT for robotic-IoT integration and FIM for Simulink fault analysis. His professorial dissertation (2020) formalized runtime verification techniques for CPS. Recent publications emphasize adaptive architectures, backdoor detection in AI, and hyperproperties for security. He collaborates extensively with academia and industry, addressing challenges in CPS testing and verification through grants like ZK 35-G (2019–2024). His lab supports platforms like CPS/IoT Ecosystem for research and education. Projects: TAIGER, ARTIST, EdgeAI, ProbInG Supervised students: N. Manjunath (PhD, 2021), Florian Exenberger (2019), Markus Heindl (2017)

Stefan Ohrhallingerمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Geometry Processing
• Surface Reconstruction
• +۳ مورد دیگر

Stefan Ohrhallinger is a Senior Researcher at TU Wien's Institute of Visual Computing and Human-Centered Technology. He holds a PhD from Concordia University (2013) and a Master's in Business Informatics from Johannes Kepler University Linz (1997). His research focuses on surface reconstruction, point-based graphics, and geometry processing with applications in computer graphics and virtual/augmented reality. Education: PhD in Computer Science, Concordia University (2013) Master of Business Informatics, Johannes Kepler University Linz (1997) Affiliations: Principal Investigator for grants totaling €1.7M+ (e.g., WWTF Modeling the World at Scale, FWF Superhumans) Teaching: 'Modeling in Computer Graphics' and 'Scientific Writing' courses His work emphasizes robust algorithms for reconstructing curves/surfaces from sparse/noisy data, with contributions to occlusion-aware processing and large-scale point cloud handling. Notable achievements include the Best Poster Award at Eurographics 2012 and over 30 peer-reviewed publications. Grants & Projects: WWTF 'Modeling the World at Scale' (2020–2026, €578k) FWF 'Superhumans: Walking Through Walls' (2019–2023, €333k) IPA 'Photogrammetry made easy' (2020–2022, €50k)

• Condensed Matter Physics
• Quantum Many-Body Systems
• Theoretical Physics
• +۳ مورد دیگر

Miriam Patricolo is a Researcher at TU Wien's Department of Solid State Physics within the Faculty of Physics. Her work focuses on theoretical condensed matter physics, particularly quantum many-body systems and functional renormalization group (fRG) techniques. She contributes to the Quantenspinsysteme P7 project (2024–2027) and actively publishes on Schwinger-Dyson equations and the Hubbard model. Her research explores pseudogap phenomena in strong-coupling regimes and integrates machine learning methodologies. Education details are not explicitly provided. Professional activities include organizing the 2025S Journal Club on Solid State Physics: Theory and Experiment. Her presentations at international conferences (e.g., SciPost Physics, DPG 2024) and workshops highlight her expertise in renormalization group methods and quantum materials.

• Machine Learning
• Graph Neural Networks
• Recommender Systems
• +۳ مورد دیگر

David Penz is a PreDoc Researcher at Technische Universität Wien (TU Wien), affiliated with the Faculty of Informatics and the Department of Machine Learning. He teaches courses such as Introduction to Machine Learning , Machine Learning Algorithms and Applications , and Theoretical Foundations and Research Topics in Machine Learning . His research focuses on advancing Graph Neural Networks (GNNs), particularly for outerplanar graphs, and developing ethical AI systems in recommendation engines. His work emphasizes data privacy, fairness in AI, and music information retrieval. Key projects include the StruDL initiative (2023–2027), exploring maximally expressive GNN architectures. He has supervised a diploma thesis on antibody-antigen binding affinity prediction using geometric deep learning. Recent publications highlight contributions to unlearning sensitive user attributes in recommendations and emotion-aware music recommendation systems. His academic output spans conferences like NeurIPS, SIGIR, and ICMR, demonstrating expertise in both theoretical and applied machine learning domains. He actively participates in academic challenges, such as the ACM Recommender Systems Challenge 2021, showcasing lightweight XGBoost approaches.

• Information Retrieval
• Data Science
• Medical Informatics
• +۳ مورد دیگر

Florina Piroi is a Senior Researcher at the Technische Universität Wien (TU Wien), affiliated with the Faculty of Informatics and the Department of Data Science. She holds the role of Senior Scientist in Data Science and is a Substitute Member of the Curriculum Commission for Business Informatics. Her primary research focuses on information retrieval, medical informatics, natural language processing, and patent text mining. She leads projects such as the CLEF-IP evaluation lab and contributes to initiatives like the DoSSIER and OS Trails research programs. Key research interests include longitudinal evaluation of machine learning models, reproducibility in NLP tasks, and knowledge graph applications in manufacturing. She has supervised PhD student Anindita M. Ningtyas, whose work addresses medical terminology accessibility for laypeople. Piroi collaborates on interdisciplinary projects, such as designing health datasets from medical forums and improving search systems for evolving corpora. Her notable contributions include benchmarking frameworks for IR systems, semantic translation tools for industry, and methods for analyzing electrical systems. She has published over 50 peer-reviewed articles in venues like SIGIR, CLEF, and ECIR, emphasizing practical applications in both academic and industrial contexts.

• Artificial Intelligence
• Database Systems
• Machine Learning
• +۱ مورد دیگر

Domenec Puig is a Visiting Scientist at the Faculty of Informatics, Technische Universität Wien (TU Wien), affiliated with the Databases and Artificial Intelligence department (E192-02). His research interests include artificial intelligence, database systems, and machine learning. He can be contacted via domenec.puig@tuwien.ac.at or through his university profile at informatics.tuwien.ac.at .

• Parameterized Complexity
• Graph Theory
• Artificial Intelligence
• +۳ مورد دیگر

Mathis Teva Rocton is a PreDoc Researcher at the Department of Algorithms and Complexity, Technische Universität Wien (TU Wien). He holds an MSc and the title of Univ.Ass. His research focuses on parameterized complexity, graph theory, and theoretical computer science with applications in artificial intelligence. Rocton is involved in projects such as 'Parameterisierte Analyse in der Künstlichen Intelligenz' (2021–2026) and 'Parameterized Graph Drawing' (2023–2027). His recent work explores twin-width concepts, feedback edge parameters, and neural network training complexity. Notable publications include contributions to IPEC 2024, STACS 2024, and NeurIPS 2023. He has supervised diploma theses, including David Jäger's work on 'Turbocharging twin-width heuristics with SAT' (2024). Rocton's research bridges algorithmic theory and practical applications in AI.