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Holzapfel Gerhard AView profile

Professor

• Biomechanics
• Mechanobiology
• Nonlinear Continuum Mechanics
• +4 more

Gerhard A. Holzapfel is a Full Professor of Biomechanics and Head of the Institute of Biomechanics at Graz University of Technology (Austria) since 2007. He holds adjunct and visiting positions at KTH Royal Institute of Technology (Sweden) and the University of Glasgow (Scotland). His academic journey includes a PhD in Mechanical Engineering from Graz University of Technology, a Habilitation from TU Vienna (1996), and prestigious awards such as the START-Award (1997) and the Warner T. Koiter Medal (2021). His research focuses on constitutive modeling of biosolids, nonlinear continuum mechanics, and medical image processing. Notable contributions include studies on arterial wall mechanics, fibrous tissue behavior, and computational biomechanics. Holzapfel leads interdisciplinary teams exploring mechanobiology, vascular pathologies, and biomaterials, with applications in cardiovascular engineering and disease modeling. Education: PhD in Mechanical Engineering, Graz University of Technology Habilitation, TU Vienna (1996) Research Highlights: Multi-scale modeling of biological tissues Biaxial mechanical testing and microscopy integration Computational fluid-structure interaction (FSI) in vascular systems Biomechanical implications of medical interventions (e.g., stents, angioplasty) Awards: Warner T. Koiter Medal (2021) Corresponding Member, Austrian Academy of Sciences (2012) Erwin Schrödinger Prize (2011) START-Award (1997) Labs & Teams: Leads the Institute of Biomechanics at TU Graz, collaborating with international teams on vascular biomechanics and mechanobiology.

• Biomechanics
• Soft Tissue Mechanics
• Cardiovascular Biomechanics
• +5 more

Gerhard Holzapfel is a Professor at TU Graz's Department of Biomechanics. His research focuses on biomechanics of soft biological tissues, cardiovascular systems, and advanced material modeling. He leads the Institute of Biomechanics, conducting experimental and computational studies on tissue mechanics, vascular diseases, and medical device design. Education : Details not explicitly provided in source text. Research Interests : Dr. Holzapfel's work spans constitutive modeling of soft tissues, computational fluid dynamics (CFD) in vascular systems, mechanobiology of cells and tissues, and biomaterials. He emphasizes translating biomechanical insights into clinical applications such as endovascular devices and surgical simulations. His studies often integrate microstructural analysis with macroscopic mechanical behavior to understand pathologies like atherosclerosis and aortic dissection. Publications : Recent work highlights advancements in fiber dispersion models for skin mechanics, Bayesian frameworks for material calibration, and CFD-driven evaluations of TEVAR (thoracic endovascular aortic repair). His articles emphasize predictive modeling of tissue behavior under various loading conditions, with applications in cardiology and regenerative medicine. Awards : No specific honors or fellowships mentioned in the provided text. Advising & Grants : No student/advisor relationships or grant details explicitly listed. The Institute of Biomechanics likely supports collaborative projects in biomechanical engineering and medical research. Labs/Teams : Director of TU Graz's Institute of Biomechanics, leading interdisciplinary teams in biomechanics, material science, and computational modeling. Collaborates with clinical partners on vascular biomechanics and surgical device development.

• Reinforcement Learning
• Markov Decision Processes
• Machine Learning
• +3 more

Ronald Ortner is a Professor and Chair of Information Technology, leading research in reinforcement learning, Markov decision processes, and computational learning theory. His work emphasizes theoretical foundations and practical applications in autonomous systems and optimization. He has published extensively since 2004, with notable contributions to bandit algorithms, regret analysis, and exploration strategies in dynamic environments. Research Focus: Reinforcement Learning, Markov Processes, Optimization Key Contributions: Regret bounds in MDPs, adaptive algorithms, transfer learning quantification Ortner engages in academic activities such as conference presentations and peer reviews, focusing on advancing algorithmic approaches in AI and machine learning. His research spans interdisciplinary areas including robotics, energy systems, and probabilistic modeling.

Warner MarzocchiView profile

Professor

• Seismic Hazard Analysis
• Volcanic Hazard Analysis
• Earthquake Forecasting
• +3 more

Warner Marzocchi is Professor of Geophysics and Natural Hazard Forecasting at the University of Naples Federico II and Scuola Superiore Meridionale. He holds a PhD in Physics (1992) and a degree in Earth Sciences (1987) from the University of Bologna. Previously, he served as Chief Scientist at the Istituto Nazionale di Geofisica e Vulcanologia (2003-2018) and held visiting positions at the University of Southern California and the Institute of Statistical Mathematics in Tokyo. His research focuses on seismic/volcanic hazard analysis, earthquake forecasting, statistical seismology, and hazard model validation. He coordinates international projects in natural hazard assessment and serves on advisory panels including the International Atomic Energy Agency, US National Seismic Hazard Model, and Global Volcano Model. Marzocchi's recent publications demonstrate advances in probabilistic forecasting models, operational earthquake forecasting systems, and statistical validation methods. His work frequently applies machine learning and complex systems theory to seismic and volcanic processes across Italy, California, and New Zealand. Honors: Ranked among world's top 2% scientists (2019-present) Member of Academia Europaea (2018) Editorial awards from Geophysical Journal International (2010) and Geophysical Research Letters (2003) He teaches graduate courses in geophysics, statistics, and hazard forecasting, and has advised numerous PhD students. He leads research teams focused on seismic risk mitigation and volcanic unrest management.

Miroslav StankovicView profile

Researcher

• Probabilistic Programming
• Invariant Synthesis
• Bayesian Networks
• +3 more

Miroslav Stankovic is a researcher at TU Wien's Research Area Cyber-Physical Systems , focusing on probabilistic programming, invariant synthesis, and moment-based analysis of probabilistic loops. His work bridges theoretical computer science and machine learning, particularly in analyzing Bayesian networks and distribution recovery.

• Knowledge Representation and Reasoning
• Data Engineering
• Database Theory
• +4 more

Eleonora Laurenza is a PostDoc Researcher at the Department of Databases and Artificial Intelligence, Vienna University of Technology (TU Wien). Her research focuses on the intersection of deductive logic-based and inductive methods in Artificial Intelligence, with a special emphasis on Knowledge Graphs, Data Engineering, and Reasoning Systems. She contributes to projects like KnowledgeGraph (2020–2028), DeConquer (2023–2027), and SustainGraph (2023–2025). Her work spans topics such as scalable reasoning in knowledge graphs, probabilistic reasoning frameworks, and applications in distributed ledger technology. Recent publications address FATEful smart contracts and model-independent knowledge graph design. She teaches courses like 'Knowledge Graphs' and 'Research and Career Planning for Doctoral Students.' Laurenza collaborates on interdisciplinary projects involving blockchain ethics, regulatory compliance systems, and uncertainty modeling in data systems. Her research bridges foundational theory with practical applications in AI-driven data management.

• Formal Methods
• Cyber-Physical Systems
• Probabilistic Programming
• +4 more

Ezio Bartocci is a Full Professor in Formal Methods for Cyber-Physical Systems Engineering at TU Wien's Faculty of Computer Science. He leads the Trustworthy Cyber-Physical Systems (TrustCPS) Group within the Cyber-Physical System Research Unit. His research focuses on formal verification, probabilistic systems, and runtime monitoring, with applications in autonomous systems, safety-critical software, and embedded systems. Roles & Affiliations: Full Professor, TU Wien (100% research focus) Principal Investigator in projects funded by EU, WWTF, FFG, and industry partners Chair of the Curriculum Commission for Computer Engineering Editor-in-Chief of the Formal Methods in Outer Space series Research Interests: Formal methods for CPS: verification, synthesis, and runtime monitoring Probabilistic programming and loop analysis Temporal logic specifications and mining Automated tools for safety-critical systems (e.g., Polar, MoonLight) Applications in healthcare, robotics, and autonomous vehicles Key Projects: ProbInG (2020–2025): Analyzing probabilistic loops ARTIST (2021–2026): AI and robotics safety EdgeAI (2022–2025): Optimizing embedded processing TAIGER (2023–2027): Trustworthy AI and CPS Grants & Funding: €10M+ secured from EU Horizon 2020, WWTF, FFG, and industry partners like TTTech Auto AG. Academic Leadership: Teaches courses on logical methods, CPS engineering, and scientific research at TU Wien. Supervises PhD students in formal methods and CPS domains. Tools Developed: Polar (probabilistic loop analyzer), MoonLight (spatio-temporal monitoring), and FIM (fault injection tool).

• Algorithms
• Graph Neural Networks
• Problem decomposition techniques
• +4 more

Matthias Paul Lanzinger is an Assistant Professor at the Technische Universität Wien's Faculty of Informatics, Department of Database and Artificial Intelligence. His research focuses on algorithms, graph neural networks, hypergraph decomposition techniques, parameterized complexity, and computational logic. He leads projects like 'DeConquer' (Vienna Science Fund) and 'HyperTrac', exploring efficient query processing and hypergraph-based algorithms. Research interests include theoretical computer science, database systems, and applying logical frameworks to solve complex computational problems. Recent work emphasizes hypertree decompositions, fuzzy Datalog, and graph motif analysis via the Weisfeiler-Leman test. He co-edited the 2024 Datalog-2.0 workshop proceedings and has supervised students on topics like column-store performance and graph query languages. His publications span venues like ACM Transactions on Database Systems, ICLR, and IJCAI, highlighting contributions to algorithmic efficiency, database theory, and logical reasoning systems. Active in academic service, he teaches courses on database systems, scientific research, and advanced topics in informatics.

• Software Engineering
• Machine Learning
• Static Code Analysis
• +3 more

Markus Böck is a PreDoc Researcher at the Software Engineering department of Technische Universität Wien (TU Wien), Austria. His academic work focuses on the intersection of software engineering and machine learning , particularly in performance prediction and probabilistic program analysis . He contributes to automated code analysis and intelligent software systems research. Role: PreDoc Researcher Department: Software Engineering, TU Wien Contact: markus.h.boeck@tuwien.ac.at His research explores: Language-agnostic static analysis of probabilistic programs Context-dependent performance prediction from source code Interactive machine learning systems for software engineering Distributional reinforcement learning applications Code-based performance modeling Program analysis for probabilistic systems Key publication trends include: Advancing probabilistic program analysis (2024-2025) Performance prediction techniques (2022-2023) Reinforcement learning innovations (2020-2022) Application of machine learning to software engineering challenges Game theory algorithm optimization (2025)

• Cyber-Physical Systems
• Machine Learning
• Autonomous Systems
• +5 more

Radu Grosu is a Professor at Technische Universität Wien (TU Wien), leading the Forschungsbereich Cyber-Physical Systems . His research focuses on Cyber-Physical Systems (CPS), Machine Learning, and autonomous robotics, with notable contributions to neural network architectures like Liquid Time-Constant Networks (LTC) and their applications in robotics and medical imaging. He is affiliated with the Network Lab and has supervised numerous PhD and Master's students, including Sebastian Michael Bittner, Daniel Scheuchenstuhl, and Sophie Neubauer. His work spans topics such as reinforcement learning, autonomous driving, and IoT ecosystems. Recent projects include developing robust AI systems for healthcare and robotics, such as tumor delineation using PET imaging and neuromorphic IoT architectures for smart villages. Grosu has published extensively on CPS, with over 146 contributions across peer-reviewed journals and conferences. His research emphasizes bridging theory and practice, addressing challenges in safety, scalability, and real-time control in autonomous systems. Key research interests include robotic perception, neural network robustness, and CPS/IoT integration. He has pioneered methods like DeepSTL for translating temporal logic requirements into neural network training objectives and developed frameworks like NimbleAI for neuromorphic sensing-processing systems. His team also explores distributed control algorithms for multi-agent systems, such as flocking drones and formation control using relative distance measurements. Recent work examines the generalization properties of deep filters in CNNs and quantum-classical reinforcement learning models for game AI. Grosu has advised over 20 students on topics ranging from deep learning in wafer defect analysis to bio-inspired neural circuits for auditable autonomy. His lab collaborates on interdisciplinary projects, such as applying AI to battery health estimation and prostate cancer diagnostics. He actively contributes to academic communities, editing special issues on AI in healthcare and CPS resilience, and has organized summer schools on CPS and IoT systems.

• Bayesian Statistics
• Time Series Analysis
• Econometrics
• +3 more

Gregor Kastner is Professor and Deputy Head of the Institute of Statistics at the University of Klagenfurt. His research focuses on Bayesian statistics, time series analysis, econometrics, and computational methods, with applications in finance, economics, and environmental modeling. He develops statistical software including packages for stochastic volatility modeling in R. Kastner's methodological work centers on Bayesian inference for high-dimensional problems, developing efficient computational algorithms for complex models. His applied research examines volatility dynamics in financial markets, macroeconomic forecasting, and spatial analysis of economic indicators. Recent projects include Bayesian nonparametric clustering for evaluating agricultural subsidies in Europe, sparse vector autoregressions for high-dimensional forecasting, and stochastic volatility models for commodity markets. He maintains active collaborations across economics, finance, and environmental science disciplines.

Milo TovaView profile

Professor

• Database Management
• XML and Semi-Structured Information
• Data Integration
• +3 more

Tova Milo is a Full Professor and Head of the Department of Computer Science at Tel Aviv University, where she has held academic roles since 1995. She specializes in database systems, XML, data integration, and crowd-sourcing. Her research bridges theoretical foundations and practical applications in data management. Education: Ph.D. in Computer Science from Hebrew University (1992). She has led major initiatives such as the ERC Advanced Investigators grant (MoDaS project) and holds ACM Fellow status. She chairs key committees like the ACM SIGACT-SIGMOD PODS executive committee and has organized over 50 international conference programs. Research interests focus on database management, XML, data-centric business processes, and leveraging crowd-sourcing for data tasks. Her work has been recognized with the ACM PODS Test-of-Time Award (2010) and an IBM Faculty Award (2008). Grants and funding include over 20 awards from the European Union, US-Israel Binational Science Foundation, and industry partners like IBM and Microsoft. She contributes to editorial boards of ACM Transactions on Database Systems and The VLDB Journal.

Pedro LarrañagaView profile

Professor

• Bayesian networks
• Estimation of distribution algorithms
• Sport analytics applications
• +6 more

Pedro Larrañaga is a Full Professor in Computer Science and Artificial Intelligence at the Technical University of Madrid (UPM) since 2007. He co-leads the Computational Intelligence Group at UPM. Previously, he held academic positions at the University of the Basque Country, including Professor, Associate Professor, Assistant Professor, and Lecturer roles in the Department of Computer Science and Artificial Intelligence from 1985–2007. Academic Rank: Professor Departments: Department of Artificial Intelligence (UPM), Department of Computer Science and Artificial Intelligence (University of the Basque Country) His research focuses on machine learning with applications in biomedical domains, neuroscience, industry, and sports analytics. Key contributions include structure learning algorithms for Bayesian networks, probabilistic graphical models for multi-dimensional classification, feature subset selection methods, and the development of estimation of distribution algorithms as an evolutionary computation metaheuristic. Research trends span machine learning methodologies and their interdisciplinary applications in Biomedicine, Bioinformatics, Neuroscience, Industry4.0, Sport Analytics , and Multi-label classification . Scientific Awards: National Prize of the Spanish Association for Artificial Intelligence (2018) Spanish National Prize in Computer Science, Aritmel Award (2013) ECAI-Fellow (2012)

• Statistical Modeling
• Machine Learning
• Data Clustering
• +3 more

Lukas Sablica, Ph.D., is a researcher affiliated with the Department of Statistics and Mathematics at WU Vienna University of Economics and Business. His work focuses on statistical methodology, computational statistics, and software development for statistical analysis. He is particularly known for contributions to probabilistic modeling, distribution sampling techniques, and the development of R packages such as circlus , mistr , and watson , which support advanced statistical analyses including circular/spherical clustering, mixture distributions, and Watson distribution modeling. Research interests include mathematical analysis of special functions, algorithmic approaches to distribution sampling, and applications of machine learning in environmental sensor data analysis. His interdisciplinary work bridges theoretical statistics with practical software solutions. Key contributions include methods for efficient sampling from complex distributions (e.g., PKBD, Watson), bounds analysis for Kummer’s functions, and machine learning models for estimating resource usage (e.g., EcoShower project). Collaborations involve experts in environmental science, computer science, and applied mathematics.

• Hydrology
• Remote Sensing
• Artificial Intelligence
• +3 more

Mathew Herrnegger is a Senior Scientist at the Institute of Hydrology and Water Resources Management (HyWa) at the University of Natural Resources and Life Sciences, Vienna (BOKU). He completed his doctorate in hydrology after studying Environmental Engineering and Water Resources Management. His research focuses on hydrological modeling, integration of remote sensing data, artificial intelligence, and machine learning in water management applications, climate change impacts on water resources, and big data analytics. Primary research areas: Hydrology, Water Resources Management, Climate Change, Artificial Intelligence, Remote Sensing, Machine Learning Dr. Herrnegger leads research groups since 2013 and has published extensively on hydrology and water management, particularly in alpine and East African contexts. His work includes flood risk assessment, groundwater recharge modeling, and soil erosion studies using advanced statistical and machine learning techniques. He actively participates in international conferences and collaborates with institutions in Kenya, Uganda, and Austria. His recent article analyses span from 2023 to 2025, covering glacier melt contributions to runoff under climate change, water quality degradation mapping, and extreme flood estimation using synthetic weather data. Key subtopics include snow-hydrology coupling, transboundary river basin management, and AI applications in stream temperature prediction. Dr. Herrnegger contributes to university teaching and supervises student theses while engaging in knowledge transfer through GIS training workshops in Uganda and Kenya. He maintains active roles in international hydrological conferences and local Austrian climate change initiatives.