جستجوی استاد راهنمای دانشگاهی

Stefan Huber is Professor and Head of Research at the Department for Information Technologies and Digitalisation at Salzburg University of Applied Sciences. He also serves as Research Group Leader and Head of the Josef Ressel Center for Intelligent and Secure Industrial Automation, a €2.5M research center established in 2022 focusing on digital assistants for industrial machines with research fields in system architectures, artificial intelligence, and cybersecurity for operational technology. His research spans multiple domains including Machine Learning , Industrial Automation , Cyber Security , Computational Geometry , and Algorithm Theory . Huber's work has evolved chronologically from theoretical computational geometry to practical applications in industrial automation systems. His current research focuses on AI applications in industrial settings, particularly in Industry 4.0 contexts, with significant contributions to OPC UA security, reinforcement learning for control systems, and time series forecasting for industrial processes. Analysis of his recent publications (2023-2025) reveals a strong focus on practical AI applications in industrial automation, with particular emphasis on security aspects of operational technology, predictive modeling for manufacturing processes, and integration of machine learning techniques into industrial control systems. His work bridges theoretical computer science with real-world industrial applications. Excellence rating for Josef Ressel Centre evaluation (2024) Recognition for successful high-tech research (2024) Huber leads multiple significant research projects including AI4GREEN (Data Science for Sustainability, 2024-2027), JRZ ISIA (Josef Ressel Centre for Intelligent Industry Automation, 2022-2027), and IAI (Industrial Artificial Intelligence, 2023-2024). His research group at the Josef Ressel Centre comprises numerous researchers working on system architectures, AI, and cybersecurity for industrial automation. The center has received positive evaluations and media coverage for its high-tech research contributions to the field.

• Machine Learning
• Artificial Intelligence
• Industrial Automation
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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.

• Data Mining
• Machine Learning with Graphs
• Combinatorial Optimization
• +۵ مورد دیگر

Olga Saukh is an Associate Professor at TU Graz's Institute of Computer Engineering, leading the Embedded Learning and Sensing Systems (ELSS) group. Her research focuses on resource-efficient AI, on-device learning, and robust sensing systems for IoT and environmental monitoring applications. She holds a Dr.rer.nat. and MSc in Computer Science, with expertise in embedded systems and wireless sensor networks. Her work integrates machine learning with hardware constraints, addressing challenges in energy efficiency, real-time adaptation, and adversarial robustness. Notable projects include PCDCNet for air quality forecasting and SensorFormer for sensor calibration. She has contributed to OpenSense Zurich's air pollution monitoring and automated pollen sensing systems. Her research spans over 50 publications since 2006, emphasizing practical deployments in structural health monitoring, smart agriculture, and urban environmental sensing. She leads interdisciplinary projects combining AI, embedded hardware, and data-driven decision-making.

• Resource-efficient machine learning
• Data-efficient machine learning
• On-device learning
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Overview Pedro Hermosilla Casajus is an Assistant Professor at the Department of Computer Vision within the Faculty of Informatics at Technische Universität Wien (TU Wien). His research focuses on advanced computer vision techniques with applications in medical imaging, 3D scene understanding, and deep learning methodologies. He is actively involved in teaching multiple courses including Fundamentals of Computer Vision, Deep Learning for Visual Computing, and Scientific Research and Writing. Projects & Collaborations MyeFLOW (2020–2025) : Developing automated analysis systems for flow cytometry data. EVOCATION (2018–2022) : Real-time shape acquisition technologies. Modeling the World at Scale (2020–2026) : Large-scale 3D modeling techniques. Research Highlights His work bridges computer vision with medical diagnostics, particularly in measurable residual disease detection using flow cytometry. He explores geometric deep learning for 3D point cloud analysis and scene graph generation, advancing unsupervised semantic segmentation methods. Recent contributions include stylized Gaussian splatting for neural rendering and open-vocabulary 3D scene understanding frameworks. Teaching He teaches core courses like Fundamentals of Computer Vision and supervises student projects in medical informatics, visual computing, and human-centered computing.

• Computer Vision
• Medical Informatics
• 3D Scene Understanding
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Aleksandar Pavlović is a researcher at the Institute of Computer Sciences specializing in knowledge graph technologies and Datalog-based systems. His work bridges semantic web standards with machine learning methodologies to advance knowledge representation and query processing efficiency. Research interests focus on: Knowledge graph embedding techniques with spatial and geometric interpretations Datalog optimization for SPARQL query evaluation Integration of graph neural networks with symbolic reasoning systems Interoperability solutions for semantic web technologies Recent publications (2023-2024) demonstrate consistent innovation in knowledge graph completion and reasoning, featuring novel embedding architectures like ExpressivE and region-based neural networks. His work shows strong alignment with database community priorities while pushing boundaries in neural-symbolic integration. Collaborations span international institutions through co-authorships with researchers including Sallinger, Schockaert, Angles, and Gottlob. He actively contributes to academic discourse through conference presentations, including recent work on AI-supported language services for long-term care systems.

• Knowledge Graphs
• Datalog
• Database Systems
• +۴ مورد دیگر

Dr. Henry von Wahl is a Lecturer (akademischer Rat) in the group of Dietmar Gallistl at Friedrich Schiller University Jena , Germany, affiliated with the Faculty of Mathematics and Computer Science . His research focuses on advanced numerical methods for partial differential equations (PDEs), particularly unfitted finite elements (CutFEM), discontinuous Galerkin (DG), and high-order FEM, applied to fluid dynamics, fluid-structure interactions, moving domains, and atmospheric flows. Education : Dr.rer.nat. in Mathematics (Otto von Guericke University Magdeburg, 2021), M.Sc. in Mathematics (University of Göttingen, 2018), B.Sc. in Mathematics (University of Göttingen, 2016). Henry von Wahl's research interests center on computational methods for complex fluid dynamics and multiphysics problems. Key areas include: Finite Element Methods (CutFEM, high-order FEM, unfitted discretizations). Discontinuous Galerkin approaches for atmospheric and moving domain problems. Phase-Field Modeling of fractures and fluid-structure interactions. Deep Learning integration for underresolved flow predictions. His publication trends emphasize Eulerian finite element frameworks for moving boundaries, conservative transport schemes, and high-accuracy phase-field models. Collaborations with researchers like Thomas Richter and Thomas Wick highlight applications in Stokes flows, atmospheric dynamics, and fracture mechanics. Currently, he teaches courses at Friedrich Schiller University Jena, including Numerical Analysis of instationary PDEs and Numerik von Randwertproblemen . He has also held postdoctoral fellowships at the University of Vienna and ICERM, Brown University.

• Numerical Methods for Partial Differential Equations
• Finite Element Methods
• Discontinuous Galerkin Methods
• +۵ مورد دیگر

Raphaël Tinarrage is a Research Fellow at the Institute of Science and Technology Austria (ISTA), part of the Wagner group. Previously, he held a postdoctoral position at FGV EMAp (Brazil) and was an Assistant Professor at Université Paris-Saclay (2017–2020). His research focuses on Topological Data Analysis (TDA), including theoretical advancements and practical applications in medical imaging, legal document analysis, and equivariant neural networks. Education: He earned his PhD from Université Paris-Saclay and Inria, advised by Fred Chazal and Marc Glisse (2017–2020). His dissertation, Topological Inference from Measures and Vector Bundles , introduced novel methods for topological data analysis. Research Interests: Tinarrage explores topological inference (e.g., stability of persistent homology), combinatorial topology (triangulation, Lusternik–Schnirelmann category), and applications like medical image segmentation and legal text analysis. His work bridges theoretical mathematics with computational tools, emphasizing practical implementations. Key Publications (2021–2025): Recent articles include LieDetect (2025) for Lie group symmetry detection, Empirical Analysis of Brazilian Supreme Court Precedents (2025), and ZigzagNetVis (2025) for dynamic graph visualization. Preprints include Train-Free Segmentation in MRI (2024). Teaching & Advising: He taught courses on topology and TDA at FGV EMAp and Université Paris-Saclay. He advised multiple students on projects involving legal NLP and TDA applications. Labs & Tools: His PhD work is implemented in the velour Python package. Code for other projects is available on his GitHub repository.

• Topological Data Analysis
• Combinatorial Topology
• Medical Image Analysis
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Krzysztof Janowicz is a Professor at the University of Vienna and Head of the Department of Geography and Regional Research. Previously, he held full professorships at the University of California, Santa Barbara (UCSB) and Pennsylvania State University. He directs the Research Network Data Science and is Editor-in-Chief of the Semantic Web journal. His research focuses on Spatial Data Science, GeoAI, and knowledge graphs, emphasizing the integration of semantic and data-driven approaches. Education includes a PhD in Geoinformatics from the University of Münster and postdoctoral work at the Institute for Geoinformatics. Teaching includes courses on spatial data science, cartography, and geoinformatics, such as field trips to Scotland and North America. He leads projects like the KnowWhereGraph, a large-scale geo-knowledge graph for interdisciplinary applications. His work addresses AI sustainability, geographic diversity evaluation, and privacy in geospatial AI. He has directed UCSB’s Center for Spatial Studies and contributed to disaster risk management frameworks through ontologies like HIP.

• Spatial Data Science
• Artificial Intelligence (AI)
• Geographic Information Science (GIScience)
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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.

• Machine Learning
• Graph Neural Networks
• Recommender Systems
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Philipp Erler is a PreDoc Researcher at the Computer Graphics department of Vienna University of Technology . His work focuses on surface reconstruction from point clouds, geometry processing, and deep learning applications in computer graphics. Research Highlights : LidarScout (2025): Direct out-of-core rendering of massive point clouds PPSurf (2024): Patch-based deep learning for surface reconstruction Points2Surf (2020): Implicit surface learning from raw point clouds without normals His research extends into virtual reality, physical simulations, and natural language processing. He has supervised four theses on topics including surface reconstruction optimization and differentiable rendering.

• Surface Reconstruction
• Point Clouds
• Deep Learning
• +۴ مورد دیگر

Michael Bronstein is a Professor & Chair in Machine Learning and Pattern Recognition at the Department of Computing, Imperial College London (since 2018), with a concurrent professorship at the Institute for Computational Science, University of Lugano, Switzerland (since 2010, currently on leave). His academic career includes visiting positions at Stanford University (Visiting Lecturer, 2008-2009), Tel Aviv University (Visiting Associate Professor, 2015-2017), Harvard University (Visiting Scholar, 2017-2018), and MIT (Research Affiliate, 2017-2018). Dr. Bronstein's research focuses on theoretical and computational geometric methods for data analysis, with applications spanning machine learning, computer vision, graphics, geometry processing, biology, and social networks. His work in geometric deep learning has established foundational frameworks for processing non-Euclidean data such as graphs and manifolds, bridging the gap between traditional Euclidean deep learning and complex structured data. His publication record demonstrates a clear trajectory from fundamental geometric methods to their application in diverse domains. Early work focused on 3D shape analysis and non-rigid shape recognition, while more recent publications showcase the application of geometric deep learning to protein structure analysis, point cloud processing, and graph-based machine learning. The consistent thread throughout his research is the development of mathematical frameworks that respect the intrinsic geometry of data. Royal Academy of Engineering Silver Medal (2020) Fellow of IEEE, IAPR, and ELLIS Five ERC grants (Starting 2012, Consolidator 2016, and Proof of Concept 2016, 2018, 2019) Royal Society Wolfson Research Merit Award Young Scientist, World Economic Forum (2014) Bronstein has successfully translated academic research into practical applications through multiple entrepreneurial ventures. As ML Lead for Project CETI and Head of Graph Learning Research at Twitter (since 2019), he bridges academic research with industrial applications. His previous startups include Fabula AI (acquired by Twitter in 2019) and Invision (acquired by Intel in 2012), where he contributed to the development of Intel RealSense 3D camera technology. His research has been supported by significant funding including ERC grants and industry awards from Google, Amazon, and Facebook.

• Geometric machine learning
• Deep learning on graphs
• 3D shape analysis
• +۷ مورد دیگر

David Schinagl is affiliated with the Institute of Visual Computing at TU Graz. His research focuses on LiDAR-based 3D object detection , explainable AI , and autonomous driving technologies . He has contributed to methods like OccAM's Laser for model interpretability and MAELi for large-scale LiDAR processing. His work emphasizes sensor data analysis and geometric confidence enhancement. Publications span top venues including CVPR, ICCV, and WACV (2018–2024). His GitHub repository demonstrates implementations like the OccAM's Laser demo for LiDAR attribution maps.

• Computer Vision
• Autonomous Driving
• 3D Object Detection
• +۳ مورد دیگر

Thomas Gärtner is a Full Professor of Machine Learning at TU Wien (Technical University of Vienna), leading the Machine Learning Research Unit (E 194-06). He previously held professorships at the University of Nottingham and the University of Bonn. His research focuses on efficient and effective machine learning algorithms, particularly in structured data domains like graphs and chemical compounds. Key areas include kernel methods, active learning, and graph neural networks. He is involved in projects such as NanoX (Austrian Science Fund) and StruDL (Vienna Science Fund), addressing challenges in structured data learning and trustworthy AI. Education: PhD from University of Bonn (2005), MSc from University of Bristol (2000), Diplom from University of Cooperative Education (1999). Research Interests: Machine Learning, Data Mining, Kernel Methods, Chemoinformatics, Graph Neural Networks, Active Learning, and Constructive Machine Learning. His work emphasizes real-world applications in chemistry and computer games. Grants & Awards: Emmy Noether Grant (2010), Best Paper Award (2016), ICLR Area Chair (2021), and recognition for contributions to EPSRC peer review (2020). Teaching: Offers courses like 'Introduction to Machine Learning' and 'Theoretical Foundations of Machine Learning.' Supervises PhD and diploma students in advanced topics such as graph algorithms and neural network distillation. Lab/Teams: Heads the Machine Learning Research Unit and collaborates with the competence center ML2R (Rhine-Ruhr region). Active in organizing ECML PKDD conferences and workshops on constructive machine learning.

• Machine Learning
• Data Mining
• Graph Neural Networks
• +۵ مورد دیگر

Martin Ehler serves as Associate Professor and Vice-Dean for Teaching at the University of Vienna's Faculty of Mathematics, where he concurrently holds the position of Deputy Head of the Institute of Mathematics. His academic activities are centered at Oskar-Morgenstern-Platz 1, Room 10.128, Vienna, with primary responsibilities spanning research leadership, teaching undergraduate/graduate mathematics courses, and administrative oversight of academic programs. His research program in Computational Harmonic Analysis drives innovations in Mathematical Data Analysis and Medical Image Processing , evidenced by his leadership in the Applied Harmonic Analysis Cluster (AHA) and co-organization of the bi-annual Strobl conference. Core methodologies integrate frame theory, manifold approximation, and spherical design principles to address challenges in neural network stability, medical imaging reconstruction, and signal processing robustness. Analysis of his 2023-2025 publications reveals dominant themes in harmonic analysis applications to deep learning architectures (particularly ReLU-layer injectivity and filterbank design), geometric approximation on manifolds, and medical imaging algorithms. These works demonstrate consistent interdisciplinary collaboration across mathematics, signal processing, and biomedical engineering domains. No scientific awards were documented in the provided materials. While teaching responsibilities for courses like Introduction to Analysis and Mathematics of Data Science are explicitly listed, details regarding graduate student supervision or research grant funding were absent from the source texts. His research ecosystem is anchored in the Applied Harmonic Analysis Cluster (AHA), facilitating cross-institutional collaboration on harmonic analysis applications, with documented partnerships through co-authored publications in venues like IEEE Signal Processing Letters and Mathematics of Computation.

• Computational Harmonic Analysis
• Mathematical Data Analysis
• Image Processing
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Dr. Thomas Takacs is a Project Leader in the Geometry in Simulations research group at the RICAM (Research Institute for Symbolic Computation), part of the Austrian Academy of Sciences. His work focuses on advancing computational methods for solving partial differential equations, particularly through isogeometric analysis and spline-based numerical techniques . His research interests include smooth basis constructions, multi-patch domain coupling, and adaptive mesh refinement strategies. Key contributions involve developing C1-continuous spline spaces over planar mixed meshes and unstructured quadrilateral meshes, with applications to engineering problems like linear elasticity. He explores the integration of machine learning (e.g., CNNs) for optimizing quad meshing and employs artificial neural networks in adaptive optimization frameworks. His work bridges geometric modeling and numerical analysis, addressing challenges such as singularity treatment in isogeometric analysis and approximation properties over self-similar meshes. Dr. Takacs collaborates on multi-institutional projects, including the Geometry in Simulations group at RICAM. His publications span computational mechanics, numerical methods, and geometric algorithms, with a focus on advancing the theoretical foundations and practical implementations of isogeometric analysis.

• Isogeometric Analysis
• Computational Mechanics
• Numerical Analysis
• +۳ مورد دیگر