جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Computational Linguistics
• Information Extraction
• +۳ مورد دیگر

Gábor Recski is a computational linguist and postdoctoral researcher at TU Wien's Faculty of Informatics, affiliated with the Data Science Research Unit. He holds a PhD and previously served as an assistant professor at the Budapest University of Technology. His research focuses on natural language processing (NLP), particularly computational semantics and transparent information extraction. He teaches courses such as Natural Language Processing and Information Extraction, and supervises students at BSc, MSc, and PhD levels. His work includes over 50 peer-reviewed publications and projects like BRISE-Vienna (2019–2025) and Honeypot LLM: Creation of a Scam Conversation Dataset. Roles: PostDoc Researcher, Substitute Member of Faculty Council, Substitute Member of Curriculum Commission for Business Informatics Education: PhD (previous role at Budapest University of Technology) Research Interests: Transparent information extraction, semantic modeling, legal text analysis, and explainable AI His research projects span legal corpus construction (e.g., BRISE-plandok), offensive text detection, and AI applications in legal research. He actively contributes to conferences like ACL, EMNLP, and SIGIR. Supervised students have explored topics ranging from explainable prediction models to graph-based relation extraction.

• Artificial Intelligence
• Knowledge Compilation
• Computational Complexity
• +۳ مورد دیگر

Tianwei Zhang is a predoctoral researcher at the Technische Universität Wien (TU Wien) within the Algorithms and Complexity department. His research focuses on theoretical computer science, particularly in the areas of artificial intelligence, knowledge compilation, and computational complexity. Position: PreDoc Researcher Projects: Overcoming Intractability in the Knowledge Compilation Map (2022–2025), REVEAL-AI (2020–2024) Zhang's work explores advanced techniques in satisfiability solving, constraint programming, and universal graph theory. His recent publications highlight contributions to model counting algorithms and SAT-based combinatorial optimization, bridging theoretical challenges with practical applications. His research trends emphasize scalable algorithms , computational intractability , and logic-based AI . Notable collaborations include work with Dr. Stefan Szeider on SAT solver applications for combinatorics. Contact: tianwei.zhang@tuwien.ac.at Phone: +43-1-58801-192154

Neha Lodhaمشاهده پروفایل

پژوهشگر

• Algorithms and Complexity
• Graph Theory
• SAT/SMT Solving
• +۳ مورد دیگر

Neha Lodha is a researcher in the Institut für Logic and Computation at TU Wien. Her work focuses on algorithms, complexity theory, and SAT/SMT solving techniques. She has contributed to graph encodings for combinatorial optimization problems and parameterized complexity analysis. Key research areas include SAT-based approaches for graph decomposition (branchwidth, treewidth), SMT methods for fractional hypertree width, and algorithm engineering for constraint satisfaction problems. Her work bridges theoretical foundations with practical algorithmic implementations. Notably, she received the 2016 SAT Conference Best Student Paper Award for her work on SAT encodings of branchwidth. This research was later expanded into a 2019 ACM Transactions publication. Her publications span conferences like IJCAI, CP, and SAT, with a focus on advancing the theoretical and practical aspects of computational logic and algorithm design.

Eduard Eibenمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Algorithms
• Parameterized Complexity
• +۳ مورد دیگر

Eduard Eiben is associated with the Algorithms and Complexity group at the Institut für Logic and Computation, Technische Universität Wien. He completed his PhD under advisors Stefan Szeider, Robert Ganian, and Georg Gottlob. His research focused on exploiting structural properties for fixed-parameter tractability, supported by the Austrian Science Fund (FWF) through projects X-Tract (FWF P26696) and the LogiCS Doctoral College (FWF W1255). In 2018, he received the Award of Excellence for his outstanding PhD thesis from the Austrian Ministry of Education, Science and Research (BMBWF).

• Applied Mathematics
• Graph Theory
• Material Science
• +۳ مورد دیگر

Clemens Brand is a Professor and Chair of Applied Mathematics at Montanuniversität Leoben. His research spans applied mathematics, graph theory, and material science with notable contributions to bulk material analysis and hydrodynamic lubrication modeling. He has authored 29 publications and supervised 26 works. Brand actively participates in international conferences such as the Leoben-Laibach Seminar series on Graph Theory. His academic career includes habilitation and doctoral qualifications (Dr.rer.nat.). Research interests include computational methods in material science, tribology, and mathematics education. He has developed textbooks for secondary education and contributed to advanced topics like surface effect modeling in lubrication systems. His work bridges theoretical mathematics with engineering applications. Professional activities include collaborations with institutions like the Bergakademie Krakau and participation in international academic networks. His research outputs emphasize peer-reviewed articles and interdisciplinary approaches to mathematical problems in engineering contexts.

• Recommender Systems
• Digital Humanism
• User Modeling
• +۵ مورد دیگر

Julia Neidhardt is an Assistant Professor at TU Wien Informatics, specializing in Information Systems Engineering. Her research focuses on recommender systems, digital humanism, and user modeling, particularly in tourism and news domains. She leads the Christian Doppler Lab for Recommender Systems and is a UNESCO Co-Chair on Digital Humanism. Neidhardt holds a background in mathematics and computer science, with postdoctoral research at the Austrian Academy of Sciences and visiting scholar roles at Northwestern University and the University of Geneva. Roles: Assistant Professor, Leader of Christian Doppler Lab for Recommender Systems, Board Member of CAIML Education: Dr. techn. (PhD) and Mag.a rer.nat. (Master's) in relevant fields Her research emphasizes ethical AI, fairness in recommendations, and human-centered design. Notable contributions include work on sentiment analysis in online forums, serendipity in recommendations, and offensive language detection in multilingual contexts. She actively chairs conferences like ACM UMAP and RecSys, and serves on editorial boards for journals like the Journal of Information Technology & Tourism. Grants/Projects: Leads the CDL Rec-Sys Lab (2022–2028), Excalibur (2022–2023), and projects on digital humanism and tourism technology. Her work integrates network analysis, computational linguistics, and empirical studies to address societal impacts of AI. Awards: Distinguished Reviewer of ACM TORS, UNESCO Co-Chair, and top-tier conference involvement.

• Formal Methods
• Program Analysis
• Verification
• +۹ مورد دیگر

Florian Zuleger is an Associate Professor at TU Wien's Department of Formal Methods in Systems Engineering (E192-04). He has a 100% research focus and serves as Curriculum Coordinator for the Master’s program in Verification and Automated Reasoning. His work emphasizes automated methods for termination analysis, resource-bound estimation, verification of programs with dynamic data structures, parameterized systems, and automated feedback for introductory programming tasks. He has led projects funded by the European Commission (2024–2027), Austrian Science Fund (FWF), Amazon Research Awards, and Vienna Science and Technology Fund. Research Interests: Automated Program Verification Formal Methods for Concurrent Systems Separation Logic and Decision Procedures Resource and Complexity Analysis Parameterized Model Checking Inductive Logic in Verification Grants & Advising: Zuleger’s recent grants explore verification of safety-critical applications and automated cost analysis. He has advised numerous students on topics ranging from verified data structures to fault-tolerant algorithms. His projects include collaboration with Amazon, the European Commission, and FWF. Labs/Teams: Involved with the LogiCS Research Group, focusing on logical methods in computer science, and contributes to tool development (e.g., ATLAS, SpecBMC, SL-COMP competitions).

Stefan Szeiderمشاهده پروفایل

استاد

• Algorithms
• Computational Complexity
• Theoretical Computer Science
• +۴ مورد دیگر

Stefan Szeider is a full professor and chair of the Algorithms and Complexity Group at the Faculty of Informatics, Technische Universität Wien (TU Wien). He also serves as a visiting scientist at UC Berkeley's Simons Institute for the Theory of Computing. His academic journey includes positions at the University of Durham (UK) and the University of Toronto (Canada), and he earned his Mathematics PhD from the University of Vienna in 2001. Dr. Szeider's research focuses on designing efficient algorithms for problems in Artificial Intelligence, automated reasoning, and combinatorial optimization. He leads several initiatives, including the Vienna Center for Logic and Algorithms (VCLA), and has secured funding from the ERC, EPSRC, FWF, and others. His Erdős number is 2, reflecting his collaborative network in mathematics and computer science. Key achievements include the first ERC Starting Grant awarded to an Austrian computer scientist (2009), and awards such as the Highlighted Paper Award at SAT 2023 and Best Paper at CP 2020. He advises numerous PhD students and postdocs, fostering the next generation of researchers in algorithms and complexity. Notable contributions extend beyond academia to public outreach, including initiatives like the 'Algorithms Think Differently' educational program and the 'Algorithms in 60 Seconds' video competition. His work bridges theoretical foundations and practical applications, influencing both academic and real-world computational challenges.

• Computer Science
• Data Visualization
• Information Systems
• +۳ مورد دیگر

Sebastian Philipp Adam is a PreDoc Researcher at the Department of Knowledge-Based Systems within the Faculty of Informatics at TU Wien. His research focuses on advanced visualization techniques, particularly hypergraph representation using dynamic and spatial methods. Education: Bachelor of Science (BSc) His work emphasizes improving the clarity and interactivity of complex data visualizations through innovative approaches such as 'fat edges' and dynamic object positioning. His 2023 thesis explores elastic set visualization methodologies. Grants & Advising: No grants or advising roles explicitly stated in available information. Labs/Teams: Affiliated with the Knowledge-Based Systems research group at TU Wien, contributing to interdisciplinary projects in computational logic and information systems.

• Theoretical Computer Science
• Graph Algorithms
• Computational Geometry
• +۳ مورد دیگر

Thomas Depian is a PreDoc Researcher affiliated with the Faculty of Informatics at Technische Universität Wien, specializing in the Department of Algorithms and Complexity. His research focuses on computational geometry, graph algorithms, parameterized complexity, and data visualization, with particular emphasis on boundary labeling, dynamic map labeling, and linear layout optimization. Current projects: Engineering Linear Ordering Algorithms for Optimizing Data Visualizations (2020–2025) Past projects: HumAlgo (2018–2023) His work spans theoretical algorithm design, complexity analysis, and practical applications in geographic information systems and data visualization. Publications include contributions to top-tier conferences such as ISAAC, GD, GIScience, and WALCOM. Areas of investigation include constraint satisfaction, geometric optimization, and parameterized algorithm frameworks. Depian completed his Diploma Thesis at TU Wien in 2023, focusing on grouping and ordering constraints in boundary labeling. His academic output demonstrates expertise in solving complex computational problems with interdisciplinary applications.

• Algorithms
• Graph Drawing
• Computational Geometry
• +۳ مورد دیگر

Alexander Dobler is a PreDoc Researcher at the Algorithms and Complexity Department of Technische Universität Wien. His research focuses on algorithmic visualization, graph drawing, and combinatorial optimization. He contributes to optimizing layout algorithms for diagrams, treemaps, and storylines, with a particular emphasis on minimizing crossings and corners in geometric representations. Key projects include 'Engineering Linear Ordering Algorithms' (2020–2025) and participation in the PACE competition with solver 'Touiouidth'. His work bridges theoretical computer science with practical visualization challenges, addressing both discrete mathematics and computational geometry. Education: BSc, Dipl.-Ing. (engineering degree) Labs/Teams: Part of the Algorithms and Complexity group at TU Wien Grants: Involved in HumAlgo (2018–2023) and REVEAL-AI (2020–2024) His recent publications (2023–2025) address topics like cluster vertex splitting complexity, hoop diagrams for set visualization, and optimizing linear diagrams. He has supervised Marcel Holzmüller’s 2025 diploma thesis on expanding planar storyplan problems.

• Computational Social Choice
• Algorithmic Game Theory
• Formal Methods
• +۶ مورد دیگر

Jie He is a PreDoc Researcher at the Department of Cyber-Physical Systems, Technische Universität Wien. His research spans computational social choice, algorithmic game theory, and formal methods in robotics and IoT systems. He works on multidisciplinary problems involving complexity analysis, fair division, and preference modeling. Current projects: EdgeAI (2022–2025), TAIGER (2023–2027), ADEX (2020–2024) Key collaborations: Research with R. Grosu, E. Bartocci, D. Nickovic Research interests focus on computational aspects of collective decision-making , including fair division, matching problems, and preference modeling. He works on both theoretical foundations (e.g., parameterized complexity) and practical applications (e.g., robotic-IoT systems). His publication history reveals deep expertise in computational complexity of social choice problems, with recent work on 3D stable roommates , fair division in graph-structured settings , and preference modeling through Euclidean and Manhattan geometries. As an advisor, he supervised diploma theses on: Optimization strategies for 5G transceivers Dynamic object detection in multi-agent systems His work appears in top conferences like ACM/IEEE DAC, ICSE, and various computational social choice venues.

• Machine Learning
• Graph Neural Networks
• Differential Privacy
• +۳ مورد دیگر

Patrick Indri is a PreDoc Researcher at the Department of Informatics, Technische Universität Wien , specializing in machine learning and its intersections with formal methods and data privacy. His work focuses on graph neural networks, robustness verification, and temporal logic applications. Education : MSc degree holder Research Interests : Expressive Graph Neural Network architectures for specialized graph types Differential Privacy integration in graph-based learning systems Robustness Certification with probabilistic guarantees Temporal logic applications in Cyber-Physical Systems anomaly detection Key Projects include the StruDL (2023–2027) initiative exploring structured deep learning. His publications demonstrate expertise in both foundational machine learning and applied formal verification techniques.

• Graph Data Management
• RDF and Semantic Web
• Database Theory
• +۳ مورد دیگر

Maxime Jakubowski is a PostDoc Researcher at TU Wien's Faculty of Informatics, affiliated with the Databases and Artificial Intelligence research group. Based in Room HA0320 at Favoritenstrasse 9, he can be contacted at maxime.jakubowski@tuwien.ac.at and maintains an ORCID profile (0000-0002-7420-1337). His research centers on graph data management within the Semantic Web ecosystem, specializing in RDF validation through shape constraint languages like SHACL and ShEx. He investigates formal foundations, expressiveness boundaries, and practical implementations including SQL compilation and neighborhood-based graph description. Current projects include FRESH (2021–2026), KtoAPP (2018–2025), and TARGET (2024–2028), focusing on graph data theory and implementation. Recent publications (2021–2025) demonstrate consistent contributions to RDF validation standards, with 14 articles addressing shape language formalization, compilation techniques, and provenance tracking. His work bridges theoretical database concepts with industrial applications in knowledge graph validation. Dr. Jakubowski supervises bachelor theses and teaches courses including Management of Graph Data (192.161) and Project in Computer Science. His research collaborations span international projects and the Dagstuhl Seminar 24102 on Shapes in Graph Data. He is an active member of the Databases and Artificial Intelligence research group, contributing to TU Wien's leadership in graph data management and Semantic Web technologies through both theoretical research and practical tool development.

• Satisfiability (SAT)
• Graph Theory
• Combinatorial Optimization
• +۳ مورد دیگر

Markus Kirchweger is a PreDoc Researcher at the Department of Algorithms and Complexity, Faculty of Informatics, Technische Universität Wien. His work spans Satisfiability (SAT) solving, graph theory, and combinatorial optimization, with a focus on symmetry breaking and SAT modulo theories. Research Interests: Developing SAT-based frameworks for graph generation and enumeration Dynamic symmetry breaking in combinatorial problem encodings Integrating user propagators into CDCL solvers Applying SAT techniques to conjectures like Erdős-Faber-Lovász and Rota’s Basis Co-certificate learning and shortest common supersequence optimization Projects: INCR (2021–2024), REVEAL-AI (2020–2024), SLIM (2019–2024), ASK-SAT (2024–2027).