Find Academic Supervisors

• Operations Research
• Game Theory
• Optimization Theory
• +5 more

Immanuel Bomze is a Full Professor of Applied Mathematics and Statistics at the University of Vienna, affiliated with the Institute of Statistics and Operations Research and the Data Science Research Center. His research focuses on Operations Research, Game Theory, Optimization, and their applications in areas like astronomy and logistics. He holds the EurOpt Fellow distinction (2014) and has received multiple best paper awards. Bomze has authored over 120 publications, including work on quadratic optimization, evolutionary game dynamics, and stochastic modeling. He serves as Editor-in-Chief of the EURO Journal of Computational Optimization and has held leadership roles in organizations like EURO. His teaching includes advanced optimization courses and PhD seminars. Notable contributions include methods for cluster detection in networks and robust optimization frameworks. Education 1982: Dr. rer. nat (PhD equivalent) in Mathematics, University of Vienna 1981: Mag. rer. nat (MSc equivalent) in Mathematics and Physics, University of Vienna Research Interests include: Optimization Theory, Game Theory applications, Stochastic Modelling, Dynamical Systems, and Data Science. His work bridges theoretical mathematics with real-world problems in logistics, astronomy, and decision theory. Key Achievements: Developed evolutionary game dynamics models for equilibrium analysis. Contributed to copositive programming and robust optimization methodologies. Co-founded the GAMENET European Network for Game Theory. Recipient of the Best Paper Award in Mathematical Methods of Operations Research (2020) and other journals. Grants & Collaborations: Extensive collaborations with institutions worldwide, including the International Institute for Applied Systems Analysis (IIASA). Research funded by grants from NSF, NWO, and others. Labs/Teams: Active in the Vienna Center for Operations Research (VCOR) and the Data Science Research Group at the University of Vienna.

Maath MuslehView profile

Researcher

• Visual Analytics
• Medical Visualization
• Uncertainty Visualization
• +3 more

Maath Musleh is a Researcher and PhD candidate at the Vienna University of Technology (TU Wien), affiliated with the Faculty of Informatics and the Department of Computer Graphics. His academic roles include serving as a University Assistant and teaching courses such as Methods for Data Generation and Analytics in Medicine and Information Visualization . He holds a BSc and MSc, with his Master’s thesis focusing on industrial multivariate time series analysis. Research interests center on Visual Analytics , Medical Visualization , and Uncertainty Visualization , with applications in healthcare, manufacturing, and agriculture. His work emphasizes explainable AI, user confidence measurement, and decision-support systems. Notable contributions include the TrustME model for explainable guidance and the ConAn framework for quantifying user confidence in uncertain analysis scenarios. Key achievements include the Best Short Paper Award at VINCI 2021 for industrial time-series visualization research. His ongoing PhD, supervised by Prof. Renata Raidou, explores Guided Visual Analytics for Decision-Making under Uncertainty . He collaborates on projects like Agritology , a multilingual decision-support system for farmers, and has developed dashboards for industrial and medical data analysis. Maath’s interdisciplinary approach integrates visualization, machine learning, and human-centered design to address challenges in complex decision-making environments.

• Natural Language Processing
• Computational Linguistics
• Information Extraction
• +3 more

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.

Renata RaidouView profile

Associate Professor

• Medical Visualization
• Visual Analytics
• Uncertainty Visualization
• +3 more

Renata Raidou is an Associate Professor in Biomedical Visualization and Visual Analytics at the Research Unit of Computer Graphics, Institute of Visual Computing & Human-Centered Technology, TU Wien, Austria. She previously held positions at the University of Groningen and TU Wien as a postdoc. Her research focuses on medical visualization, uncertainty visualization, and data physicalization for healthcare applications. Raidou has received prestigious awards including the Dirk Bartz Prize (2017) and EuroVis Young Researcher Award (2022). She coordinates TU Wien's BSc in Digital Health and MSc in Medical Informatics. Education: PhD in Medical Visualization (Eindhoven University of Technology, 2017), MSc in Biomedical Engineering (TU Delft), Diploma in Electrical and Computer Engineering (NTUA). Professional Roles: Editorial Board member of Computer & Graphics , Steering Committee member of EG VCBM, and member of EASAC's AI in Healthcare working group. Research Interests: Medical visualization strategies for P4 medicine, anatomical edutainment through physicalization, and uncertainty-aware visual analytics in radiotherapy. Her work bridges visual computing, machine learning, and medical applications, emphasizing clinical decision support and public health education. Awards: Over 10 awards including Best Paper recognitions at IEEE Vis and EuroVis, and the EuroVis Best PhD Award. Her contributions span visualizing anatomical variability, ensemble data exploration, and interactive medical training tools. Grants & Projects: Leads the Health Virtual Twins project (2024–2028) for personalized stroke management. Active in interdisciplinary collaborations, including the Shonan Meeting on Formalizing Biological Visualization. Labs/Teams: Head of the Visualization Group at TU Wien, developing tools like Vologram (holographic medical data physicalization) and Slice and Dice anatomical crafts. Collaborates on edutainment systems and AI-driven healthcare visualization.

Ilir MurturiView profile

Researcher

• Edge Computing
• Internet of Things (IoT)
• EdgeAI
• +3 more

Ilir Murturi is a PostDoc Researcher at the Distributed Systems Group, TU Wien, Vienna. He holds a PhD from TU Wien (2022) and B.Sc./M.Sc. degrees from the University of Prishtina, Kosova. His research focuses on Edge Computing, IoT, EdgeAI, and privacy in distributed systems. He has supervised multiple theses and contributed to projects like DECENT (a decentralized elasticity controller) and edge planning frameworks using AI. His work spans academic activities including reviewing for IEEE Transactions on Services Computing and ACM journals. Research interests include resource management in edge networks, federated learning, and zero-trust architectures. Recent publications explore elasticity frameworks, federated domain generalization, and distributed computing challenges. He has also co-edited a special issue on learning-driven edge computing for IoT networks.

• Applied Mathematics
• Graph Theory
• Material Science
• +3 more

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
• +5 more

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.

Renata Georgia RaidouView profile

Associate Professor

• Medical Visualization
• Visual Analytics
• Uncertainty Visualization
• +4 more

Renata Georgia Raidou is an Associate Professor in Biomedical Visualization and Visual Analytics at TU Wien's Institute of Visual Computing & Human-Centered Technology. She leads the Research Unit of Computer Graphics and serves as Curriculum Coordinator for the Bachelor in Informatics (Specialization Digital Health) and Master in Medical Informatics programs. She holds prestigious awards including the EuroVis Young Researcher Award (2022), Best PhD Award (2018), and Dirk Bartz Prize (2017). Her research focuses on medical applications of Visual Analytics, with emphasis on uncertainty visualization, comparative visualization, and anatomical edutainment through physicalizations. She explores how visual tools can enhance decision-making in precision medicine, particularly in radiotherapy and cancer treatment. Her work bridges visualization, machine learning, and image processing to address clinical challenges. Raidou's recent projects include developing predictive visual analytics systems for radiotherapy planning and creating tactile physicalizations for anatomy education. She actively contributes to the field through editorial roles (Associate Editor of Computer & Graphics ) and policy advising via EASAC's 'AI in Healthcare' initiatives. Key Projects: Health Virtual Twins for Stroke Management, PREVIS for Radiotherapy Decision Support, Pelvis Runner for Anatomical Variability Analysis Teaching: Courses on Medical Visualization, Data Analytics in Health Sciences, and Visual Computing Grants: European Commission-funded projects (2024–2028) Her lab develops interdisciplinary tools for clinicians, researchers, and educators, emphasizing both technical innovation and real-world impact.

• Formal Methods
• Program Analysis
• Verification
• +9 more

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).

• Computer Science
• Data Visualization
• Information Systems
• +3 more

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
• +3 more

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
• +3 more

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
• +6 more

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.

• Combinatorial Optimization
• Machine Learning
• Operations Research
• +3 more

Marc Huber is a PreDoc Researcher (academic rank: Researcher) at the Institute of Logic and Computation within the Faculty of Informatics at TU Wien. Currently on leave, he conducts research at the intersection of combinatorial optimization and machine learning, focusing on algorithmic innovations for complex scheduling and sequence problems. His core research interests include: Combinatorial Optimization Machine Learning for Optimization Beam Search and Metaheuristics Staff and Flow Shop Scheduling Sequence Problems (Longest/Shortest Common Subsequence) Algorithm-ML Integration Huber's publication trajectory (2021-2024) reveals a consistent methodological evolution: progressively integrating reinforcement learning and value function approximation into beam search frameworks. This approach has yielded significant advances in staff rostering, multi-agent path finding, and sequence alignment, demonstrating how learned heuristics outperform traditional optimization techniques in solution quality and computational efficiency. Scientific Awards: No awards, fellowships, or medals were documented in available sources. As a thesis supervisor, Huber has guided four master's students at TU Wien through research on graph neural networks for clique problems, reinforcement learning for flow shop scheduling, e-mobility fleet scheduling, and staff resortering algorithms. His advising emphasizes practical implementation of learning-based optimization techniques. He operates within the Algorithms and Complexity research group (E192-01), collaborating closely with Prof. Raidl on algorithm engineering projects funded through TU Wien's research infrastructure. Current work focuses on extending learning beam search to multi-objective combinatorial problems.

• Satisfiability (SAT)
• Graph Theory
• Combinatorial Optimization
• +3 more

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).