Find Academic Supervisors

• Blockchain
• Smart Contracts
• Ethereum
• +7 more

Monika di Angelo is an Assistant Professor at Vienna University of Technology's Faculty of Informatics, Department of Automation and Control Systems. Working at 50% capacity, she focuses on Information Systems Engineering and Visual Computing and Human-Centered Technology. Her research spans blockchain technology, smart contracts, Ethereum security, vulnerability detection, cultural heritage applications, and educational informatics. Dr. di Angelo's research interests center around blockchain technology and smart contract security, with particular expertise in Ethereum. Her work explores vulnerability detection in smart contracts, compiler analysis, token standards, and wallet implementations. She also maintains research interests in cultural heritage applications through digital visualization and simulation, extending from earlier work on architectural heritage and pedestrian behavior modeling. Her educational research focuses on didactics in computer science education, particularly for secondary schools and vocational training. Her publication record shows a clear evolution from early work in educational technology and architectural visualization toward blockchain and smart contract research starting around 2018. Recent publications demonstrate deep expertise in Ethereum security analysis, vulnerability detection frameworks, and comparative analysis of smart contract platforms. Her work combines theoretical analysis with practical tool development for blockchain security. Mobilitätsstipendium der TU Wien für hervorragende Dissertation Lions-Förderpreis (1989) Dr. di Angelo has supervised over 50 diploma and master's theses since 2008, with recent supervision focusing primarily on blockchain security, smart contract analysis, and Ethereum vulnerability detection. Her students' work reflects her research trajectory, moving from educational technology topics toward blockchain security in recent years. She has secured research funding from the Austrian Research Promotion Agency (FFG) for projects evaluating educational concepts for MINT promotion. Her DIVS project (2010-2020) represented a long-term research commitment to visualization and simulation of pedestrian behavior at train stations and architectural heritage applications. Her research extends across multiple domains including the SmartBugs framework for smart contract analysis, Ethereum security research, and applications of blockchain technology in various domains. She maintains active collaborations with researchers in blockchain security and has contributed to the development of tools and methodologies for smart contract analysis.

Chao ZhangView profile

Associate Professor

• Dielectrics
• Electrochemistry
• Electrolyte materials
• +3 more

Chao Zhang is an Associate Professor at the Department of Chemistry-Ångström Laboratory, Uppsala University, specializing in computational electrochemistry and multi-scale modeling of electrolyte materials. His research bridges atomistic simulations with machine learning approaches to address challenges in energy storage and conversion systems. Education: Dr. rer. nat. from RWTH Aachen University (2013); Docent from Uppsala University (2020) Appointments: Postdoctoral researcher at the University of Cambridge (prior to joining Uppsala in 2017) His group develops finite-field methods for computational electrochemistry and investigates electrified solid-liquid interfaces. Recent research trends include neural rendering for underwater SLAM systems (2025), robust path-following control in marine robotics, and event-based localization in LiDAR-integrated environments. Scientific Awards: ERC Starting Grant (2020) Junior Research Fellowship, Wolfson College (2015) Jülich Excellence Prize for Young Scientists (2013)

• Conceptual Modeling
• Knowledge Graphs
• Model-Driven Engineering
• +5 more

Syed Juned Ali is a Researcher (Univ.Ass.) at TU Wien's Business Informatics Group. His work focuses on conceptual modeling, knowledge graphs, and model-driven engineering. He holds BSc and MSc degrees and contributes to academic publications in top venues like Science of Computer Programming. Key projects include CM2KGcloud (a knowledge graph transformation platform) and GGMF (a modularization framework). He teaches Model Engineering (VU 188.923) and collaborates with Prof. Dominik Bork on AI-driven decision systems. Professional activities include conference paper reviews and contributions to enterprise architecture research. Research interests span AI integration in decision models, automated model modularization, and cross-language modeling tools. His work bridges theory and practice through tools like EAKG Toolkit for enterprise architecture analysis and genetic algorithm-based frameworks for scalable model decomposition.

• Medical Imaging
• Haptic Systems
• Computer Graphics
• +6 more

Prof. Matthias Harders is a Professor at the Department of Computer Science, University of Innsbruck. His work focuses on medical imaging, haptic systems, virtual reality, and data-driven simulation. He leads research in interactive visualization tools, medical device development, and machine learning applications in healthcare and environmental engineering. Research areas include haptic augmented reality for surgical training, deformable medical image registration, and synthetic data generation for retinal imaging. Notable projects include SPBView for eye movement analysis and the PoRi device for post-stroke rehabilitation. His work bridges computer science with biomedical applications, emphasizing real-world impact in healthcare technology. Publications span medical simulation, machine learning for biogas prediction, and perceptual interfaces. He collaborates on EU-funded projects involving VR/AR systems and has contributed to open-source tools for point cloud analysis and surgical planning.

Yingcai WuView profile

Professor

• Visual Analytics
• Urban Informatics
• Sports Analytics
• +3 more

Yingcai Wu is a Professor at the State Key Lab of CAD & CG within the College of Computer Science and Technology at Zhejiang University, Hangzhou, where he serves as Head of the Interactive Data Group (IDG@ZJU). His research focuses on: Visual Analytics applications in Urban Informatics, Sports Analytics, and Social Media Core methodologies in Information Visualization and Human-Computer Interaction (HCI) Current projects include SmartAdp (visual location selection), RouteZoomer (route-zooming), PieceStack (stacked graphs), PeakVizor (video clickstreams), EvoRiver (topic coopetition), and OpinionFlow (opinion diffusion). Professor Wu actively recruits PostDoc, PhD, Master, and undergraduate students for visualization research and served as Area Paper Chair for IEEE VIS 2023.

Peter FilzmoserView profile

Professor

• Compositional Data Analysis
• Robust Statistics
• Outlier Detection
• +5 more

Peter Filzmoser is a Professor at the Institute for Statistics and Mathematical Economics (E105) of Vienna University of Technology, leading the Computational Statistics Research Area (E105-06) and affiliated with the Network Lab. His research focuses on: Compositional Data Analysis Robust Statistics Outlier Detection Machine Learning for High-Dimensional Data with applications in geochemistry, mobility data, tribology, and sustainable development. Recent publications (2023) demonstrate significant advances in explainable outlier detection using Shapley values, robust techniques for compositional data analysis, and applications in forecasting heterogeneous time series. His work extends compositional data analysis through graph signal processing and develops novel robust methodologies for real-world problems. Professor Filzmoser has advised over 15 Master's and PhD students from 2021-2023. Key research projects he leads include: Automotive Intelligence for/at Connected Shared Mobility CSTAT: Blind Source Separation Generalized relative data and Robustness in Bayes spaces

• Combinatorial and Computational Geometry
• Graph Drawing
• Algorithms for Robotics

Irene Parada is an Assistant Professor and Serra Húnter Fellow at Universitat Politècnica de Catalunya (UPC BarcelonaTech), Spain. She is part of the Research Group on Discrete, Combinatorial and Computational Geometry. Previously, she held postdoctoral positions at Utrecht University, Technical University of Denmark, and TU Eindhoven. Her research focuses on Combinatorial and Computational Geometry, Graph Drawing, and Algorithms for Robotics. She has been recognized with the Margarita Salas postdoctoral fellowship and received Excellent Course Evaluations for her teaching roles. Education: PhD in Computer Science (2019) from Graz University of Technology, Austria (supervised by Oswin Aichholzer) Master's in Advanced Mathematics and Mathematical Engineering (2015) from UPC BarcelonaTech Bachelor's in Mathematical Engineering (2014) from Universidad Complutense de Madrid Research Interests: Irene's work spans geometric algorithms, graph visualization, and modular robotics. She explores computational challenges in geometric configurations, such as perfect matchings, graph embeddings, and reconfiguration problems. Her contributions include novel algorithms for dynamic planar graphs and efficient robot motion strategies. Key Achievements: Recipient of the Margarita Salas postdoctoral fellowship Best paper awards for 'On compatible matchings' and 'Saturated k-plane drawings with few edges' Excellent teaching evaluations for courses like Motion and Manipulation (Utrecht) and Data Structures (TU Eindhoven) Teaching & Mentorship: Irene has coordinated courses in Numerical Computing and Numerical Methods at UPC. She has also lectured on Motion and Manipulation, Algorithms, and Topological Data Analysis. Her roles included teaching assistantships in Discrete Mathematics and Linear Algebra at UPC. Labs & Collaborations: She collaborates with the Geometric Computing Group (Utrecht) and the Algorithms cluster (TU Eindhoven). Her work often involves interdisciplinary teams, addressing challenges in robotics, graph theory, and computational geometry.

Eva MayrView profile

Researcher

• Digital Humanities
• Information Visualization
• User-Centered Design
• +3 more

Dr. Eva Mayr is a Senior Researcher at the University for Continuing Education Krems, affiliated with the Center for Cultures and Technologies of Collecting . Her work focuses on user-centered design, casual information visualization, digital humanities , and cognitive and media psychology . She leads major projects such as the Bibliotheca Eugeniana Digital (2022–2024) funded by the Austrian Academy of Sciences, and previously directed initiatives like In/Tangible European Heritage (2020–2023, EU-funded) and Uncertainty visualization of cultural heritage data (2017–2020, state-funded). Her research emphasizes innovative visualization techniques for cultural heritage, including methods to handle uncertainty in data and enhance accessibility for casual users. She teaches Usability and Research Methods , and actively contributes to organizations like the OCG IIID . Key contributions include frameworks for integrating cultural data (e.g., InTaVia ) and advancing narrative visualization approaches for historical and biographical data. Publications span journals like Frontiers in Big Data and IEEE Computer Graphics and Applications , with a focus on cultural heritage digitization, set visualization, and mental models in design. Projects often bridge academia and applied contexts, addressing challenges in accessibility, collaboration, and data quality within cultural institutions.

• Cyber-Physical Production Systems Engineering
• Software Product Line Engineering
• Variability Modeling
• +6 more

Kristof Meixner is a Postdoctoral Researcher at the Institute of Information Systems Engineering (E194) within the Faculty of Informatics at Technische Universität Wien. He completed his PhD in Computer Science in 2024 and holds a Master's degree in Business Informatics from the same institution. His academic journey includes significant teaching responsibilities and substantial research contributions in cyber-physical production systems engineering. Position: Postdoctoral Researcher, Institute of Information Systems Engineering (Dec 2024 - present) Previous Position: Researcher at Christian Doppler Laboratory for Security and Quality Improvement in Production Systems (Mar 2018 – Dec 2024) Teaching: Lecturer for Advanced Software Engineering, Software Engineering Project, and related courses since 2018 Service: Research Unit Representative in the Scientific Staff Council since 2017 Meixner's research focuses on model-based approaches for software product line engineering in cyber-physical production systems. His work addresses the integration of interdependent models for products, processes, and resources, with particular emphasis on variability management, change impact analysis, and digitalization of production systems. His research combines empirical methods with practical tool development to improve software engineering practices in industrial contexts. His publication portfolio shows a strong trend toward multi-disciplinary approaches that integrate software engineering with production systems engineering. The 2024 publications particularly emphasize risk management, multi-domain modeling, and the application of AI techniques to variability management problems. His work frequently appears in top-tier software engineering and production systems conferences including ETFA, SPLC, and CBI. 80+ peer-reviewed publications 754 citations h-index: 16 As an educator, Meixner has supervised numerous bachelor's and master's theses, demonstrating his commitment to mentoring the next generation of software engineers. His teaching spans both theoretical foundations and practical applications of software engineering principles, with a focus on production systems contexts. He has also contributed to diversity initiatives at TU Wien, serving as a representative on the Diversity Team for the Diversitas 2016 Award. Despite experiencing a spinal cord injury in 2000 that resulted in wheelchair dependency, Meixner has maintained an active research and teaching career, demonstrating both personal resilience and professional dedication to advancing software engineering practices.

Fabian KluteView profile

Research Fellow

• Graph drawing
• Automated cartography
• Map labeling
• +3 more

Fabian Klute is a Research Fellow at Universitat Politècnica de Catalunya, specializing in discrete and computational geometry. His research focuses on graph drawing, automated cartography, map labeling, and geometric computing, with emphasis on theoretical foundations and algorithm development. Klute's publications demonstrate consistent focus on geometric complexity, graph visualization, and combinatorial optimization. Recent works establish hardness results for segment folding and edge insertion problems, develop algorithms for geometric set diversity, and advance boundary labeling techniques. A significant research thread explores parameterized complexity in graph modification problems. His contributions in graph drawing include innovations in confluent drawings, book embeddings, and 1-planar extensions. Cartography-related research advances automated labeling and spatial representation for complex curve arrangements.

Velitchko FilipovView profile

Researcher

• Information Visualization
• Visual Analytics
• Dynamic Network Analysis
• +6 more

Velitchko Filipov is a PostDoc Researcher at the Institute of Visual Computing & Human-Centered Technology within the Faculty of Informatics at Vienna University of Technology (TU Wien). His work focuses on advancing Information Visualization and Visual Analytics, with a strong specialization in Dynamic Network Analysis. He is currently involved in multiple research projects including ArtVis (2022-2027), SANE (2024-2027), VaCoViCu2 (2023-2027), and SMGV-Esprit (2024-2027), with ArtVis funded by the Austrian Science Fund (FWF). Dr. Filipov completed his doctoral dissertation titled "Networks in time and space: visual analytics of dynamic network representations" at TU Wien in 2024. Prior to that, he completed his Diploma Thesis "Visual exploration and comparison of multiple resume: focus on time and space" at the same institution in 2017. His research centers on creating innovative visual representations and interactive techniques for exploring complex, time-varying networks. These approaches have significant applications in Art History, where understanding the evolution of artistic movements is crucial, and Information Diffusion. His work also benefits computational social sciences and digital humanities by providing tools to uncover patterns within evolving relational data. He has made notable contributions to uncertainty visualization, network physicalization, and the visualization of historical networks. Dr. Filipov's publication record shows a clear progression from foundational work on CV visualization and biographical trajectory mapping toward increasingly sophisticated techniques for dynamic network analysis. His recent work demonstrates a strong focus on making network data tangible through physicalization techniques and on addressing uncertainty in network representations, reflecting the evolving challenges in the visualization field. His scientific achievements have been recognized with several prestigious awards: Best Paper Award at EuroVis 2025 for "NODKANT: Exploring Constructive Network Physicalization" Top Cited Article 2025 in Computer Graphics Forum for "Are We There Yet? A Roadmap of Network Visualization from Surveys to Task Taxonomies" Nominee for Best Dissertation Award 2024 at TU Wien Informatics Best Paper at VIS4DH 2019 Graph Drawing Contest winner in 2019 and 2018 Dr. Filipov actively supervises students, with recent theses including "Dynamic network analysis with centrality measures" (2024) and "Utilizing visual analytics for network exploration in the domain of art history research" (2023). His research is supported by multiple grants, most notably the FWF project ArtVis. He collaborates extensively with researchers across Europe, particularly with Silvia Miksch, Wolfgang Aigner, and Renata Raidou. He is a key member of the Visual Analytics research group at TU Wien, participating in interdisciplinary collaborations that bridge computer science with art history and digital humanities. His work on the ArtVis project exemplifies this approach, developing visualization techniques specifically tailored to address the research challenges faced by art historians studying artistic networks and influence.

• Cognitive Psychology
• Perception Psychology
• Experimental Psychology
• +4 more

Ao.Univ.-Prof. Christof Körner is an Associate Professor at the Institute of Psychology, part of the College of Natural Sciences at Karl-Franzens-University Graz. His research focuses on visual information processing, eye movement analysis, and cognitive mechanisms underlying attention, memory, and perception in human interaction with visual environments. Research Areas: Cognitive Psychology, Visual Search, Eye Tracking, Perception, Attention, Memory Processes Projects: FWF-funded research on "Attention and Memory in Visual Search" (2015-2020), "Memory Processes in Repeated Visual Search" (2007-2010), and "Graph Understanding: Modeling and Underlying Processes" (2004-2006) His recent publications explore gaze behavior in complex graph comprehension, neural correlates of visual search through fixation-related potentials, and memory-driven effects in sequential visual tasks. Scientific contributions include editorial roles at the Journal of Eye Movement Research (2016-2018) and advisory board membership in the Handbook of Human Centric Visualization (2013). Awards: Marie-Curie-Stipendium der Europäischen Kommission (2001), University of Bristol Contact: christof.koerner@uni-graz.at

Markus SchedlView profile

Professor

• Recommender Systems
• Information Retrieval
• Algorithmic Fairness
• +6 more

Markus Schedl is a Full Professor at Johannes Kepler University Linz , Austria, where he leads the Multimedia Mining and Search (MMS) group within the Institute of Computational Perception . He also heads the Human-centered Artificial Intelligence (HCAI) group at the Linz Institute of Technology (LIT) AI Lab . Education: Computer Science (TU Wien), PhD (JKU Linz), Master's in International Business Administration (WU Wien, University of Gothenburg) Research Interests span recommender systems , information retrieval , algorithmic fairness , user modeling , and machine learning . His recent publications focus on: Multimodal and hybrid AI for human-centric personalization Context-aware music recommendation and emotion recognition Graph neural networks for session-based recommendation Technical and regulatory compliance in fair, transparent, and privacy-preserving systems Projects are funded by the Austrian Science Fund (FWF) , Austrian Research Promotion Agency (FFG) , and European Commission (EC) . He maintains industry collaborations with Siemens, Spotify, and Deezer. Teaching includes courses like Introduction to Machine Learning and Social Media Mining and Analysis at JKU, with guest lecturing experiences at Pompeu Fabra, Queen Mary London, and KTH Stockholm. Labs & Teams: MMS group at JKU's Institute of Computational Perception and HCAI group at LIT AI Lab.

Erich KistlerView profile

Professor

• Mediterranean Archaeology
• Classical Archaeology
• Ancient Greek Society
• +6 more

Erich Kistler is a full professor of Classical Archaeology at the Institute of Archaeology , Leopold Franzens University Innsbruck , since 2010. His career spans roles at the University of Zurich, University of Bern, Ruhr University Bochum, and declined an offer from Heidelberg University in 2012. He directs the Innsbruck Monte Iato Project in Sicily and has led interdisciplinary initiatives like the doctoral program 'Figuration Counterculture.' Current: Professor at University of Innsbruck Past: Professor at Ruhr University Bochum (2008-2010), Co-Director of Ietas excavation (2006-2008) His research focuses on the Iron Age Mediterranean , emphasizing elite 'orientalization,' ritual consumption, and local-global dynamics. He explores visual communication in archaic/classical vases and how barbarian representations convey cultural norms. Recent publications analyze the indigenous settlement of Monte Iato via ethnoarchaeometric methods, pottery production trends, and Persian cultural influences. His projects, funded by FWF and DFG, include 'Zwischen Aphrodite-Tempel und spätarchaischem Haus' and 'Töpfers Reaktionen auf lokale Bedürfnisse.' 2024: De-Monumentalisation of Chiefly Houses 2023: Neutron Activation Analysis applications 2021: Ritual abandonment and politics of indigeneity Scientific awards include the FWF Wissenschaftskommunikationspreis (2021-2022) and Forschungspreis Südtiroler Sparkasse (2016). He co-edits ATRIUM and contributes to international journals and conferences.

Nils Morten KriegeView profile

Associate Professor

• Data Mining
• Machine Learning with Graphs
• Combinatorial Optimization
• +5 more

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.