Find Academic Supervisors

• Cultural Heritage
• Document Analysis
• Medical Data Analysis
• +5 more

Florian Kleber is a senior scientist at the Computer Vision Lab within the Institute of Visual Computing and Human-Centered Technology at Vienna University of Technology (TU Wien), Austria. His research spans two primary domains: document analysis for cultural heritage preservation and medical data analysis. He has been actively involved in lecturing at TU Wien, particularly in Document Analysis courses, and has served as a substitute member of the Curriculum Commission for Informatics. Dr. Kleber's research focuses on the intersection of computer vision and practical applications. His work in cultural heritage includes multi-spectral acquisition and restoration of ancient manuscripts, development of tools for historical document analysis, and work on writer identification and retrieval systems. In medical data analysis, he has specialized in flow cytometry analysis, particularly for minimal residual disease assessment in acute lymphoblastic leukemia. His research demonstrates a consistent pattern of applying advanced computer vision techniques to solve domain-specific challenges in both humanities and medical fields. His recent publications reveal a strong trend toward synthetic data generation for training document analysis systems, self-supervised learning approaches for writer retrieval, and explainable AI techniques for medical data analysis. The publications show increasing sophistication in transformer-based architectures applied to both document analysis and medical imaging domains. His work bridges theoretical computer vision advances with practical applications in cultural heritage institutions and medical settings. Dr. Kleber has supervised multiple diploma theses at TU Wien, including work on automated analysis of herbarium collections, transparency techniques for neural networks, synthetic data for document analysis, and physical layout analysis of newspaper images. He has been involved in numerous research projects including the Vienna City Library poster collection analysis, digitization of museum holdings in Lower Austria, and the AutoFlow project for medical data analysis. His laboratory work centers around the Computer Vision Lab (E193-01) at TU Wien, where he collaborates on projects involving document image analysis, cultural heritage preservation, and medical data processing. His team has developed tools for manuscript analysis, document reconstruction, and flow cytometry data interpretation, often in collaboration with cultural institutions and medical facilities.

• Data Management
• Reproducibility in Science
• Semantic Web Technologies
• +3 more

Dr. Tomasz Miksa is a Senior Scientist at TU Wien's Department of Data Science, specializing in machine-actionable data management plans (maDMPs), reproducibility frameworks, and data stewardship. His work bridges semantic web technologies, automated workflows, and interdisciplinary data governance. He leads projects like OS Trails (2024–2027) and FAIR-AI , focusing on synchronizing research data services and ensuring FAIR (Findable, Accessible, Interoperable, Reusable) principles. Miksa holds a PhD in Computer Science from TU Wien (2016) and has supervised numerous theses on topics like reproducible query processing and DMP integration. He actively contributes to initiatives such as FAIR Data Austria and openEO API standardization, emphasizing sustainable data practices across disciplines. Research interests include semantic representation of DMPs, automated validation tools, and frameworks for auditable privacy-preserving data analysis. His work on data citation mechanisms and evolving database schemas ensures long-term reproducibility in fields like environmental science and earth observation. Miksa collaborates internationally, co-editing special collections on data management planning and publishing in journals like ACM Transactions on Management Information Systems and IEEE eScience . Key contributions include the DCSO ontology for DMPs, the WellFort platform for privacy-aware data analysis, and frameworks for identifying software dependencies in biomedical workflows. His teaching focuses on data stewardship, with courses like Data Stewardship VO/UE addressing national and institutional strategies. Ongoing efforts aim to integrate machine-actionable DMPs with institutional repositories and cloud-based research infrastructures.

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 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.

• 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.

• Machine Learning
• Graph Neural Networks
• Differential Privacy
• +3 more

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.

• 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).

Thomas BäckView profile

Professor

• Artificial Intelligence
• Evolutionary Computation
• Machine Learning
• +6 more

Thomas Bäck is a Full Professor at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, and Chief Scientist at NORCE Norwegian Research Center, Norway. His career spans roles as Director of the Center for Applied Systems Analysis (Germany) and Vice Scientific Director at LIACS (since 2017). He holds additional positions like Adjunct Professor at the University of Calgary and Visiting Professor at Xi'an Jiaotong University. Bäck's research focuses on evolutionary computation, machine learning, and their applications in sustainable smart industry and health. He leads interdisciplinary initiatives like the Society, Artificial Intelligence, and Life Sciences (SAILS) program, with expertise in natural computing, data-driven optimization, and quantum computing challenges. IEEE Fellow (2022) Member of KNAW (2021) IEEE CIS Pioneer Award (2015) Fellow of International Society of Genetic and Evolutionary Computation (2003) Best PhD Thesis Award from German Society of Computer Science (1995) Bäck has graduated 22 PhD students and currently supervises 14, with 62 MSc students mentored. His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and Associate Editor for multiple journals. He has secured grants from the Dutch Research Council and The Research Council of Norway for projects like ECOLE and CIMPLO.

• Decision support systems
• Computational intelligence
• Recommender systems
• +4 more

Distinguished Professor Jie Lu is Associate Dean (Research Excellence) in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), where she also serves as Director of the Australian Artificial Intelligence Institute (AAII). With over two decades of academic service at UTS, she has held progressively senior roles including Professor since 2007, Head of School of Software (2011-2014), and Director of the Centre for Artificial Intelligence (2017-2020) before assuming her current leadership positions. Professor Lu earned her PhD from Curtin University in 2000 and joined UTS the same year. Her academic journey progressed from Lecturer (2000-2001) to Senior Lecturer (2002-2004), Associate Professor (2005-2007), and ultimately Professor (2017-present). Professor Lu's research spans computational intelligence with particular expertise in decision support systems, fuzzy transfer learning, concept drift, and recommender systems. Her work bridges theoretical innovation with practical applications across transportation, telecommunications, healthcare, and education sectors. She has pioneered approaches in data-driven decision making that deliver tangible economic benefits and risk management solutions for industry partners. Her recent publications demonstrate continued leadership in AI research, with a focus on addressing challenges in non-stationary environments, out-of-distribution detection, cross-domain recommendation, and healthcare applications. Her work increasingly integrates large language models with specialized domain knowledge for enhanced decision support. Officer of the Order of Australia (2022) 2023 NSW Premier's Prize for Excellence in Engineering or Information & Communication Technology Australian Laureate Fellow (2019) IEEE Fellow for contributions to fuzzy machine learning and decision making (2018) Fellow of International Fuzzy Systems Association (2017) Australia's Most Innovative Engineer Award (2019) Multiple IEEE Transactions Outstanding Paper awards Professor Lu has supervised over 50 PhD students to completion, with half pursuing academic careers and half working in industry. She has secured over A$10 million in research funding as lead Chief Investigator, including 10 highly competitive ARC Discovery Projects. Her industry collaborations include significant projects with Optus, Sydney Trains, Domain Holdings, and healthcare organizations. As Director of the Australian AI Institute, Professor Lu leads Australia's largest AI research hub with over 250 researchers and PhD students. Under her leadership, AAII has secured 47 ARC grants and over 110 industry projects since 2017. She also serves as Editor-in-Chief of Knowledge-Based Systems, a leading journal in the field.

Shen Heng TaoView profile

Academic

• Artificial Intelligence
• Multimedia Computing
• Computer Vision
• +3 more

Shen Heng Tao is a Distinguished Professor and Dean of the School of Computer Science and Engineering at the University of Electronic Science and Technology of China (UESTC). He is also the Executive Dean of the AI Research Institute and Chief Scientist of Vision Intelligence at the Peng Cheng Laboratory. His academic journey includes a BSc (First Class Honours) and PhD from the National University of Singapore (2000 and 2004), followed by roles at the University of Queensland, where he became a Professor in 2011. His research focuses on artificial intelligence, multimedia computing, and computer vision, with notable contributions to cross-media intelligent analysis and real-time video retrieval systems. He has published over 400 peer-reviewed papers, including 160+ IEEE/ACM Transactions, and holds an H-index of 78. His work has been recognized with awards such as the IEEE Fellow, ACM Fellow, OSA Fellow, and Clarivate Highly Cited Researcher. He has led 15 major research grants, including projects from the Ministry of Science and Technology of China and National Natural Science Foundation. His service includes roles as Associate Editor for journals like ACM Transactions of Data Science and IEEE Transactions on Multimedia, and conference chairs like ACM Multimedia 2021. His lab, the Center for Future Media, explores advanced topics like multimodal learning, trustworthy AI, and human-robot interaction. Current research emphasizes cross-modal reasoning and real-world applications in metaverse, autonomous systems, and smart cities.

• Randomized Algorithms
• Networking
• Social Networks
• +7 more

Aravind Srinivasan is a Professor of Computer Science at the University of Maryland, College Park, USA. He holds tenure and has held academic positions at the National University of Singapore and Bell Labs. He completed his B.Tech at IIT Madras and his Ph.D. at Cornell University, with postdoctoral research at the Institute for Advanced Study and DIMACS. His research focuses on randomized algorithms, networking, social networks, combinatorial optimization, and their applications to public health, machine learning, and energy systems. He has published over 115 papers in top journals like Nature and the Journal of the ACM, and his work has received significant recognition, including 1,551 citations for his 2004 Nature paper on disease modeling. Education: B.Tech, Indian Institute of Technology Madras Ph.D., Cornell University Awarded Fellowships from ACM, AAAS, IEEE, and EATCS, he also serves as Editor-in-Chief of the ACM Transactions on Algorithms. His students have secured roles in academia, industry, and government, reflecting his impactful mentorship. His contributions span theoretical computer science and applied domains, with notable work in probabilistic methods, network science, and interdisciplinary applications.

Wojciech SzpankowskiView profile

Professor

• Analysis of Algorithms
• Information Theory
• Analytic Combinatorics
• +6 more

Wojciech Szpankowski is the Saul Rosen Distinguished Professor of Computer Science at Purdue University. He holds concurrent positions as a Guest Professor at ETH Zurich and a Professor at Jagiellonian University, Krakow. His research focuses on analysis of algorithms, information theory, analytic combinatorics, and random structures. He has directed major initiatives like the NSF Science & Technology Center on Science of Information (CSoI), a $50M 10-year project. Szpankowski is a Fellow of the IEEE and has received prestigious awards including the Flajolet Prize (2020) and Humboldt Research Award (2010). He has held visiting roles at institutions worldwide, including Stanford University and the Newton Institute, Cambridge. His academic journey includes tenure as Full Professor at Purdue (since 1992), Assistant Professorships at McGill University (1984–1992) and Technical University of Gdańsk (1980–1984). His scholarly contributions span monographs like "Average Case Analysis of Algorithms on Sequences" (2001) and "Analytic Pattern Matching" (2015), with a forthcoming work on Analytic Information Theory (2022). He actively contributes to scientific boards, including HIIT Helsinki and NeuroMat Brazil. Szpankowski’s publications emphasize interdisciplinary research at the intersection of computer science, mathematics, and biology. His work on privacy-preserving data analysis, dynamic network inference, and protein superfamily evolution showcases his broad impact. Keynote talks at major conferences (e.g., SODA 2019, AofA 2016) reflect his leadership in theoretical computer science. His grants and recognitions underscore institutional trust: the NSF Science & Technology Center (2010) and Arden L. Bement Jr. Award (2015) highlight his pioneering role in redefining information science. Collaborations span academia and industry, with affiliations at Hewlett-Packard and INRIA.

Pietro Lio'View profile

Professor

• Computational Biology
• Bioinformatics
• Machine Learning
• +2 more

Pietro Lio' is a Professor of Computational Biology at the University of Cambridge's Department of Computer Science and Technology, where he has held academic positions since 2003. He progressed from Lecturer to Senior Lecturer, Reader, and ultimately Professor (2018-present). His career includes prior roles as Associate Professor of Genetics at the University of Teramo and research positions at the European Bioinformatics Institute, University of Cambridge (Genetics/Zoology), and University of Southampton. Education: 1989: BSc in Electronic Engineering and Biology, University of Firenze 1995: PhD in Theoretical Genetics, University of Pavia 2007: PhD in Engineering (Non-Linear Dynamics & Complex Systems), University of Firenze Research Focus: Professor Lio' specializes in interdisciplinary approaches bridging computer science and biology. His primary research domains include computational biology, bioinformatics algorithms, machine learning (particularly graph neural networks), deep learning architectures, and complex network analysis. His work spans molecular to ecological scales, with applications in genomics, systems biology, and environmental modeling. Publication Trends: Recent work (2018-2020) demonstrates strong emphasis on graph neural networks (Graph Attention Networks, Deep Graph Infomax) and blockchain applications. Earlier publications (2015-2017) focus on ecological modeling, multi-omics integration, molecular communications, and single-cell genomics, reflecting consistent cross-disciplinary innovation. Awards and Honors: 2018: BITS Award (Bioinformatics Italian Society) 2017: Visiting Professorship at University of Padova 2016: BYRA First Prize (ISEM) & MCE Award Second Prize 2013-2015: Lagrange Fellowship 2011: European Commission Award for methodological innovation No information is available regarding student advising, grants, labs, or research teams in the provided materials.

• Conceptual Modeling
• Model-Driven Engineering
• Enterprise Architecture
• +3 more

**Dominik Bork** is an Assistant Professor at the Department of Business Informatics within the Faculty of Informatics at TU Wien. His core research focuses on conceptual modeling, model-driven engineering, and the integration of artificial intelligence into enterprise systems. He leads projects such as the Network Lab and has coordinated initiatives like the Automatisiertes End-to-End-Testen von Cloud-basierten Modellierungswerkzeugen and MFP 4.2 Advanced Analytics for Smart Manufacturing . His work emphasizes GLSP-based web modeling tools , including the development of open-source platforms like BIGUML for UML modeling. He has published extensively on topics like knowledge graph transformation, accessibility in modeling tools, and AI-enhanced decision management. Bork actively contributes to academic communities through conference organizing roles and guest editorial work for journals like Enterprise, Business-Process and Information Systems Modeling . His research bridges theoretical advancements with practical applications in enterprise architecture and sustainable systems engineering. Education & Background : Holds titles including Dipl.-Wirtsch.Inf.Univ. and Dr.rer.pol., reflecting his interdisciplinary expertise in business informatics and economics. Key Contributions : - Developed CM2KGcloud , a web-based platform for conceptual model-to-knowledge graph transformation. - Pioneered EA ModelSet , a FAIR dataset advancing machine learning in enterprise modeling. - Authored influential papers on inclusive conceptual modeling for disability-aware tools. - Led efforts in model-based construction of enterprise architecture knowledge graphs . Grants/Projects : Automatisiertes End-to-End-Testen von Cloud-basierten Modellierungswerkzeugen (Principal Investigator) Digital Platform Enterprise (Principal Investigator) MFP 4.2 Advanced Analytics for Smart Manufacturing (Principal Investigator) Labs/Teams : Active in the Network Lab at TU Wien, focusing on cutting-edge research in modeling technologies and AI integration.