جستجوی استاد راهنمای دانشگاهی

Renata Raidouمشاهده پروفایل

دانشیار

• Medical Visualization
• Visual Analytics
• Uncertainty Visualization
• +۳ مورد دیگر

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.

Renata Georgia Raidouمشاهده پروفایل

دانشیار

• Medical Visualization
• Visual Analytics
• Uncertainty Visualization
• +۴ مورد دیگر

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.

• 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.

• Decision support systems
• Computational intelligence
• Recommender systems
• +۴ مورد دیگر

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 Taoمشاهده پروفایل

دانشگاهی

• Artificial Intelligence
• Multimedia Computing
• Computer Vision
• +۳ مورد دیگر

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.

Pietro Lio'مشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Machine Learning
• +۲ مورد دیگر

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.

Zhu Xiaoxiangمشاهده پروفایل

استاد

• Synthetic Aperture Radar
• Remote Sensing
• Global Urban Mapping
• +۴ مورد دیگر

Xiaoxiang Zhu is a Full Professor for Data Science in Earth Observation at Technical University of Munich (TUM) and Director of the International AI Future Lab (AI4EO). She leads interdisciplinary research on signal processing and machine learning applied to Earth observation (EO) data, addressing global challenges like urbanization and climate change. Her work focuses on extracting geoinformation from big EO datasets using innovative AI techniques. Education & Positions: Professor (W3) since 2019, TUM Former Head of EO Data Science Department at German Aerospace Center (DLR) Adjunct Teaching Professor (2013–2015) Research Interests: Deep learning in SAR and multispectral imagery Global urban morphology mapping Uncertainty quantification in AI models EO data fusion and big data analytics Climate change monitoring via satellite data Articles Trends: Her publications emphasize AI-driven solutions for EO challenges, including SAR tomography, benchmark datasets (e.g., So2Sat LCZ42), and uncertainty estimation in neural networks. Over 220 journal papers and 173 conference papers highlight her contributions to geosciences and remote sensing. Awards: IEEE Fellow (2021) ERC Grants (Starting & Proof of Concept) Heinz Maier-Leibnitz-Preis (2015) Member of German and Bavarian Academies of Sciences Advising & Grants: Supervised PhD students (e.g., Mou) Secured €10M+ in research funding Co-led Helmholtz AI Research Field MASTr (2019–2022) Labs & Teams: Founder of AI4EO Lab Co-leader of Munich Data Science Research School (MUDS) Member of ELLIS Society and IEEE committees

• Conceptual Modeling
• Model-Driven Engineering
• Enterprise Architecture
• +۳ مورد دیگر

**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.

• Cyber-Physical Systems
• Runtime Verification
• Formal Methods
• +۳ مورد دیگر

Dejan Nickovic is an Associate Professor in the Department of Cyber-Physical Systems at TU Wien. His primary affiliation is with the Research Area Cyber-Physical Systems (E191-01). Nickovic's research focuses on formal methods, runtime verification, and fault localization in cyber-physical systems (CPS). He has contributed to automated testing frameworks, specification mining, and formal validation techniques for embedded and reactive systems. His work emphasizes bridging the gap between theoretical formalisms and practical CPS applications. Notable research directions include mining specifications from data (e.g., timing diagrams), developing tools like TD-Magic and DeepSTL, and advancing hypernode automata for complex system modeling. He has also explored fault-injection methods (FIM) for Simulink models and mutation testing strategies for improving CPS reliability. Nickovic collaborates extensively with industry partners, as evidenced by his work on production-test coverage analysis in simulation environments. His research has been published in top-tier conferences and journals, focusing on topics like information-flow interfaces, hyperproperties, and adaptive testing strategies. He advises graduate students on CPS-related theses, including those on fault localization, analog-mixed signal verification, and failure explanation in CPS models. His contributions have advanced both academic and industrial applications of formal methods in CPS design and validation.

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Prof. Allan Hanbury is a full professor at TU Wien's Faculty of Informatics, leading the E-Commerce Research Area. His work focuses on domain-specific systems for information extraction and retrieval, with expertise in machine learning, natural language processing, and medical informatics. Key projects include developing tools like CRUISE-Screening for systematic literature reviews and VoMBaT for high-recall search evaluation. He coordinates initiatives in transparent automated content moderation and image mining for patents. His research bridges theoretical advancements with practical applications in healthcare, legal domains, and digital humanities. Publications span over 240 works, emphasizing reproducibility, explainability in AI, and cross-lingual systems. Active in organizing conferences like PatentSemTech and contributing to CLEF challenges. Advises on thesis topics involving hate speech detection, legal text analysis, and graph-based methods. Collaborates internationally on projects like CSMeD (clinical trial matching) and BRISE (legal corpus development). His work integrates NLP, IR, and data mining to address real-world challenges in information access and AI transparency.

• Cybersecurity
• Code Obfuscation
• Malware Analysis
• +۳ مورد دیگر

Sebastian Schrittwieser is a Researcher in the Research Group Security and Privacy, part of the Faculty of Computer Science. His work focuses on cybersecurity, code obfuscation, malware analysis, and machine learning applications in security. He leads and contributes to projects like INODES (Cyber Defense Strategies) and EMRESS (Resilience Evaluation Models). His research bridges theoretical foundations and practical applications, addressing challenges in software protection and threat detection. Key research interests include: Code Obfuscation Techniques and Resistance Adversarial Machine Learning and Risk Assessment Malware Analysis and Program Simulation User Behavior in Cybersecurity Contexts Recent publications emphasize empirical studies on IT/OT infrastructure security, graph neural network vulnerabilities, and quantum-inspired machine learning. He actively collaborates with institutions like SBA Research and presents at international conferences. Grants include Research Funding for projects on optimal cyber defense strategies (INODES) and software resilience evaluation (EMRESS). His work aligns with interdisciplinary efforts in security engineering and privacy-preserving technologies.

Emanuele Rodolàمشاهده پروفایل

استاد

• Machine Learning
• Geometry Processing
• Computer Vision
• +۱ مورد دیگر

Emanuele Rodolà is a Full Professor of Computer Science at Sapienza University of Rome , leading the GLADIA group (Geometry, Learning and Applied AI) funded by an ERC Grant and a Google Research Award. Former Assistant (2017-2020) and Associate Professor at Sapienza Postdoc at USI Lugano (2016-2017), TU Munich (Alexander von Humboldt Fellow, 2013-2016), and University of Tokyo (JSPS Research Fellow, 2013) His research spans geometry processing , graph/geometric deep learning , computer vision , and language/sound processing , with over 100 publications. He has served on program/organizing committees of top conferences and founded successful workshops in machine learning and graphics. Ellis Fellow ERC Grant recipient Google Research Award Alexander von Humboldt Fellow JSPS Research Fellow