جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Answer Set Programming
• +۵ مورد دیگر

Zeynep Gözen Saribatur Yaman is a PostDoc Researcher at the Department of Databases and Artificial Intelligence, Technische Universität Wien (TU Wien). Her role is supported by the Austrian Science Fund (FWF) as a Projektassistentin (Dr.in techn.). She is affiliated with the DBAI group and contributes to multiple research projects including AURA (2022–2026), DynaCon (2017–2020), AI4EU (2019–2021), and HumanE-AI-Net (2020–2024). Her primary affiliation is with TU Wien’s Faculty of Informatics, where she focuses on advancing explainable AI through abstraction techniques in logic-based systems. Zeynep holds a Doctorate in Technical Sciences (Dr.techn.) from TU Wien (2019), where her dissertation addressed Abstraction for reasoning about agent behavior with answer set programming . She also holds an MSc in a relevant field, though its specifics are not explicitly detailed in the text. Her research spans multiple funded initiatives, emphasizing both theoretical contributions and applied work in robotics and agent systems. Her research interests revolve around abstraction mechanisms in Answer Set Programming (ASP), argumentation frameworks , and their applications to explainable AI , robotics planning , and agent behavior modeling . She explores techniques to reduce complexity in logic-based systems while preserving critical reasoning aspects, with a focus on making AI systems more transparent and understandable. Zeynep has contributed to several projects aiming to enhance AI reasoning through abstraction. Her work bridges formal methods and practical AI challenges, such as reasoning about dynamic environments and multi-agent systems. She actively participates in international conferences and workshops, including KR, AAMAS, ICAPS, and EPIA, where she presents advancements in knowledge representation and reasoning. Her advising record is not explicitly stated in the provided texts. She has collaborated on grants from FWF, EU Horizon 2020, and other competitive funding bodies. Her research also intersects with cognitive factories and hybrid reasoning systems for robotics applications. As part of the DBAI group at TU Wien, she contributes to the development of AI tools and methodologies that prioritize comprehensibility and scalability. Her lab affiliations include the Knowledge-Based Systems Group (DBAI), where she works on theoretical and applied AI challenges.

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.

• Cyber-Physical Systems
• Biological Systems
• Control Systems
• +۶ مورد دیگر

Radu Grosu is a Full Professor and Head of the Cyber-Physical Systems Research Unit at TU Wien. His research focuses on modeling, analysis, and control of cyber-physical and biological systems, with applications in robotics, autonomous systems, medical imaging, and formal verification. He leads the Scuderia Segfault team for autonomous F1TENTH racing and has extensive collaborations with industry partners like TTTech Auto AG and FFG. His work integrates machine learning, control theory, and formal methods to address challenges in safety-critical systems and autonomous decision-making. Roles: Full Professor, Head of Research Unit, Faculty Council Substitute Member Affiliations: TU Wien, Scuderia Segfault, Austrian Science Fund (FWF) projects Research interests span cyber-physical systems (CPS), cardiac-cell networks, genetic regulatory networks, and AI-driven solutions for healthcare and manufacturing. He has pioneered methods in neural circuit policies, flocking control, and real-time reinforcement learning. His projects include EdgeAI for embedded systems, radiation treatment optimization in glioblastoma, and autonomous vehicle testing frameworks. Key contributions include: Developing Lagrangian reachability analysis for safety verification Advancing neuromorphic IoT architectures and sensor networks Creating tools like DeepSTL for translating requirements into specifications Grants include FFG-funded projects on autonomous driving examiners and energy-efficient neuromorphic systems. His work bridges theoretical foundations with practical implementations in CPS resilience, medical diagnostics, and industrial automation.

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.

Stefan Szeiderمشاهده پروفایل

استاد

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

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

Thomas Bäckمشاهده پروفایل

استاد

• Artificial Intelligence
• Evolutionary Computation
• Machine Learning
• +۶ مورد دیگر

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.

Andreas Holzingerمشاهده پروفایل

دانشیار

• Explainable AI
• Interpretable machine learning
• Health informatics
• +۳ مورد دیگر

Andreas Holzinger leads the Holzinger Group (HCAI, Human-Centered AI Lab) at the Institute for Medical Informatics/Statistics, Medical University Graz, and holds a venia docendi at TU Graz. He serves as consultant for multiple governments and scientific organizations and participates in the advisory boards of national AI strategies. PhD in Cognitive Science (Graz University, 1998) Habilitation in Applied Computer Science (TU Graz, 2003) His research focuses on Explainable AI , Interactive Machine Learning , and Human-in-the-Loop approaches within Health Informatics , combining Human-Computer Interaction and Knowledge Discovery . He founded the Expert Network HCI-KDD and organizes the IFIP Cross-Domain Conference CD-MAKE . His recent publications emphasize human-centric AI , medical data visualization , and interactive knowledge discovery , particularly in biomedical and clinical domains. He has contributed to gamification approaches in education technology and virtual patient applications. 2018 - Top 10 AI expert in Austria (IFIP WCC) 2018 - Elected Austrian Representative to IFIP TC 12 2017 - Invited Keynote at DATA 2017 2016 - Invited Keynote at KES Conference Andreas has participated in 30+ multinational R&D projects (€6M+ budget), published 300+ works (11k+ citations, h-index 48), and actively promotes cross-domain collaboration between AI and biomedical research.

• 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.

Alexey Ignatievمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Formal Methods
• Satisfiability Modulo Theories (SMT)
• +۳ مورد دیگر

Alexey Ignatiev is an Associate Professor in the Optimisation research group at Monash University's Faculty of Information Technology. Previously, he was a postdoctoral researcher and researcher at the University of Lisbon's Faculty of Sciences, focusing on SAT/SMT-based decision procedures. He holds a Ph.D. from the Matrosov Institute for System Dynamics and Control Theory (Russian Academy of Sciences), where his thesis explored parallel CDCL-BDD integration. His research emphasizes formal methods in AI, including explainable AI (XAI), SAT-based reasoning, and optimization for applications like software upgradability, model-based diagnosis, and fault localization. His work spans over 100 publications, with notable contributions to MaxSAT solving (RC2 solver), neuro-symbolic frameworks (NEUSIS), and rigorous explanations for machine learning models. He has collaborated extensively with institutions like the University of Lisbon and Monash University, contributing to advancements in formal verification and interpretable machine learning.

Thomas Eiterمشاهده پروفایل

استاد

• Answer Set Programming
• Knowledge Representation
• Logic Programming
• +۷ مورد دیگر

Thomas Eiter is a Professor at TU Wien's Institute of Logic and Computation. His research focuses on declarative programming paradigms, knowledge representation, and artificial intelligence. He leads projects in neurosymbolic systems, answer set programming (ASP), and stream reasoning, with applications in visual question answering, scheduling optimization, and semantic scene generation. Eiter has contributed to foundational work in ASP semantics, computational complexity, and hybrid reasoning frameworks. His work bridges logical formalisms with practical AI challenges, emphasizing explainability and scalability. Projects like ALASPO and neurosymbolic integration showcase his focus on advancing both theoretical and applied aspects of AI. Projects: HumanE AI Network, WASP, REWERSE Research Themes: Neurosymbolic AI, Answer Set Programming, Stream Reasoning Notable achievements include pioneering work on semiring-based reasoning frameworks and developing efficient ASP solvers like Alpha. His contributions span over 471 publications, emphasizing interdisciplinary applications in computer vision, robotics, and automated planning.

• 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.

• Forest Growth
• Forest Monitoring
• Forest Inventory
• +۹ مورد دیگر

Arne Nothdurft is a University Professor for Forest Monitoring at the University of Natural Resources and Life Sciences (BOKU) in Vienna, Austria, where he chairs the Institute of Forest Growth within the Department of Forest and Soil Sciences. With a career spanning over two decades in forest research and academic leadership, he has established himself as a leading expert in advanced forest inventory techniques and forest growth modeling. Professor Nothdurft's research focuses on the application of cutting-edge technologies in forest monitoring, particularly LiDAR and personal laser scanning systems for forest inventory. His work bridges the gap between traditional forestry practices and modern digital solutions, with emphasis on mixed species forest management, climate change adaptation, and the development of smart forestry systems. His research interests encompass forest growth modeling, tree species classification using point cloud data, and the development of spatial prediction models for forest inventory parameters. His recent publications demonstrate a strong trend toward integrating artificial intelligence with forestry applications, particularly in the analysis of 3D point cloud data from laser scanning technologies. His work spans both theoretical advancements in spatial statistics and practical applications for forest managers, with a particular focus on improving the accuracy and efficiency of forest inventory systems. Thurn und Taxis Förderpreis für die Forstwissenschaft (2008) Professor Nothdurft has supervised numerous master's and doctoral theses, primarily focused on the application of laser scanning technologies in forestry, forest inventory optimization, and growth modeling. His research is supported by multiple ongoing projects funded by Austrian research agencies and federal ministries, with a strong emphasis on practical applications for forest management. He leads the Institute of Forest Growth, which maintains the Lehrforst Rosalia long-term forest monitoring site, and collaborates extensively with the Institute of Forest Engineering on smart forestry initiatives.

• Symbolic Computation
• Machine Learning
• Computer Algebra
• +۲ مورد دیگر

Tereso del Rio Almajano is a Researcher at the Research Institute for Symbolic Computation (RISC), Johannes Kepler University, Linz, Austria. His work focuses on optimizing Cylindrical Algebraic Decomposition (CAD) algorithms through machine learning and heuristic development to address doubly exponential computational complexity in symbolic computation. He earned his PhD from Coventry University's Centre for Computer Science and Mathematical Modelling under supervisors Dr. Matthew England, Dr. AmirHosein Sadeghimanesh, Prof. Vasile Palade, and Dr. Kamal Bentahar. His dissertation, "Heuristics and Machine Learning to Improve Symbolic Computation Algorithms: Speeding Up Quantifier Elimination", pioneered methodologies for accelerating CAD while ensuring output validity. His research spans Symbolic Computation, Machine Learning, and Computer Algebra, with specific expertise in Quantifier Elimination and variable ordering optimization. Key contributions include dataset augmentation techniques that reduced CAD construction time by 38%, the state-of-the-art gmods heuristic derived from complexity analysis, and the first application of Explainable AI in symbolic computation to extract human-interpretable heuristics from machine learning models. As part of RISC, he addresses computational challenges in algebraic geometry and quantifier elimination, demonstrating how machine learning can surpass human-designed heuristics in symbolic computation efficiency while maintaining mathematical certainty.

• 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.

• Artificial Intelligence
• Evolutionary Algorithms
• Machine Learning
• +۵ مورد دیگر

Lars Mehnen serves as a Senior Lecturer and Researcher at the University of Applied Sciences Technikum Wien (UAS Technikum Wien) in Austria, where he has been teaching since 2019 in Computer Science and previously from 2003-2019 in Biomedical Engineering. His academic career includes significant roles at Vienna University of Technology as University Assistant (2004-2007) and Research Assistant (1998-2002), along with leadership positions in major international space initiatives including QB50 (2010-2013), GENSO (2006-2010), and SSETI (1999-2006). TU-Wien: Dipl.Ing. in Computer Engineering and Medical Informatics Dr. Techn. from Institute for Fundamentals and Theory of Electrical Engineering and Institute of Technical Mathematics Mehnen's research spans multiple domains with particular expertise in Artificial Intelligence, Evolutionary Algorithms, and Sensor Technology. His work in explainable and non-parametric evolutionary algorithms represents cutting-edge contributions to AI methodology. In biomedical engineering, he has developed innovative magnetoelastic skin curvature sensors for cardiovascular monitoring and other medical applications. His teaching portfolio is exceptionally diverse, covering Statistics (parametric and non-parametric), Artificial Intelligence, Aerodynamics (particularly for sports equipment), Physics for game engineering, and extensive programming topics including Java, C/C++, Python, and functional programming languages. His publication record from 2001-2024 shows a clear evolution from early work on magnetostrictive bilayer sensors toward more recent applications of machine learning in biological classification, medical diagnostics, and educational technology. The 2024 publication on ChatGPT's diagnostic accuracy for medical conditions demonstrates his engagement with the latest AI developments. His research consistently bridges theoretical computer science with practical applications in healthcare, sports engineering, and space technology. Mehnen has been deeply involved in European collaborative projects throughout his career, including QB50 (EC F7), GENSO (ESA initiative), and B-Sens (EC-Project in 5th framework). These projects involved coordination with numerous universities and institutions across Europe, demonstrating his ability to lead international research collaborations. His work on the SSETI Express satellite project resulted in the successful launch of a micro-satellite at the end of 2005. At UAS Technikum Wien, Mehnen contributes to curriculum development across multiple domains including statistics, AI, programming, and physics. His research on paraglide control systems demonstrates application of his expertise to sports safety technology, while his recent work in teaching analytics shows commitment to improving educational outcomes through data-driven approaches.