جست‌وجوی استاد و پژوهشگر

Katja Hoseمشاهده پروفایل

استاد

• Knowledge Graphs
• Semantic Web
• Data Management
• +۷ مورد دیگر

Katja Hose is a Full Professor of Data Management at TU Wien's DBAI research unit, heading the Data Management and Knowledge-Driven AI Lab. She previously held a Poul Due Jensen Foundation Professorship at Aalborg University. Her research focuses on data and knowledge engineering, including graph databases, knowledge graphs, querying, analytics, and machine learning, with interdisciplinary applications in bioscience, healthcare, and environmental assessment. Education: PhD in Computer Science (Ilmenau University of Technology, 2009), Postdoc at Max Planck Institute for Informatics (2009–2012). Academic roles include Program Co-Chair for ISWC 2024 and EDBT 2023, and editorial board membership at VLDBJ and TGDK. She leads projects like TARGET (health virtual twins) and ARMADA (data management). Research Interests: Knowledge Graphs, Semantic Web, Big Data, Machine Learning, Data Integration, and Provenance Systems. Key contributions include SHACL shape extraction, conversational data analytics, and environmental knowledge graphs. Awards include the 2025 Distinguished Meta-Reviewer Award and 2024 Manfred Paul Award. Advising and Grants: Supervised students including E. Pürmayr (Diploma Thesis 2025). Active in EU projects (TARGET, ARMADA) and grant coordination. Labs/Teams: DMKI Lab at TU Wien, collaborating with interdisciplinary teams in healthcare and environmental science.

Ian Horrocksمشاهده پروفایل

استاد

• Knowledge Representation
• Description Logics
• Automated Reasoning
• +۴ مورد دیگر

Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Fellow of Oriel College. His research focuses on knowledge representation, description logics, automated reasoning, and semantic web technologies. He has held academic positions at the University of Manchester (2003–2007) and served as Chief Scientist at Cerebra Inc. (2001–2006). Horrocks earned his BSc (1st class), MSc, and PhD in Computer Science from the University of Manchester (1981–1997). His work includes foundational contributions to ontology languages (e.g., OWL) and reasoning systems such as HermiT and ELK. He has supervised over twenty doctoral students and postdoctoral researchers. His honors include Fellowships from the Royal Society (2011), ECCAI (2009), and the British Computer Society (2005). He serves as Editor-in-Chief of the Transactions on Graph Data and Knowledge and leads initiatives in semantic web standards and knowledge graph applications. Key Roles: Editor-in-Chief (Journal of Web Semantics), Co-Chair (W3C OWL Working Group) Grants: EPSRC Senior Research Fellowship (2005), numerous international collaborations Labs: Oxford Semantic Technologies, involvement in projects like RDFox and PAGOdA

Francesco Locatelloمشاهده پروفایل

استادیار

• Causal Learning
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Francesco Locatello is a tenure-track Assistant Professor at the Institute of Science and Technology Austria (ISTA), leading the Causal Learning and Artificial Intelligence lab. He is also an AI Resident at the Chan Zuckerberg Initiative. He holds a PhD from ETH Zürich, co-advised by Gunnar Rätsch and Bernhard Schölkopf. His research focuses on causal representation learning, score matching, and object-centric learning, with applications in machine learning and AI. His work has been recognized with prestigious awards, including the ICML 2019 Best Paper Award and the Hector Foundation Award (2023). Education: PhD in Machine Learning, ETH Zürich (advisors: Gunnar Rätsch, Bernhard Schölkopf) Research Interests: Causal Learning, Causal Representation Discovery, Score Matching Algorithms, Object-Centric Learning, Robust Generalization in AI, and Applications in Vision and Reinforcement Learning. Recent Work Trends: His publications emphasize causal mechanisms in neural representations, scalable causal discovery methods, and improving model generalization through latent space analysis. Recent studies explore geometric representations, mechanistic neural networks, and OOD detection using relative angles. Awards: ICML 2019 Best Paper Award Hector Foundation Award for Outstanding Achievements in Machine Learning (2023) Google Research Scholar Award (2024) Advising & Teams: Supervises a dynamic lab with students and postdocs across ISTA, ELLIS, and partner institutions. Notable advisees include Dingling Yao (ISTA), Riccardo Cadei (co-advised with Cordelia Schmid), and Marco Fumero (now a postdoc at ISTA). Collaborates with leading researchers like Arthur Gretton, Max Welling, and Volkan Cevher. Labs & Initiatives: Leads the Causal Learning and AI lab at ISTA, contributing to ELLIS programs and fostering interdisciplinary collaborations in causal AI.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Professor Thomas Lukasiewicz is a Full Professor and Head of the Artificial Intelligence Techniques research group at the Faculty of Informatics, Vienna University of Technology (TU Wien). His research focuses on enabling machines to mimic human-like intelligence through techniques spanning deep learning, symbolic reasoning, and predictive coding. Key areas include explainable AI, hybrid neurosymbolic systems, and applications in healthcare and law. He teaches courses such as Deep Learning for Natural Language Processing, Scientific Research and Writing, and multiple seminars in artificial intelligence and knowledge representation. His research projects include Explainable AI in Healthcare (2023–2027) and foundational work on predictive coding networks. His publications (15+ recent articles) address medical image segmentation, neurosymbolic frameworks, and language model evaluation in mathematics. Notable work includes neurosymbolic hybrid models (CCN⁺), reinforcement learning for medical report generation, and theoretical foundations of predictive coding networks.

• Biomechanics
• Rehabilitation Engineering
• Digital Health
• +۴ مورد دیگر

Brian Horsak is a Professor and Head of the Center for Digital Health and Social Innovation at Fachhochschule Steyr. He holds an endowed professorship in Applied Biomechanics and Rehabilitation Research, focusing on integrating advanced technologies like VR/AR, machine learning, and wearable devices into clinical gait analysis and motor rehabilitation. His roles include leading the Institute of Health Sciences and contributing to the Department of Health Sciences and Media and Digital Technologies. Education: Dr. rer. nat. (2012, University of Vienna), Habilitation in Kinesiology (2020, University of Vienna), Master's in Sports Science (2002–2008, University of Vienna). Research interests revolve around improving patient care through biomechanical innovations, including musculoskeletal simulations, gait pattern analysis, and rehabilitation technologies. He leads projects like ReMoCap-Lab (motion capture for motor rehabilitation) and chairs the Applied Biomechanics in Rehabilitation Research initiative. Key achievements include the Lower Austria Innovation Prize (2021), multiple best paper awards, and grants for projects like TRUST AI and VReeze. His work bridges clinical practice with digital health solutions, emphasizing explainable AI (XAI) in gait classification and VR-based balance training. Notable contributions include developing the GaitRec dataset and studies on smartphone-based motion capture reliability. He collaborates internationally, publishing widely in Gait & Posture , Scientific Reports , and IEEE journals. Current projects focus on AI-driven gait analysis, musculoskeletal modeling, and XR applications in healthcare.

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +۴ مورد دیگر

Armin Kirchknopf serves as a Junior Researcher at the Media Computing Research Group within the Institute of Creative Media/Technologies, Department of Media and Digital Technologies at the University of Applied Sciences St. Pölten. His interdisciplinary work bridges artificial intelligence, computer vision, and social media analysis, with significant contributions to misinformation detection and disaster response systems. Based at Campus-Platz 1 in St. Pölten, Austria, he actively collaborates on EU-funded projects and publishes in top-tier AI venues. His educational journey spans humanities and technology: a Bachelor of Arts in Egyptology and Master of Arts in Classical Archaeology from the University of Vienna (including fieldwork at excavation sites across Austria, Germany, and Egypt), followed by a Bachelor of Science in Media Technology from FH St. Pölten. This unique background informs his human-centered AI research approach. Kirchknopf's research centers on explainable multimodal AI systems for real-world challenges. His recent work demonstrates expertise in transformer-based architectures for cross-lingual fake news detection, sexism identification, and flood monitoring through social media imagery. He pioneers techniques like Grad-CAM for object detection explainability and develops visualization tools for complex data interpretation, emphasizing transparency and social impact in AI deployment. Analysis of his 13 publications (2017-2022) reveals a strategic shift toward applied AI in societal contexts , particularly using social media data for disaster management and combating online toxicity. His projects consistently integrate computer vision with natural language processing, showing increasing sophistication in multilingual capabilities and model interpretability frameworks. His scientific recognition includes: Creative Business Award for co-developing the Tenjin learning quiz application No documented student advisement or grant leadership appears in current records, though he actively mentors through project-based collaborations. His work with the Media Computing Research Group drives innovation in educational technology and public safety applications. Kirchknopf contributes to the Media Computing Research Group's portfolio including Fake News Detection, SAiEX (Safe AI with explainable integrity), InfraBase (building footprint segmentation), and Ressel Center music therapy projects. His cross-disciplinary collaborations span computer scientists, archaeologists, and social scientists, reflecting the group's commitment to human-centric technological solutions .

• Model-Based Testing
• Automata Theory
• Machine Learning
• +۵ مورد دیگر

Bernhard Aichernig is an Associate Professor at the Institute of Software Engineering and Artificial Intelligence. His work bridges formal methods, model-based testing, and artificial intelligence, with a focus on automata learning, digital twins, and AI-assisted programming. Institution: Institute of Software Engineering and Artificial Intelligence Key Research Areas: Model-Based Testing, Automata Learning, AI-Driven Verification His research explores the integration of machine learning into formal verification, enabling scalable testing of complex systems like IoT devices and reinforcement learning agents. Recent projects include AI-Augmented DevOps frameworks (AIDOaRT) and digital twin validation (LearnTwins). Notable scientific awards include multiple best paper recognitions at SEFM (2020, 2021) and the TAYSIR Competition first place (2023). His publications emphasize hybrid approaches combining genetic programming, SMT solving, and neural networks for system modeling. 2025 : AI-assisted programming, timed automata via domain knowledge 2024 : Stochastic environment modeling, Git system learning 2023 : Reinforcement learning under partial observability, digital twins for VPN servers He actively contributes to testing frameworks like AALpy and investigates explainable AI for fault diagnosis in cyber-physical systems.

• Cybersecurity
• Blockchain
• Machine Learning
• +۱۲ مورد دیگر

Edgar Weippl is a Professor at the Faculty of Computer Science, University of Vienna, where he serves as Vice Dean and Head of the Research Group Security and Privacy. His work spans cybersecurity, blockchain, and machine learning, with teaching roles in information security and software security courses. Current Positions: Vice Dean (Faculty of Computer Science), Head (Security and Privacy Research Group), Deputy Head (Neuroinformatics & Knowledge Engineering Groups) Research Interests: Cybersecurity, blockchain, IoT security, code obfuscation, privacy technologies, reinforcement learning, and socio-technical systems security Selected Publications: Focus on blockchain privacy, VoWiFi security, code obfuscation, and reinforcement learning applications

• Computational Logic
• Declarative Logic Programming
• Answer-Set Programming
• +۵ مورد دیگر

Hans Tompits is an Associate Professor in the Department of Knowledge-Based Systems at Technische Universität Wien (Vienna University of Technology). His research focuses on computational logic, declarative logic programming, and formal methods, with a particular emphasis on Answer-Set Programming (ASP). He coordinates the Master's program in Logic and Computation and leads projects in areas such as formal methods for optimization, fault-tolerant autonomous systems, and algorithmic composition. His work bridges theoretical advancements with practical applications, including tools like SeaLion (an ASP IDE with debugging support) and dlvhex (an ASP-based semantic web reasoner). He has contributed to foundational topics like program equivalence, debugging techniques, and integration of ASP with external systems. His recent projects address challenges in autonomous vehicle architectures, music composition algorithms, and safety-critical system design. Tompits has published extensively on topics ranging from nonmonotonic reasoning and modal logics to the development of declarative programming tools. His interdisciplinary approach spans computer science, mathematics, and AI, with applications in both academic and industrial contexts.

• Legal Informatics
• Artificial Intelligence and Law
• Data Protection and Privacy
• +۵ مورد دیگر

Erich Schweighofer serves as Associate Professor at the University of Vienna within the Institute for European, International and Comparative Law, specifically affiliated with the Department of International Law and International Relations. His research activities are centered at the Juridicum building (Schottenbastei 10-16, 1010 Vienna), where he maintains an active office presence with scheduled consultation hours. His scholarly focus spans Legal Informatics , Artificial Intelligence and Law , Data Protection , and Legal Knowledge Representation , with particular emphasis on explainable AI systems for legal contexts and formal methodologies for translating legal norms into computational frameworks. This interdisciplinary work bridges jurisprudence and computer science through projects examining biometric regulation, autonomous vehicle governance, and natural language processing applications in legal domains. Analysis of his 2021-2024 publications reveals consistent thematic progression toward operationalizing legal principles in AI systems, with increasing focus on transparency mechanisms, temporal logic for dynamic regulations, and cross-jurisdictional compliance challenges. His work predominantly appears in the International Legal Informatics Symposium (IRIS) proceedings and JURIX conferences, reflecting deep engagement with the legal informatics community. Professor Schweighofer leads a dedicated research team including project assistants Mag. Jessica Fleisch, Mag. Jonas Pfister, Felix Schmautzer, and Mag. Jakob Zanol, while actively participating in the University of Vienna's Working Group on Legal Informatics (Arbeitsgruppe Rechtsinformatik). His collaborative approach extends to organizing the biennial IRIS symposium, which has established itself as a cornerstone event for European legal informatics scholarship since 1998.

Navid Rekab-sazمشاهده پروفایل

استادیار

• Natural Language Processing
• Information Retrieval
• Fairness and Bias in AI
• +۷ مورد دیگر

Navid Rekab-saz is an Assistant Professor at the Institute of Computational Perception, Johannes Kepler University Linz (JKU), Austria. He is actively involved in research and teaching, offering courses such as Natural Language Processing and Natural Language Processing with Deep Learning . He maintains regular office hours and is accessible via email and a dedicated booking system for meetings. His research focuses on natural language processing , information retrieval , fairness and bias in AI , and recommender systems , with applications in humanitarian action and ethical AI. He employs deep learning and machine learning techniques to address challenges in bias mitigation, explainability, and domain adaptation. His work often bridges technical innovation with societal impact, especially in developing inclusive and fair AI systems. The recent publications of Navid Rekab-saz reflect a strong trend in debiasing strategies , parameter-efficient learning , and evaluation of societal biases in search and recommendation systems. His research spans from foundational work on word embeddings and retrieval models to applied studies in humanitarian NLP and gender bias in user queries. He frequently collaborates with a broad network of researchers and contributes to the development of datasets and benchmarks. Scientific Awards: Best Student Paper Award at ISMIR 2022 for 'Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms' Advising and Grants: Navid Rekab-saz has advised and collaborated with numerous students and researchers, many of whom are co-authors on his publications. While specific grant details are not listed in the provided text, his extensive publication record in top-tier venues suggests active involvement in funded research projects, likely supported by national or European funding bodies. He is also engaged in interdisciplinary research, particularly at the intersection of technical AI and legal or social implications. Labs and Teams: He is a core member of the Institute of Computational Perception at JKU, where he contributes to research projects in computational linguistics and AI. He collaborates closely with the team led by Prof. Markus Schedl and participates in initiatives related to music information retrieval, fairness in AI, and humanitarian applications of NLP.

• Neurosymbolic AI
• Answer Set Programming
• Visual Question Answering
• +۳ مورد دیگر

Nelson Nicolas Higuera Ruiz is a PreDoc Researcher at the Vienna University of Technology, affiliated with the Faculty of Informatics' Knowledge-Based Systems research group. His work bridges logic programming and deep learning for explainable AI. Research Focus: Neurosymbolic AI, Visual Question Answering (VQA), Answer Set Programming (ASP), and hybrid reasoning systems Projects: Leads optimization research in the LCS (2017–2025) project, developing neurosymbolic approaches for intelligent systems Key Contributions: Pioneering adaptive large-neighbourhood search algorithms for ASP optimization, modular neurosymbolic architectures, and contrastive explainability frameworks for VQA Collaborations: Active in international workshops and conferences including IJCAI, AAAI, and CLeaR, frequently collaborating with researchers like Thomas Eiter and Johannes Oetsch Publications: Focus on neurosymbolic integration, optimization algorithms, and explainability across AI, logic programming, and computer vision domains

Fajar Juang Ekaputraمشاهده پروفایل

عضو هیئت علمی

• Semantic Web
• Knowledge Graphs
• Neurosymbolic AI
• +۴ مورد دیگر

Fajar Juang Ekaputra is a Tenure Track Assistant Professor at the Institute of Data, Process, and Knowledge Management (DPKM), WU Vienna and a part-time Postdoctoral Researcher at the Data Science research unit, TU Wien . With a focus on Semantic Web , Knowledge Graphs , and their integration with Machine Learning in Neurosymbolic AI systems, his work spans domains like Cyber-Physical Systems and Materials Engineering . Education: Dr.techn. (2018), TU Wien M.T. (2010) and S.T. (2008), Institute Teknologi Bandung (ITB) Research Interests center on hybrid AI systems combining Semantic Web and Machine Learning , with applications in Cyber-Physical Systems (e.g., smart grids, smart buildings), data privacy in smart cities, and materials engineering . His 102+ publications include frameworks like SWeMLS-KG and SHACL4Protege . Recent Articles (2024) address explainable AI in cyber-physical systems, privacy trust in data infrastructures, and neurosymbolic frameworks . Earlier works (2023–2022) explore ontology-based data management , auditable AI , and hybrid system architectures . Scientific Awards: Best Paper Awards (ICoDSE 2023, ICoDSE 2016) Best Poster Nomination (SEMANTiCS 2019) PhD Scholarship (Austria’s Agency for Education and Internationalisation, 2012) Advising includes supervising PhD students (e.g., Majlinda Llugiqi, Katrin Schreiberhuber) and master’s theses on topics like knowledge graph characteristics and data quality assessment . He leads projects such as FAIR-AI (FFG-funded, 2024–2026) and SENSE (Horizon Europe, 2023–2025).

• Work Psychology
• Organizational Psychology
• Economic Psychology
• +۳ مورد دیگر

Bettina Kubicek is Professor of Work and Organizational Psychology at the University of Graz, Faculty of Natural Sciences, where she leads research in the Smart Regulation profile area and contributes to the Human Factor in Digital Transformation and Brain and Behavior research networks. Her work bridges psychological theory with practical organizational challenges in contemporary workplaces. Her research spans work psychology, organizational psychology, and economic psychology with emphases on flexible work arrangements, work intensification, and human-robot collaboration. She investigates cognitive demands of flexible work, work-home interface dynamics, and digital transformation impacts through diary studies and experimental designs. Recent work explores telework challenges, AI in HR, and trust mechanisms in human-robot teams during disaster response. Analysis of her recent publications reveals growing focus on human-robot interaction in Industry 4.0, cognitive aspects of telework, and cross-cultural validation of work addiction scales. Her methodology combines intensive longitudinal designs with physiological measures, increasingly incorporating VR interfaces and AI-driven personnel selection experiments. Scientific awards include: Wirtschaftskammerpreis for project 'Flexible Arbeitsbedingungen und die Vereinbarkeit von Beruf und Familie' (2015) Innovationspreis der Universität Wien (2012) Multiple research stipends for University of Wisconsin-Madison stays (2005-2008) She has secured significant competitive grants including FWF-funded 'Cognitive demands of flexible work: Risks and challenges' (2017-2022) and FFG-supported 'Enabling and Assessing Trust when Cooperating with Robots in Disaster Response' (2021-2023), examining trust calibration and cognitive load in human-robot teams. Current projects address evidence-based guidelines for ICT use in work-life boundaries. As core member of the Human Factor in Digital Transformation network, she collaborates with engineers, computer scientists, and healthcare professionals to develop human-centered technological solutions, particularly in healthcare robotics and disaster response systems.

Torsten Hoeflerمشاهده پروفایل

استاد

• Deep learning Systems
• Parallel programming models
• High performance and datacenter networking
• +۸ مورد دیگر

Torsten Hoefler is a Full Professor of Computer Science at ETH Zurich, Switzerland, with an adjunct appointment in Electrical Engineering. He previously held roles at the National Center for Supercomputing Applications (University of Illinois at Urbana-Champaign) and Indiana University. Full Professor of Computer Science, ETH Zurich (2020–present) Adjunct Professor of Electrical Engineering, ETH Zurich (2020–present) Member at Large, ACM SIGHPC Executive Committee (2013–present) Leadership roles in the MPI Forum and Blue Waters project His research focuses on performance-centric system design , with emphasis on scalable networking, parallel programming models, and performance modeling. Key contributions include the Slim Fly network topology, Data-Centric Python framework, and innovations in parallel graph computations and RDMA-based systems. Recent publications span topics like LLM training networks , quantization geometry , chiplet interconnects , and AI-driven climate modeling , reflecting his interdisciplinary approach combining HPC, AI, and hardware-software co-design. ACM Gordon Bell Prize (2019) ERC Consolidator Grant (2020) IEEE TCSC Award for Excellence (2019) SIAM SIAG/SC Junior Scientist Prize (2012) Latsis Prize of ETH Zurich (2015) He has received multiple best paper awards at top conferences (SC10, SC13, SC14, SC19, IPDPS'15, HPDC'15, OOPSLA'16) and contributed to MPI-3 standardization.