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Katja HoseView profile

Professor

• Knowledge Graphs
• Semantic Web
• Data Management
• +7 more

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.

• Data Visualization
• Human-Computer Interaction
• Big Data Analytics
• +13 more

Huamin Qu is a Chair Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He serves as the Founding Dean of the Academy of Interdisciplinary Studies (AIS), Founding Head of the Division of Emerging Interdisciplinary Areas (EMIA), and was the Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ). Qu directs the VisLab and coordinates the Human-Computer Interaction (HCI) group. He obtained his BS in Mathematics from Xi'an Jiaotong University and MS/PhD in Computer Science from Stony Brook University. Qu's research integrates Data Visualization , Human-Computer Interaction , and Human-Centered AI , with applications in urban informatics, social networks, and explainable AI. His work focuses on developing interactive systems for big data analytics, visual storytelling, and AI-driven decision support. Research extends to multimodal communication, fintech, and augmented reality applications. His publications emphasize visual analytics for complex datasets (mobility, social media, financial), interaction techniques for immersive environments, and AI-enhanced visualization tools. Recent works explore explainable AI interfaces and large-scale data communication frameworks. IEEE Visualization Academy (2020) IEEE VGTC Technical Achievement Award AI 2000 Most Influential Scholar (2019, 2023, 2024) 21 Best Paper/Honorable Mention awards IBM Faculty Award (2009) APICTA Merit Award (2015) Yelp Dataset Grand Prize (2018) Qu has advised 48 PhD graduates (21 now faculty at institutions like UC Davis, University of Minnesota, Texas A&M) and 30 MPhil students. He secured major grants including RGC theme-based projects (digital citizenship, air pollution), UGC AoE (slope safety), and China's 973 Program. As VisLab director, he leads 20+ researchers in visualization/HCI projects adopted by Microsoft, IBM, Huawei, and Tencent.

• Machine Learning
• Computer Vision
• Robust Machine Learning
• +3 more

Christoph H. Lampert is a Professor at the Institute of Science and Technology Austria (ISTA), leading the Machine Learning and Computer Vision (MLCV) Group. His research spans machine learning, computer vision, and trustworthy AI with emphasis on robustness and fairness. He serves as ELLIS Fellow and Unit Director for ISTA's ELLIS unit. His research program focuses on foundational challenges in machine learning including robustness against distribution shifts, fairness in algorithmic decision-making, and verification of neural networks. Key contributions include work on 1-Lipschitz networks for robust classification, multi-source learning frameworks, and federated learning architectures. The group maintains strong output in top-tier venues through theoretical and empirical approaches. Recent publications (2023-2025) demonstrate consistent focus on verification, robustness, and multi-source learning, with notable recognition including the DARPA Disruptive Ideas award for logic gate neural network verification. Work frequently bridges computer vision and machine learning theory, with applications in safety-critical systems. Scientific Awards: ELLIS Fellow DARPA Disruptive Ideas award at NeuS (2025) for "Logic Gate Neural Networks are Good for Verification" Professor Lampert has supervised 12+ PhD students including recent graduates Alex Peste (2023), Nikola Konstantinov (2022), and Mary Phuong (2021), with current advisees including Max Cairney-Leeming and Egor Zverev. His group secures consistent publication placements at NeurIPS, ICML, and ICLR while editing major volumes like "Advanced Structured Prediction" (MIT Press 2015). The MLCV group comprises 10+ members including postdocs and PhD students, operating within ISTA's ELLIS unit (approved 2019). The team maintains active collaborations across Europe through the ELLIS network and regularly hosts visiting researchers.

Ian HorrocksView profile

Professor

• Knowledge Representation
• Description Logics
• Automated Reasoning
• +4 more

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 LocatelloView profile

Assistant Professor

• Causal Learning
• Artificial Intelligence
• Machine Learning
• +3 more

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
• +7 more

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
• +4 more

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
• +4 more

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
• +5 more

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
• +12 more

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
• +5 more

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.

Caroline BassettView profile

Professor

• Digital Media
• Computational humanities
• AI and the transformation of knowledge cultures
• +15 more

Caroline Bassett is Professor of Digital Humanities at the University of Cambridge and Director of Cambridge Digital Humanities. She previously held positions at the University of Sussex as Lecturer (2000-2008), Reader (2008-2016), and Professor of Digital Media and Communications (2016-2019), where she co-founded the Sussex Humanities Lab. Her career began in technology journalism before transitioning to academia. Her research critically examines computational technologies' societal and cultural impacts. Key interests include digital media theory, AI's transformation of knowledge cultures, feminist analyses of technology, media archaeologies, automation studies, and the intersection of science fiction with technological utopianism. Recent work explores sound studies, mobile media, gender politics in tech, and everyday digital practices. Bassett's publications consistently engage with digital culture through critical, feminist, and historical lenses. Major thematic clusters include: technological feminism and gender politics (35%), automation histories and anxieties (25%), media archaeology and theory (20%), digital humanities methodologies (15%), and sound/silence studies (5%). Her work increasingly addresses AI ethics and explainability in recent years. Honors and Awards: Helsingin Sanomat Foundation Fellow, Helsinki Collegium for Advanced Studies (2015-2017) IGSF Visiting Fellow, McGill University (Jan-Sept 2010) Leverhulme-funded Associate Visiting Faculty, George Mason University (2002-2003) She directs Cambridge Digital Humanities, a major interdisciplinary research initiative. Previously, she established the Sussex Humanities Lab, fostering digital research collaborations across arts, humanities, and social sciences.

• Legal Informatics
• Artificial Intelligence and Law
• Data Protection and Privacy
• +5 more

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.

• Data Mining
• Machine Learning
• Clustering Algorithms
• +3 more

Anna Beer is a researcher in the Faculty of Computer Science, specializing in data mining and machine learning with a focus on density-based clustering, spectral clustering, and interactive clustering frameworks. She holds a BSc and MSc in computer science and maintains an ORCID profile (https://orcid.org/0000-0002-6890-997X) for her research contributions. Research Themes: Development of clustering algorithms (e.g., DISCO, Scar, LUCKe), fairness in density-based clustering (FairDen), and applications to molecular dynamics and climate research (DROPP). Collaborations: Works with colleagues like Ira Assent, Christian Plant, and Lars Krieger, with recent contributions to conferences like ICLR 2025. Activities: Presented research on density-connectivity distance at a 2023 oral contribution. Publications: 9 publications since 2019, including 3 in 2025 and 6 in 2024, covering topics from cluster evaluation to deep active learning strategies.

• Machine Learning
• Statistical Computing
• Cluster Analysis
• +2 more

Theresa Scharl-Hirsch is a Senior Scientist and Deputy Scientific Director at the Core Facility Bioinformatics, University of Natural Resources and Life Sciences, Vienna (BOKU). She holds concurrent appointments at the Institute of Statistics, BOKU, and has extensive experience in bioprocess modeling, machine learning, and statistical computing. Her work bridges biochemical engineering with advanced data science methodologies. Her research focuses on real-time monitoring of biopharmaceutical processes, clustering of high-dimensional data (particularly RNA sequencing), and application of explainable machine learning techniques. She has developed statistical models for process optimization and quality prediction in antibody capture and protein purification, with a strong emphasis on industrial implementations using R programming. Key trends in her publications include three-way data analysis, matrix-variate Gaussian mixture models, and permutation-based variable importance methods for deep learning architectures. Her work spans bioprocess engineering, bioinformatics, and industrial data science applications.