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Gertrud HuberView profile

Part-Time Lecturer

• Alpine folk music
• Ethnomusicology
• Cultural memory
• +3 more

Gertrud Huber is a German zither player, harpist, and educator specializing in Alpine folk music traditions. She holds a Ph.D. from the Institute for Folk Music Research and Ethnomusicology at the University of Music and Performing Arts Vienna (2014) and serves as a Part-Time Lecturer in zither pedagogy at the Federal Academy for Musical Youth Education in Trossingen . Her career spans solo performances, ensemble collaborations, and cross-cultural research. Her research interests focus on: Alpine folk music preservation Zither's role in cultural memory Migrant identity formation Gender dynamics in zither education Historical evolution of plucked string instruments Intercultural musical dialogues Recent publications analyze: Zither traditions across Europe (2014) Gender perceptions in zither teaching (2012) Intercultural instrument adaptations (2013) Historical documentation of zither musicians (2010) Scientific awards include: First national prize at 'Jugend musiziert' (1981) Youth award at 'Zwiesler Fink' (1982) Continued juror role at international zither competitions

• Artificial Intelligence
• Machine Learning
• Healthcare Informatics
• +6 more

Oliver Krauss serves as an Assistant Professor at FH Upper Austria (University of Applied Sciences Upper Austria) at the Hagenberg Campus. He is affiliated with multiple research centers including the Research Center Hagenberg, Web Intelligence and Innovation Laboratory, AIST Assistive Technology Lab, Center of Excellence Medical Technology/TIMed Center, Center of Excellence for Smart Production, and Digital Media Lab. His work falls under the Strength area ICT - Information & Communication Technology with additional connections to HEAL initiatives. Dr. Krauss's research spans several interconnected domains with a strong focus on practical applications of artificial intelligence. His primary research interests include Healthcare Informatics (particularly HL7 FHIR standards), Language Models and Natural Language Processing, Web Intelligence for user behavior analysis, Assistive Technology applications, and Process Mining for business optimization. His work demonstrates consistent interdisciplinary connections between computer science fundamentals and real-world healthcare and business applications. Analysis of Krauss's recent publications (2023-2025) reveals a clear trajectory toward increasingly sophisticated AI applications. His research has evolved from foundational IoT and sensor data processing toward advanced language model applications in healthcare informatics, e-commerce personalization, and fact-checking systems. A notable pattern is his ability to bridge theoretical AI concepts with practical implementations in medical technology and business intelligence, often incorporating emerging technologies like GPT-3 and other large language models into domain-specific solutions. Dr. Krauss has received significant recognition for his work, including: FH Upper Austria Young Researcher Award (2019 and 2022) HL7 Austria Student Award for Master's Thesis (2015) Best Paper Award at Intelligent Systems and Applications 2022 His research activities include substantial project leadership and collaboration, with notable projects including MARIE (Mobile Robotic Assistance for Office Environments), CRISAM® Loop (AI-based risk management), and EDEN (Emergency Detection for Elevator Networks). He has maintained consistent publication output with 74 publications to date and has been active in scientific communities through conference participation, invited lectures (including on HL7 FHIR topics), and committee work. His h-index of 6 reflects a solid research impact with 110 citations. Dr. Krauss leads research within multiple specialized laboratories including the Web Intelligence and Innovation Laboratory where he applies language models to website visitor segmentation, the AIST Assistive Technology Lab focusing on healthcare applications, and contributes to the Center of Excellence Medical Technology/TIMed Center where his work on patient medical records analysis has significant clinical implications.

• Information Retrieval
• Knowledge Graphs
• Natural Language Processing
• +5 more

Florina Piroi is a Senior Scientist (Research Fellow) at the Center for Research Data Management (E058-06) within the Faculty of Informatics at TU Wien. Her research integrates information retrieval, knowledge graphs, and semantic technologies to advance exploratory search systems and research data infrastructure. Her core research interests include: Information Retrieval evaluation and validation methodologies Knowledge graph applications for domain-specific search Patent text mining and semantic technologies Medical concept normalization and entity linking Argumentative zoning in scientific documents Table extraction and classification systems Analysis of her recent publications reveals strong trends in applying machine learning to specialized IR tasks, particularly in patent analysis and medical text processing. Her work emphasizes robust evaluation frameworks and cross-lingual capabilities, with consistent contributions to the CLEF campaign and PatentSemTech workshops. Dr. Piroi actively collaborates with international researchers through the Network Lab at TU Wien and contributes to the university's Center for Research Data Management, focusing on semantic technologies for research data infrastructure and scholarly communication systems.

• Machine Learning
• Computer Vision
• Trustworthy AI
• +5 more

Christoph Lampert is a Professor at the Institute of Science and Technology Austria (ISTA), where he leads the Machine Learning and Computer Vision research group. His primary affiliations are with ISTA, though no specific school or department is explicitly mentioned in available sources. His research spans machine learning fundamentals, computer vision, and emerging areas of trustworthy AI including privacy, fairness, robustness, and federated learning systems. Lampert's recent work demonstrates strong focus on theoretical and applied aspects of differential privacy, neural collapse phenomena, federated learning architectures, and verification methods for neural networks. His group consistently publishes at top venues like NeurIPS, ICML, and ICLR, with recent explorations in privacy-preserving ML, multi-task learning theory, and hardware-algorithm co-design. He has received significant recognition including the 2025 DARPA Disruptive Ideas award and ELLIS Fellowship. As ELLIS Unit Director at ISTA, he coordinates pan-European AI research initiatives. His editorial contributions include leadership roles at Journal of Machine Learning Research and IEEE TPAMI . Lampert actively advises graduate researchers, with recent PhD graduates including Bernd Prach (robust image classification) and Alex Peste (robustness/fairness). Current research groups explore federated learning, neural verification, and privacy-preserving algorithms.

Himal TrikhaView profile

Researcher

• Jainism
• Indian Epistemology
• Sanskrit Philology
• +5 more

Himal Trikha is a researcher at the University of Vienna's Department of South Asian, Tibetan and Buddhist Studies . He specializes in Jaina philosophy, focusing on intertextual interactions between Jainas and other Indian thought systems, particularly the Vaiśeṣika tradition. His work examines the epistemological model of anekāntavāda (manifoldness) and its critical applications in classical Sanskrit texts. His research encompasses the analysis of Jaina philosophical texts, including Vidyānandin's Satyaśāsanaparīkṣā , using philological methods to study argumentative structures and intertextual elements. He created the Digital Corpus of Vidyānandin's Works and contributes to the Biobibliography of Digambara Philosophers . His 15 most recent publications span Jaina epistemology, intertextuality in Sanskrit works, and comparative Indian philosophy. Key themes include falsification strategies, perspectivism, and the sociological implications of doctrinal debates. Trikha's work has been presented at conferences in Vienna, Münster, and SOAS, with a particular emphasis on rational discourse models in pluralistic epistemologies. Current teaching includes courses on Indian philosophy such as Alternative Ontologies in Indian Philosophy and Basic Notions of Indian Philosophy , reflecting his expertise in classical and medieval Sanskrit philosophical traditions.

• Physical Geography
• Glaciology
• Geomorphology
• +3 more

Stefan Haselberger is a Lecturer at the Department of Geography and Regional Research, University of Vienna. His teaching focuses on Physical Geography, including courses on field methods, landscape dynamics, and watercourse management. His research interests center on glacial geomorphology, alpine ecosystems, and the application of remote sensing technologies like drones to study vegetation and biomass in non-forest environments. His work explores topics such as deglaciation processes in alpine regions, biogeomorphic interactions between plant communities and sediment dynamics, and the role of microbial communities in stabilizing proglacial slopes. He has conducted field studies in locations like the Kaunertal Valley and Gepatschferner glacier forelands in Austria, and Jotunheimen in Norway. Recent publications highlight his focus on drone-based biomass estimation, vegetation succession in glacier forelands, and climatic implications of glacial retreat. His research integrates field measurements, remote sensing, and experimental approaches to understand environmental changes in mountainous regions. No scientific awards were explicitly mentioned in the provided texts. His advising and grants information is not detailed here, but his active teaching and research roles indicate involvement in academic mentorship and project leadership.

Elisabeth LexView profile

Associate Professor

• Recommender Systems
• User Modeling
• Information Retrieval
• +3 more

Assoc. Prof. Dr. Elisabeth Lex is a tenured Associate Professor at Graz University of Technology (TUG) and PI of the Recommender Systems and Social Computing Lab. She serves as Dean of Study for the master's program in Computational Social Systems. With a habilitation in Applied Computer Science, her research focuses on psychology-informed recommender systems, user modeling, and computational social science. She has published over 100 articles in venues like WebConf, HT, and RecSys, and led major projects like the trans-university 'Polarization in Public Opinion.' Education : PhD in Computer Science (2011), TU Graz Postdoc at Universidad Nacional de San Luis (Argentina) and RWTH Aachen (Germany) Research Interests : Her work integrates cognitive science principles into AI systems, addressing fairness, privacy, and transparency in recommendations. She explores how human behavior and social dynamics influence algorithmic systems, with applications in music recommendation, misinformation detection, and polarization analysis. Article Trends : Recent work emphasizes trustworthy AI, including privacy-aware recommendation systems (e.g., differential privacy), bias mitigation in music recommendations, and computational framing analysis for political narratives. She bridges technical innovation with societal impacts, such as analyzing polarization in public health policies via social media. Scientific Awards & Grants : Outstanding Paper Award (ISSI 2015) EU H2020 grants (TRIPLE, AI4EU, OpenUP) Transuniversity Research Project on Polarization (€325k funding) Advising & Labs : Supervised over 20 PhD/Master students on topics like conspiracy narratives, privacy in recommendations, and job recommendation systems. Her lab develops open-source frameworks (e.g., TagRec, AFEL-REC) and collaborates with industry partners through the Know-Center. Labs/Teams : Leads the Recommender Systems and Social Computing Lab at TUG, part of the interdisciplinary Social Computing area at the Know-Center. Collaborates with global initiatives like OpenAire and Open Knowledge Maps.

• Numismatics
• Ancient History
• Archaeology
• +3 more

Dr. Ludovic Jouvet is a Postdoctoral Fellow in the Research Group »Numismatics« at the Austrian Academy of Sciences (OEAW) in Vienna. His current position is affiliated with the Dominikanerbastei 16 facility in Vienna. He is actively involved in the project titled »Ordering the World in Coins: The Numismatic Networks of Fröhlich, Khell and Eckhel and their Systems of Classification for Ancient Coins«, focusing on historical numismatic methodologies and intellectual networks. Research Focus: Examination of 18th-19th century numismatic classification systems and their socio-intellectual contexts, particularly analyzing the works of Fröhlich, Khell, and Eckhel. Affiliations: Austrian Academy of Sciences, associated with the numismatic research team and guest researchers network. No specific academic awards or published articles are listed in the provided text. His work bridges historical research practices with material culture analysis, emphasizing the role of coin classification in early modern scholarly networks.

• Material Science
• Cultural Heritage Conservation
• Analytical Chemistry
• +3 more

Rita Wiesinger is a Lecturer at the Department of Natural Sciences and Technology in the Art at the Vienna Academy of Fine Arts. Her research focuses on material science applications in art conservation, particularly analyzing polymer degradation, metal corrosion (e.g., silver), and pigment behavior under environmental stressors. She employs advanced analytical techniques like LIBS, LA-ICP-MS, XRD, and Raman spectroscopy to study material composition and stability. Her work spans polymer films' response to pollutants, pigment interactions in paints, and corrosion processes in cultural heritage materials. Recent studies highlight the combined use of spectroscopic methods for material characterization, emphasizing their utility in understanding degradation mechanisms and informing conservation strategies. No scientific awards or grants are explicitly noted in the provided texts. She teaches courses such as 'Materialbeständigkeit in der Kunst I,' contributing to education in material durability for art conservation.

• Recommender Systems
• User Modeling
• Generative AI
• +4 more

Thomas Elmar Kolb is a PreDoc Researcher at Technische Universität Wien , affiliated with the Data Science group (E194-04) and the Curriculum Commission for Business Informatics . His research focuses on Recommender Systems , User Modeling , and Generative AI , emphasizing fairness, bias, and cross-domain applications. Recent research trends in his work include: Advancing fairness metrics in news recommendation systems Integrating Large Language Models (LLMs) for cross-domain recommendation Developing Austrian language polarity resources (ALPIN Dictionary) Exploring the interplay between trust and serendipity in user experiences Analyzing user roles in online forums for behavioral modeling Supervisions include diploma theses on group fairness, podcast recommendation systems, and populism detection. Key projects involve the 2022-2028 CDL-RecSys initiative and the 2020-2021 DYSEN project on dynamic sentiment analysis.

• Natural Language Processing
• Computational Linguistics
• Information Extraction
• +3 more

Gábor Recski is a computational linguist and postdoctoral researcher at TU Wien's Faculty of Informatics, affiliated with the Data Science Research Unit. He holds a PhD and previously served as an assistant professor at the Budapest University of Technology. His research focuses on natural language processing (NLP), particularly computational semantics and transparent information extraction. He teaches courses such as Natural Language Processing and Information Extraction, and supervises students at BSc, MSc, and PhD levels. His work includes over 50 peer-reviewed publications and projects like BRISE-Vienna (2019–2025) and Honeypot LLM: Creation of a Scam Conversation Dataset. Roles: PostDoc Researcher, Substitute Member of Faculty Council, Substitute Member of Curriculum Commission for Business Informatics Education: PhD (previous role at Budapest University of Technology) Research Interests: Transparent information extraction, semantic modeling, legal text analysis, and explainable AI His research projects span legal corpus construction (e.g., BRISE-plandok), offensive text detection, and AI applications in legal research. He actively contributes to conferences like ACL, EMNLP, and SIGIR. Supervised students have explored topics ranging from explainable prediction models to graph-based relation extraction.

• Audio Signal Processing
• Music Information Retrieval
• Deep Learning
• +3 more

Alexander Schindler is an External Lecturer at the Department of Information Systems Engineering at Technische Universität Wien. His research focuses on audio signal processing, music information retrieval, and deep learning applications in multimodal analysis. He coordinates the approacH project (2010–2013) funded by the European Commission, exploring audio-visual search engines. His academic background includes a Dipl.-Ing. in Technical Engineering and a Dr.techn. from TU Wien. Key research areas include acoustic scene classification, deepfake detection, and music video analysis. He has supervised students on topics like bird song identification and machine outage prediction. Notable publications span from unsupervised cross-modal learning (2020) to multi-modal MIR frameworks (2019). He holds a Bakk.techn. and advanced technical qualifications. Recent work includes advancements in deepfake audio detection (2025) and audio-visual surveillance systems (2024). His projects bridge theoretical research with practical applications in forensic analysis and industrial predictive maintenance. He contributes to international conferences like ACM SAC and DCASE, focusing on neural network architectures for audio analysis.

• Information Retrieval
• Data Mining
• Text Classification
• +5 more

Allan Hanbury is a Full Professor for Data Intelligence and Head of the Data Science Research Unit at the Faculty of Informatics, TU Wien, Austria. He serves as Faculty Representative responsible for financial affairs and internationalization within the faculty. He is also a faculty member at the Complexity Science Hub Vienna. Roles: Faculty Representative (financial/internationalization), Data Science Research Unit Head Key Projects: Coordinator of EU-funded Khresmoi (medical search), DoSSIER (Marie Curie ITN training network), VISCERAL (big data evaluation), KConnect (medical text analysis) Spin-off: Co-founder of contextflow (radiology image search) Research focuses on information systems engineering and visual computing, with emphasis on: Information retrieval, data mining, text classification Medical data intelligence, health informatics Domain-specific systems for information extraction/retrieval Publications span over 180 refereed works, with recent contributions on LLMs in query generation, clinical trial matching, and systematic review automation. His work bridges academic research with industry applications in healthcare, legal text analysis, and patent mining. Grants include EU Horizon projects (e.g., DoSSIER, BRISE-Vienna), FFG-funded innovation initiatives, and collaborations with organizations like Siemens, Deutsche Telekom, and the Austrian Ministry of Finance.

• Selective Sampling
• Text Classification and Web Mining
• Data Mining in the Social Sciences
• +6 more

Johannes Fürnkranz serves as a Professor at the Faculty of Informatics, TU Wien, affiliated with the Distributed Systems research group (Institute E194-02). His institutional presence is evidenced by current contact details and recent scholarly output. His research spans critical machine learning domains including Selective Sampling, Text Classification and Web Mining, Data Mining in the Social Sciences, Inductive Logic Programming, and Noise Handling. Specialized interests cover Machine Learning in Game Playing, Pairwise Classification and Preference Learning, alongside Meta-Learning and Ensemble Classifiers, reflecting both theoretical depth and applied versatility in artificial intelligence. Current publication trends indicate active work in change point detection for heterogeneous data systems, combining predictive modeling with anomaly detection in time series. This aligns with broader themes in statistical learning and non-parametric methods for complex data structures. No scientific awards were documented in the source material. Advising activities and research funding details remain unspecified in available information. Laboratory affiliations center on the Distributed Systems group within TU Wien's Faculty of Informatics infrastructure.

• Data Science
• Machine Learning
• Sentiment Analysis
• +4 more

Bettina Manuela Johanna Kern is a PreDoc Researcher at the Department of Data Science, Vienna University of Technology (TU Wien). Holding a BSc and MSc in Informatics, she is currently pursuing her PhD while contributing to research in data science and machine learning. Her work spans interdisciplinary domains including education technology, sentiment analysis, and FAIR data principles. Education: BSc and MSc in Informatics Affiliation: Department of Data Science, TU Wien Key projects: FAIR-AI (2024–2026), HumRec (2021–2025), DYSEN (2020–2021) Her research focuses on reproducibility in machine learning , sentiment analysis for Austrian German dialects , and trust in data infrastructures . She develops resources like the ALPIN Sentiment Dictionary and investigates methodological challenges in active-assisted living technologies. Her work bridges computational methods with social science applications. Recent publications (2024) highlight her contributions to reproducibility frameworks and student dropout prediction models. Earlier work (2022–2021) covers sentiment resource development and ethical evaluation of aging technologies. Contact: bettina.kern@tuwien.ac.at | ORCID | Personal Website