جست‌وجوی استاد و پژوهشگر

• Applied Mathematics
• Computational Physics
• Scientific Machine Learning
• +۱۰ مورد دیگر

Lukas Exl is a Senior Lecturer at the University of Vienna and a Research Director at the Wolfgang Pauli Institute (WPI), where he leads the Mathematical AI/ML Research Division. He holds a habilitation (venia docendi) in Computational Science from the University of Vienna, the first in this interdisciplinary field. Research Platform MMM Mathematics-Magnetism-Materials Wolfgang Pauli Institute (WPI), Vienna His research integrates Applied Mathematics, Computational Physics, and Scientific Machine Learning, focusing on numerical methods for PDE-based simulations, data-driven modeling, and reduced-order approaches. Key applications include computational micromagnetism for green energy materials and developing physics-informed neural networks (PINNs) with interpretable architectures. Recent publications emphasize machine learning techniques for magnetic material optimization, stray field computation, and trustworthy AI (TAI/XAI). He supervises students in Computational Science, Applied Mathematics, and Physics, with a focus on Extreme Learning Machines (ELMs), PINNs, and tensor decomposition methods. Data-driven Reduced Order Approaches for Micromagnetism (FWF Project, €484k, 2024-2028) Design of Nanocomposite Magnets by Machine Learning (FWF Project, €254k, 2022-2027) Reduced Order Approaches for Micromagnetics (FWF Project, €402k, 2018-2024) His team includes researchers like Dr. Sebastian Schaffer (PhD graduate), Kein Gjordeni, and Caroline Maitz. Collaborations span Danube University Krems and Technical University of Denmark's Energy Conversion and Storage department.

Lukas Steinbergerمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Differential Privacy
• High-dimensional Data Analysis
• +۳ مورد دیگر

Lukas Steinberger serves as Associate Professor for Statistical Machine Learning in the Department of Statistics and Operations Research at the University of Vienna's Faculty of Business, Economics and Statistics. He is also affiliated with the university's Data Science research network. Prior to his current position, he was a post-doctoral researcher with Angelika Rohde at the University of Freiburg and held a temporary Full Professorship in the Department of Mathematics at the Technical University of Munich. Steinberger's research program centers on statistical inference under differential privacy, high-dimensional data analysis, and uncertainty quantification in statistical learning. His work bridges theoretical statistics with practical machine learning applications, particularly focusing on how to perform valid statistical inference while preserving data privacy. His research has significant implications for fields requiring sensitive data analysis including healthcare, finance, and social sciences where privacy constraints are paramount. His publication record demonstrates consistent contributions to top-tier statistics journals including multiple papers in the Annals of Statistics. His recent work shows a clear trajectory toward developing theoretically sound methods for privacy-preserving data analysis that maintain statistical efficiency. The research themes across his publications reveal strong connections between differential privacy mechanisms, high-dimensional statistical theory, and practical machine learning applications. Scientific Awards: Award for dissertations in the field of theoretical statistics (2016) Steinberger maintains an active research agenda with numerous conference invitations and seminar presentations internationally, reflecting his standing in the statistical community. His current research project "Classification - Preprocessed and high-dimensional data" (2021-2025) indicates ongoing funding and research direction. He serves as Vice-Director of Studies for the Business, Economics and Statistics program and teaches courses including Statistics 2, Computational Statistics, and Classification, Clustering and Discrimination at the master's level. As part of the Data Science research network at University of Vienna, Steinberger collaborates with interdisciplinary teams working at the intersection of statistics, computer science, and domain-specific applications. His research group focuses on developing theoretically grounded approaches to statistical machine learning problems under privacy constraints.

Dustin J. Pennمشاهده پروفایل

استاد مدعو

• sexual selection
• communication
• behavioral biology
• +۴ مورد دیگر

Dustin J. Penn is an Adjunct Professor at the Faculty of Life Sciences, Center for Organismal Systems Biology, University of Vienna, and a Senior Scientist and former Director (until 2014) of the Konrad-Lorenz-Institute of Ethology at the University of Veterinary Medicine Vienna. His research program focuses on sexual selection, communication, and behavioral biology, primarily using house mice as a model system. Dr. Penn's research interests include: Sexual selection and complex courtship signals Chemical communication through major urinary proteins (MUPs) Ultrasonic vocalizations in mice Evolutionary biology and host-pathogen interactions Darwinian medicine applications Conservation biology integration His laboratory investigates various aspects of animal communication, including individual recognition mechanisms, honest signaling principles, and neural integration of olfactory and acoustic cues that govern mating preferences. Current research projects include studies on adaptive functions of ultrasonic vocalizations, MUPs in chemical communication, and evolution of honest signaling, supported by grants from the Austrian Science Foundation (FWF) and Human Frontier Science Program. Dr. Penn's recent publications demonstrate a strong focus on mouse communication systems, inbreeding effects on reproductive traits, and signaling theory. His work shows how courtship vocalizations correlate with copulatory success, how chemical signals function in mate selection, and how trade-offs maintain honest signaling in animal communication systems. As an educator, Dr. Penn teaches courses on Darwinian Medicine, leads the institute's Journal Club on behavioral ecology topics, and offers practical courses in Experimental Design and Behavioral Ecology. He has also contributed to science communication through his 'Evolution Matters' guide for the European Society for Evolutionary Biology.

• Cryptocurrency
• Blockchain
• Ethereum
• +۳ مورد دیگر

Aljosha Judmayer is affiliated with the Faculty of Computer Science and actively contributes to the Security and Privacy and Theory and Applications of Algorithms research groups. His work spans blockchain technology, cryptocurrency, and privacy mechanisms in software systems. Research Interests : Aljosha specializes in blockchain security, smart contract vulnerabilities, and interdisciplinary challenges between legal frameworks and decentralized technologies. His focus areas include privacy protocols, cryptocurrency economics, and secure messaging systems. Projects and Collaborations : He is part of the Christian Doppler Laboratory for Improving Security of Information Processes in Production Systems (CDL-SQI), collaborating with researchers like Weippl, Ullrich, and Stifter on blockchain-based security solutions. Recent Publications : His 2025 work on WhatsApp’s handshake mechanism addresses privacy risks in encrypted communication. In 2023, he explored miner extractable value in blockchain transactions and contributed to harmonizing blockchain terminology across disciplines. Activities : Aljosha has presented at events such as the Science to Science Blockchain Summit Mysterium and participated in panel discussions on decentralized finance (DeFi) and blockchain applications in healthcare.

• Natural Language Processing
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Pedro Henrique Luz de Araujo is a member of the Faculty of Computer Science affiliated with the Research Group Data Mining and Machine Learning . His work focuses on Natural Language Processing , Language Modeling , and Behavioral Learning , particularly in Brazilian legal text domains. He has contributed to advancements in named entity recognition , hate speech detection , and specification-driven AI systems . Active in 2022-2025 with publications in PLoS ONE and top NLP conferences Organized the KONVENS 2024 conference Research emphasizes LLM calibration , multimodal classification , and domain-specific datasets His recent publications show expertise in large language models (2025: persona effects; 2024: specification overfitting, text-guided image clustering), NER techniques (2024), and legal document processing (2023: multimodal classification; 2020: VICTOR dataset, LeNER-Br).

• Education
• Didactics
• Entertainment Computing

Tatjana Slavova is a researcher at the Faculty of Computer Science, affiliated with the Research Group Education, Didactics, and Entertainment Computing. Her work focuses on interdisciplinary research at the intersection of pedagogy and computational technologies. Research Interests: The Research Group Education, Didactics, and Entertainment Computing suggests expertise in educational methodologies, didactic frameworks, and the application of entertainment technologies in computational contexts. This aligns with broader interests in pedagogical innovation and technology-enhanced learning experiences.

• Computer Science
• Data Mining
• Machine Learning
• +۳ مورد دیگر

Andrii Shkabrii is a researcher affiliated with the Faculty of Computer Science, actively contributing to fields such as Data Mining, Machine Learning, and Clustering. His work bridges computational methods with archaeological data analysis, particularly in historical time studies. Education: MSc in Computer Science Research Interests: Specializing in centroid-based deep clustering algorithms and non-redundant image clustering techniques, Shkabrii's work impacts both theoretical computer science and applied archaeology. He explores clustering quality metrics and domain expertise integration. Publications (2023-2024): Recent work includes advancements in reclustering barriers (2024) and medieval glass bead analysis (2023), reflecting interdisciplinary applications of machine learning.

Nils Morten Kriegeمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning with Graphs
• Combinatorial Optimization
• +۵ مورد دیگر

Dr. Nils Morten Kriege is an Associate Professor at the Faculty of Computer Science, University of Vienna, where he leads the Data Mining and Machine Learning research group. Previously, he served as Assistant Professor (2020-2023) at the same institution and held positions at TU Dortmund including Interim Professor (2019/2020) and Postdoctoral Researcher (2015-2020). His research focuses on graph-based machine learning methods with applications in cheminformatics and drug discovery. His educational background includes a Doctorate in Computer Science (2015) and Diploma in Computer Science (2009), both from TU Dortmund. He has also been a Visiting Researcher at the University of York, UK. Dr. Kriege's research centers on graph algorithms and machine learning with graphs, particularly focusing on graph neural networks, graph kernels, and their applications in cheminformatics and drug discovery. His work bridges theoretical computer science with practical applications, developing novel methods for graph similarity, graph classification, and network analysis. He has made significant contributions to understanding the expressivity and robustness of graph neural networks, as well as developing efficient algorithms for graph similarity search and molecular analysis. His recent publications (2023-2025) demonstrate a strong focus on graph neural networks, with particular attention to their expressivity, robustness against attacks, and practical applications in drug discovery. His work spans theoretical foundations (Weisfeiler-Leman hierarchy, graph isomorphism testing), practical implementations (efficient quantization, defense frameworks), and domain-specific applications (cheminformatics, drug discovery). Vienna Research Groups for Young Investigators (2019) - €1,466k funding for "Algorithmic Data Science for Computational Drug Discovery" Member of the Global Young Faculty V, Stiftung Mercator (2017) Dr. Kriege leads an independent research group funded through the Vienna Research Groups for Young Investigators program, focusing on computational drug discovery. He has served on program committees for major conferences including NeurIPS, ICML, IJCAI, AAAI, ICLR, and ICDM, and has reviewed for prestigious journals such as Transactions on Pattern Analysis and Machine Intelligence. His teaching portfolio includes courses on Data Mining, Graph Learning, and Introduction to Machine Learning. He leads the Machine Learning with Graphs work group within the Data Mining and Machine Learning Research Group at the University of Vienna, collaborating with researchers like Wilfried Gansterer and Petra Mutzel on graph-based methods for drug design and molecular analysis.

Stefan Balkeمشاهده پروفایل

پژوهشگر

• Music Information Retrieval
• Deep Learning
• Jazz Music Analysis
• +۳ مورد دیگر

Stefan Balke is a Researcher at the AudioLabs Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), focusing on wind music research and Music Information Retrieval (MIR). Previously, he held a PostDoc position at the Institute of Computational Perception, JKU Linz (2018–2019) and later worked as a Data Scientist and Team Lead in industry. He temporarily served as a professor at Hochschule Weserbergland in 2023/24. Education: PhD (Dr.-Ing.) in MIR from FAU (2018), Electrical Engineering studies at Leibniz Universität Hannover (2008–2013). Research Interests: Music Information Retrieval, Deep Learning, Jazz and Wind Music Analysis, Dataset Development. His work includes creating datasets like ChoraleBricks (wind music) and JSD (jazz structure analysis), and tools like trackswitch.js for audio visualization. Grants & Projects: Secured 35k€ funding for his orchestra through the 'Engagiertes Land' program (2024). Collaborated on exhibits such as 'The Listening Machine' (Ars Electronica) and 'Con Espressione!' (mathematics of music exhibition). Contributions: Active on GitHub with repositories for datasets (choralebricks, jsd) and tools. Co-developed web-based audio tools and contributed to open-source projects like Sonic Visualiser and librosa.

Laszlo Böszörmenyiمشاهده پروفایل

استاد

• Distributed Multimedia Systems
• Self-Organizing Content Delivery
• Video Content Analysis
• +۲ مورد دیگر

Laszlo Böszörmenyi is Full Professor of Computer Science and Head of the Institute of Information Technology at Alpen-Adria University Klagenfurt, Austria. His research specializes in distributed multimedia systems with emphasis on self-organizing architectures and intelligent video content analysis. Research focuses on: Self-organizing content delivery networks Interactive video browsing and exploration Medical video analysis (endoscopic and surgical) Video content summarization and retrieval Distributed systems for multimedia applications He leads several research projects in medical multimedia and traffic surveillance systems. Dr. Böszörmenyi has published extensively on video analysis techniques, particularly for medical applications including: Surgical phase recognition in laparoscopic videos Endoscopic video compression and archiving Instrument detection in surgical workflows Specialized browsers for medical video exploration He serves as Associate Editor for Multimedia Systems Journal and participates in numerous program committees for multimedia conferences.

Christoph Kühbergerمشاهده پروفایل

استاد

• History Didactics
• Political Education
• Historical Culture
• +۵ مورد دیگر

Christoph Kühberger is a University Professor of History and Political Education at the Department of History, Faculty of Cultural Studies, University of Salzburg, where he has been employed since October 1, 2017. Previously, he served as Professor of History and Political Science Didactics at the Salzburg University of Education (2014-2017) and as Vice Rector for Social and Human Sciences at the same institution (2009-2017). His academic journey includes positions at the University of Hildesheim, University of Greifswald, and research assistant roles at the University of Salzburg. Kühberger's research focuses on theory and didactics of history, historical-political and political education, empirical political and history didactics, subject-oriented history didactics, ethnographic history didactics, and decolonization of historical knowledge. His work explores historical learning in informal settings, particularly in children's private spaces, and examines how historical culture manifests in everyday life through material objects, toys, and games. He has made significant contributions to understanding historical consciousness in diverse contexts, including Hawaiian history education and decolonial approaches to history teaching. His recent publications reveal a strong emphasis on ethnographic approaches to history didactics, with special attention to children's historical culture in domestic settings, material culture studies, and decolonial perspectives on historical knowledge. Kühberger's work bridges traditional historical research with innovative methodological approaches, including digital humanities applications in history education. His research demonstrates a consistent interest in how historical understanding is formed outside formal educational settings and how this informs classroom practice. Kühberger is actively involved in multiple research projects including 'The epistemic struggle with the unwritten past' (focusing on Hawaiian teachers' approaches to Ancient Hawaiian History), 'Historical Culture in the Children's Room,' and 'Playing with History' (examining toys and games as historical representations). He serves as Deputy Chairman of the Conference for History Didactics and is engaged with the Doctoral School PLUS 'Competence Research in Teacher Education.' His international scholarly activities include upcoming lectures at the University of Auckland (February 2025), University of Mainz (September 2024), and University of Vienna (December 2024), reflecting his growing international recognition in the field of history didactics and historical culture studies. Kühberger's research demonstrates a commitment to bridging theoretical frameworks with practical applications in history education across diverse cultural contexts.

• Algebraic Geometry
• Symbolic Computation
• Differential Algebra
• +۵ مورد دیگر

Guenter Landsmann is a Researcher at the Research Institute for Symbolic Computation (RISC) of Johannes Kepler University in Linz, Austria. His primary research focuses on Algebraic Geometry and Symbolic Computation, with significant contributions to Gröbner bases, polynomial functions, and differential algebra. His work bridges theoretical mathematics and computational methods, addressing challenges in algebraic structures, geometric modeling, and topological properties. His research interests include Gröbner Reduction techniques, parametrization of algebraic varieties and canal surfaces, and the application of symbolic computation to problems in differential equations and topology. Notable contributions include studies on polynomial functions over GL(2,R), implicitization of algebraic varieties, and the development of algorithms for dimension computation in filtered free modules. Publications span ISSAC conferences, technical reports, and journals like Mathematical and Computer Modelling of Dynamical Systems. His work demonstrates a sustained focus on advancing computational tools for algebraic geometry and their applications in geometric modeling and abstract algebra. While no scientific awards are explicitly listed, his extensive publication record reflects a dedicated academic career in symbolic computation and related fields. His affiliation with RISC positions him at the forefront of interdisciplinary research combining mathematics and computer science.

• Biomass gasification
• Fluidized bed technology
• Synthetic natural gas production
• +۵ مورد دیگر

Florian Benedikt is a researcher at the Institute of Chemical Engineering, Faculty of Technical Chemistry at Vienna University of Technology (TU Wien). He is affiliated with the Research Group Industrial Plant Design and Application of Digital Methods (E166-07-2) and holds the academic credentials Dipl.-Ing. Dr.techn. BSc. His research focuses on advanced biomass conversion technologies with particular emphasis on dual fluidized bed gasification systems. His research interests span biomass gasification, synthetic natural gas production, hydrogen technologies, and digital twin applications for energy plants. Dr. Benedikt's work addresses critical challenges in renewable energy production, including fuel flexibility, gas cleaning processes, and integration of biomass-based systems into existing energy infrastructure. His research combines experimental work with process modeling to develop efficient and sustainable energy conversion pathways. Analysis of his recent publications reveals a strong focus on advancing dual fluidized bed steam gasification technology from pilot to demonstration scale, with particular attention to process optimization, digitalization, and environmental performance. His work bridges fundamental research with practical implementation, as evidenced by the 1 MW demonstration plant project and multiple techno-economic assessments. Dr. Benedikt actively supervises graduate students working on related topics including pressure swing adsorption, fluidized bed gasifiers, and gas cleaning technologies. His collaborative research network includes prominent researchers at TU Wien such as Hermann Hofbauer and Stefan Müller, with whom he has co-authored numerous publications.

• Statistics
• Operations Research
• Data Analysis
• +۳ مورد دیگر

Florian Frommlet holds positions in two faculties: the Faculty of Business, Economics and Statistics (Department of Statistics and Operations Research) and the Faculty of Computer Science (Research Group Visualization and Data Analysis). His academic rank is Senior Lecturer. His research focuses on statistical methodologies, optimization, and computational statistics, with applications in genomics and data analysis. Key research interests include statistical properties, high-dimensional data analysis, and genome-wide association studies. He has contributed to projects such as Optimal selection procedures in genome wide association studies (2010–2014), funded by research grants. Notable publications span topics like multiple testing procedures and semidefinite programming bounds in combinatorial optimization. No academic awards or formal advisees are listed. His interdisciplinary work bridges statistics, computer science, and genomics, reflecting his dual affiliation across faculties.

Christoph Finkمشاهده پروفایل

پژوهشگر ارشد

• Social Geography
• Critical Cartography
• Urban Ethnography
• +۸ مورد دیگر

Christoph Fink is an MSCA Postdoctoral Fellow at the Department of Social and Cultural Anthropology in the Faculty of Social Sciences at the University of Vienna . He holds a PhD in Interdisciplinary Sustainability Science from the University of Helsinki and a Master's degree in Geography (Cartography and Geoinformation) from the University of Vienna. Prior to his current position, he worked as a postdoctoral researcher at the Digital Geography Lab at the University of Helsinki. Research Interests span social geography , critical cartography , urban ethnography , and data privacy in conservation . His work bridges quantitative and qualitative methods, focusing on urban activists' role dynamics , digital urban twins , and using social media data for biodiversity conservation . Recent projects include 15-minute city accessibility studies and development of the R⁵py Python library for multimodal transport analysis. Scientific Awards include the Marie Skłodowska Curie Action Postdoctoral Fellowship . His publications demonstrate expertise in urban sustainability , digital conservation , and critical cartographic practices . He contributes to urban mobility research through the URBANAGE Horizon 2020 project and co-convenes the Wednesday Seminar series at the Department of Social and Cultural Anthropology. His methodological toolkit combines computational linguistics , social network analysis , and visual anthropology techniques.