جستجوی استاد راهنمای دانشگاهی

• Project management
• Organizational psychology
• Educational psychology
• +۷ مورد دیگر

Karin Lackner is an Assistant Professor (retired) affiliated with the Institute for Teaching and School Development at the University of Klagenfurt. Her work focuses on educational and organizational psychology, project management, and intercultural dynamics within institutional settings. Research Areas: Organizational and educational psychology Healthcare management Team development strategies Sociodynamic design Sports research Intercultural project management Her scholarly contributions span interdisciplinary research methodologies and practical applications in educational systems. Recent work emphasizes innovation in teaching methodologies and institutional frameworks. Publications include analyses of digital media behavior ( Political Microtargeting Avoidance ), robotics applications ( Swarmalator Systems ), and sustainable business models ( Entrepreneurial Ecosystems ). These reflect her interdisciplinary approach bridging social sciences and technical fields. Grants & Projects: Collaborative research initiatives in education innovation and organizational dynamics (funding sources include Austrian Research Promotion Agency and Carinthian Economic Development Fund). She leads initiatives promoting inclusive educational practices and advises on institutional transformation strategies.

• Robotics
• Multi-Agent Systems
• Computer Science
• +۳ مورد دیگر

Konstantin Warneke is a Senior Researcher at the Department of Computer Science, Alpen-Adria-Universität Klagenfurt. His work focuses on robotics, multi-agent systems, and automation. While his formal teaching role at the university has concluded, he remains active in research projects such as 'Heterogeneous multi-agent localization' and 'Wind Turbine Blade Inspection Using Multimedia Drones,' funded by agencies like FFG and OeAD. His research spans radar technology, swarm intelligence, and edge-cloud computing, with recent publications in journals like Physical Review E and IEEE Sensors Journal. He collaborates on interdisciplinary projects addressing sustainability, automated systems, and media studies. Notably, his work emphasizes applied research with industry partners such as Infineon Technologies and NOI AG, contributing to practical innovations in fields like agricultural monitoring and renewable energy.

Friedrich Leischمشاهده پروفایل

استاد

• Statistical Computing
• Cluster Analysis
• Bioprocess Modeling
• +۳ مورد دیگر

Friedrich Leisch was a Professor at the Institute of Statistics, University of Natural Resources and Life Sciences, Vienna. He held leadership positions including Head of the Department of Landscape, Spatial and Infrastructure Sciences and was a core developer for the R programming language. His career included positions at Ludwig-Maximilians-Universität München and Vienna University of Technology, where he earned his doctorate and habilitation. Education includes: PhD in Applied Mathematics, Vienna University of Technology (1999) Habilitation in Statistics, Vienna University of Technology (2005) Leisch's research focused on statistical computing methodologies with applications across diverse scientific domains. His primary interests included: Development of resampling methods for cluster analysis and validation Bioprocess modeling for pharmaceutical manufacturing Causal modeling frameworks for scientific inference Statistical computing and R package development His recent publications demonstrate a strong focus on biotechnological applications of statistical learning, developing methods for real-time process monitoring in pharmaceutical production. Articles frequently address machine learning explainability, temporal clustering, and environmental impact assessment of agricultural practices. Scientific awards include: Peter Keller Award for tourism research Supervised numerous doctoral and master's students across statistics and biotechnology domains. Led multiple research projects including: Mobile Field Phenotyping for Agronomy and Breeding Statistical evaluation of downstream processing data Market Segmentation Methodology projects Collaborated extensively with bioprocessing research groups including the Austrian Centre of Industrial Biotechnology (ACIB), focusing on statistical modeling of fermentation processes and purification systems.

• Machine Learning
• Artificial Intelligence
• Medical Informatics
• +۳ مورد دیگر

Stephan Dreiseitl is a Professor at the University of Applied Sciences Campus Hagenberg, Department of Software Engineering. His research focuses on machine learning, medical informatics, and data analysis. He has contributed to advancements in feature selection, neural networks, and decision support systems. Research Interests: His work spans machine learning algorithms (e.g., neural networks, SVMs), medical applications like teledermatology, and computational methods for feature engineering. He emphasizes practical applications in healthcare and software engineering. Notable Activities: He led the PhD Programm - PhD Programme from 2011–2020 and delivered talks on topics such as Support Vector Machines and Artificial Neural Networks. Publications: His recent work includes studies on technostress measurement via HRV analysis, efficient multiclass AUC computations, and parallel minibatch training in machine learning. He also authored a textbook on Mathematics for Software Engineering. Labs/Teams: Active in the Research Center Hagenberg, contributing to interdisciplinary projects at the intersection of computer science and medical informatics.

• Opinion Mining
• Sentiment Analysis
• Evolutionary Algorithms
• +۳ مورد دیگر

Gerald Petz is Professor at the Digital Business Institute, focusing on digital transformation and computational analysis of user-generated content. His research develops methodologies for opinion mining, sentiment analysis, and evolutionary algorithms applied to business intelligence. Current projects investigate persuasive chatbot interactions, consumer response modeling, and reputation management in digital environments. He leads multiple applied research projects including SENOMWEB and OPMIN 2.0, developing tools for analyzing social media data and customer feedback. His work connects computational linguistics with business applications, particularly in service recovery and consumer psychology domains. Teaching activities include seminars on search optimization for e-commerce and implementing accessible web content management systems. He regularly presents research at international conferences on digital business transformation and computational social science.

Raffael Heissمشاهده پروفایل

استاد

• Health Communication
• Political Communication
• Digital Media Effects
• +۳ مورد دیگر

Raffael Heiss is Professor at MCI Management Center Innsbruck's Center for Social & Health Innovation and Department of Nonprofit-, Social & Health Management. He previously served as Fulbright Visiting Professor at Georgetown University and holds a PhD in Communication from the University of Vienna. His research focuses on political and health communication, digital media effects, and civic engagement. Education: PhD in Communication, University of Vienna MA in Communication & Political Science, University of Vienna BA in Political Science, University of Innsbruck His research examines how digital media shapes health behaviors, political engagement, and misinformation processing. Current projects investigate health influencers' impact on adolescents and tools for detecting reliable health information online. He employs experimental designs, panel studies, and citizen science approaches across diverse populations. Publication trends show consistent focus on health communication (38%), social media effects (32%), and misinformation (22%), with recent work emphasizing influencer communication and youth health literacy. Methodologically, 65% employ experimental designs while 28% use content analysis. Awards & Grants: Fulbright Visiting Scholar Grant (2022) Austrian Science Fund grants for health influencer research (€350k, €90k) Ars Docendi State Prize for Teaching Excellence Top Paper Awards from ICA and AEJMC Supervises 15+ graduate students annually on topics spanning health communication, social media analytics, and tourism management. Leads research collaborations with EU institutions on countering health disinformation.

• Finance
• Healthcare Systems
• Evolutionary Algorithms
• +۲ مورد دیگر

Ronald-Karl Hochreiter is a Privatdozent (Priv.-Doz.) and researcher affiliated with the Institute of Security Research. Holding a doctorate (Dr.), he has maintained active research output from 2010 to 2021 across multiple disciplines including finance, healthcare systems, and computational social science. His research demonstrates significant interdisciplinary breadth: in finance, he investigates network centrality in interbank markets and Markov chain-based credit risk modeling; in healthcare, he analyzes rheumatology specialist supply through nationwide surveys; and in computer science, he develops evolutionary algorithms to mitigate negative opinion spread in social networks. His scientometrics work further contributes to journal meta-ranking methodologies using statistical estimators. Publication trends reveal sustained engagement with real-world economic and social challenges, with recent work (2020-2021) emphasizing financial stability systems and healthcare resource allocation, while earlier contributions focus on risk modeling and academic evaluation frameworks. Collaborative patterns show consistent co-authorship across institutional and disciplinary boundaries. Scientific Awards: No scientific awards are documented in the provided text Advising and Grants: No information regarding student supervision, grants, or funding sources is available in the source material Labs and Teams: The Institute of Security Research affiliation is noted, but specific laboratory structures, research teams, or collaborative groups are not described

• Recommender Systems
• User Modeling
• Generative AI
• +۴ مورد دیگر

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
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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.

Renata Raidouمشاهده پروفایل

دانشیار

• Medical Visualization
• Visual Analytics
• Uncertainty Visualization
• +۳ مورد دیگر

Renata Raidou is an Associate Professor in Biomedical Visualization and Visual Analytics at the Research Unit of Computer Graphics, Institute of Visual Computing & Human-Centered Technology, TU Wien, Austria. She previously held positions at the University of Groningen and TU Wien as a postdoc. Her research focuses on medical visualization, uncertainty visualization, and data physicalization for healthcare applications. Raidou has received prestigious awards including the Dirk Bartz Prize (2017) and EuroVis Young Researcher Award (2022). She coordinates TU Wien's BSc in Digital Health and MSc in Medical Informatics. Education: PhD in Medical Visualization (Eindhoven University of Technology, 2017), MSc in Biomedical Engineering (TU Delft), Diploma in Electrical and Computer Engineering (NTUA). Professional Roles: Editorial Board member of Computer & Graphics , Steering Committee member of EG VCBM, and member of EASAC's AI in Healthcare working group. Research Interests: Medical visualization strategies for P4 medicine, anatomical edutainment through physicalization, and uncertainty-aware visual analytics in radiotherapy. Her work bridges visual computing, machine learning, and medical applications, emphasizing clinical decision support and public health education. Awards: Over 10 awards including Best Paper recognitions at IEEE Vis and EuroVis, and the EuroVis Best PhD Award. Her contributions span visualizing anatomical variability, ensemble data exploration, and interactive medical training tools. Grants & Projects: Leads the Health Virtual Twins project (2024–2028) for personalized stroke management. Active in interdisciplinary collaborations, including the Shonan Meeting on Formalizing Biological Visualization. Labs/Teams: Head of the Visualization Group at TU Wien, developing tools like Vologram (holographic medical data physicalization) and Slice and Dice anatomical crafts. Collaborates on edutainment systems and AI-driven healthcare visualization.

• Recommender Systems
• Digital Humanism
• User Modeling
• +۵ مورد دیگر

Julia Neidhardt is an Assistant Professor at TU Wien Informatics, specializing in Information Systems Engineering. Her research focuses on recommender systems, digital humanism, and user modeling, particularly in tourism and news domains. She leads the Christian Doppler Lab for Recommender Systems and is a UNESCO Co-Chair on Digital Humanism. Neidhardt holds a background in mathematics and computer science, with postdoctoral research at the Austrian Academy of Sciences and visiting scholar roles at Northwestern University and the University of Geneva. Roles: Assistant Professor, Leader of Christian Doppler Lab for Recommender Systems, Board Member of CAIML Education: Dr. techn. (PhD) and Mag.a rer.nat. (Master's) in relevant fields Her research emphasizes ethical AI, fairness in recommendations, and human-centered design. Notable contributions include work on sentiment analysis in online forums, serendipity in recommendations, and offensive language detection in multilingual contexts. She actively chairs conferences like ACM UMAP and RecSys, and serves on editorial boards for journals like the Journal of Information Technology & Tourism. Grants/Projects: Leads the CDL Rec-Sys Lab (2022–2028), Excalibur (2022–2023), and projects on digital humanism and tourism technology. Her work integrates network analysis, computational linguistics, and empirical studies to address societal impacts of AI. Awards: Distinguished Reviewer of ACM TORS, UNESCO Co-Chair, and top-tier conference involvement.

Rainer Böhmeمشاهده پروفایل

استاد

• Digital Forensics
• Privacy-Enhancing Technologies
• Cryptographic Financial Instruments
• +۵ مورد دیگر

Univ.-Prof. Dr. Rainer Böhme is a Professor for Security and Privacy in the Department of Computer Science at the University of Innsbruck, Austria. He also held visiting positions, including at MIT (2023) and the University of Münster (2010–2015). His research focuses on digital forensics, privacy-enhancing technologies, cryptocurrency analysis, and the economic aspects of cybersecurity. Böhme has contributed to foundational work on Central Bank Digital Currencies (CBDCs), steganography, and cybercrime. He is a BIS Research Fellow and has advised institutions like the Austrian National Bank. His work bridges technical security, economic incentives, and policy implications. Notable projects include the BITCRIME initiative and studies on Bitcoin's regulatory challenges. He leads the Security and Privacy Lab, fostering interdisciplinary research in information security and privacy. Education: Ph.D. (Dr.-Ing.) in Computer Science from Technische Universität Dresden (2008), M.A. in Communications, Economics, and Computer Science from the same university (2003). Notable grants include BIS-supported research on CBDC design. His teaching includes courses on security management and blockchain systems. Research interests span steganography, digital currencies, and the societal impact of cybercrime. Recent work explores neural compression's forensic implications and privacy in digital payments. Awards include recognition for contributions to CBDC policy and steganalysis techniques.

• Randomized Algorithms
• Networking
• Social Networks
• +۷ مورد دیگر

Aravind Srinivasan is a Professor of Computer Science at the University of Maryland, College Park, USA. He holds tenure and has held academic positions at the National University of Singapore and Bell Labs. He completed his B.Tech at IIT Madras and his Ph.D. at Cornell University, with postdoctoral research at the Institute for Advanced Study and DIMACS. His research focuses on randomized algorithms, networking, social networks, combinatorial optimization, and their applications to public health, machine learning, and energy systems. He has published over 115 papers in top journals like Nature and the Journal of the ACM, and his work has received significant recognition, including 1,551 citations for his 2004 Nature paper on disease modeling. Education: B.Tech, Indian Institute of Technology Madras Ph.D., Cornell University Awarded Fellowships from ACM, AAAS, IEEE, and EATCS, he also serves as Editor-in-Chief of the ACM Transactions on Algorithms. His students have secured roles in academia, industry, and government, reflecting his impactful mentorship. His contributions span theoretical computer science and applied domains, with notable work in probabilistic methods, network science, and interdisciplinary applications.

Wojciech Szpankowskiمشاهده پروفایل

استاد

• Analysis of Algorithms
• Information Theory
• Analytic Combinatorics
• +۶ مورد دیگر

Wojciech Szpankowski is the Saul Rosen Distinguished Professor of Computer Science at Purdue University. He holds concurrent positions as a Guest Professor at ETH Zurich and a Professor at Jagiellonian University, Krakow. His research focuses on analysis of algorithms, information theory, analytic combinatorics, and random structures. He has directed major initiatives like the NSF Science & Technology Center on Science of Information (CSoI), a $50M 10-year project. Szpankowski is a Fellow of the IEEE and has received prestigious awards including the Flajolet Prize (2020) and Humboldt Research Award (2010). He has held visiting roles at institutions worldwide, including Stanford University and the Newton Institute, Cambridge. His academic journey includes tenure as Full Professor at Purdue (since 1992), Assistant Professorships at McGill University (1984–1992) and Technical University of Gdańsk (1980–1984). His scholarly contributions span monographs like "Average Case Analysis of Algorithms on Sequences" (2001) and "Analytic Pattern Matching" (2015), with a forthcoming work on Analytic Information Theory (2022). He actively contributes to scientific boards, including HIIT Helsinki and NeuroMat Brazil. Szpankowski’s publications emphasize interdisciplinary research at the intersection of computer science, mathematics, and biology. His work on privacy-preserving data analysis, dynamic network inference, and protein superfamily evolution showcases his broad impact. Keynote talks at major conferences (e.g., SODA 2019, AofA 2016) reflect his leadership in theoretical computer science. His grants and recognitions underscore institutional trust: the NSF Science & Technology Center (2010) and Arden L. Bement Jr. Award (2015) highlight his pioneering role in redefining information science. Collaborations span academia and industry, with affiliations at Hewlett-Packard and INRIA.