جست‌وجوی استاد و پژوهشگر

• Mobile Computing
• Transportation Systems
• Traffic Simulation
• +۵ مورد دیگر

Jens Krösche is a Professor at FH Hagenberg (part of Upper Austria University of Applied Sciences), affiliated with the Research Center Hagenberg's Information & Communications Technology (ICT) department. His research focuses on mobile computing, transportation systems, and sensor networks with key contributions in location-based services, traffic simulation, and energy data infrastructure. Education & Career Expertise developed through projects like EDDIE (European energy data infrastructure), SOMOBIL (mobile phone data for public transport), and DVS-Mobile (traffic control systems). Holds a Dr.-Ing (Engineering Doctorate). Research Interests Specializes in: Mobile device applications in urban planning and transport Context-aware systems and sensor data fusion Indoor positioning and navigation solutions Data-driven approaches for sustainable mobility Projects EDDIE (2023-2025) : Developing distributed energy data infrastructure SOMOBIL (2014-2016) : Mobile phone data for public transport optimization DVS-Mobile (2012-2014) : Intelligent traffic systems for dynamic conditions Professional Activities Peer reviewer for conferences like UbiComp, MoMM, and IPIN Committee member for Web Engineering & Tourism workshops Speaker on location-based gaming in tourism contexts

• Mobile Computing
• Human Activity Recognition
• Wearable Devices
• +۴ مورد دیگر

Stephan Selinger is a Professor at FH Hagenberg, part of the University of Applied Sciences Upper Austria. He specializes in the Department of Information and Communications Technology (ICT), focusing on wearable devices, human activity recognition, and energy expenditure prediction. His research integrates machine learning, sensor technology, and data analysis to address challenges in health monitoring, sports science, and navigation systems. He is a Co-Investigator in the EDDIE project (2023-2025), a Horizon-funded initiative developing distributed energy data infrastructure. His work spans topics like Kalman filtering for positioning, smart sock-based power estimation, and comparative studies of wearable device performance. Dr. Selinger has organized academic events and peer-reviewed conferences such as IoT 2015 and MUM 2012. His research outputs include 13 publications since 2006, with notable contributions in energy expenditure modeling and activity recognition algorithms.

Wolfgang Aignerمشاهده پروفایل

استاد

• Information Visualization
• Visual Analytics
• Human-Computer Interaction
• +۳ مورد دیگر

Wolfgang Aigner is a Professor and Deputy Head of the Faculty of Engineering and Business at the University of Applied Sciences St. Pölten. He leads the Institute of Creative Media Technologies and is affiliated with the Department of Media and Digital Technologies. His expertise spans Information Visualization, Visual Analytics, and Human-Computer Interaction, with a focus on time-oriented data. He holds a PhD from TU Wien and a Habilitation in Practical Computer Science. Education : PhD (Dr. techn.) in Computer Science, TU Wien (2006) Habilitation (Priv.-Doz.), TU Wien (2013) MSc (Dipl.-Ing.) in Computer Science, Johannes Kepler University Linz and TU Wien (1997-2003) Research : Aigner's work emphasizes visual analytics for time-series data and data journalism. Key projects include VALiD (Visual Analytics in Data-Driven Journalism) and VisOnFire (workflow visualization). He authored over 125 peer-reviewed articles and the book Visualization of Time-Oriented Data (Springer, 2011). Projects & Labs : Active in initiatives like KAVA-Time, IoT4LAC, and Trust AI. He leads research groups in Media Computing, focusing on areas like Data Science, Health Informatics, and Machine Learning.

Martin Horauerمشاهده پروفایل

استاد

• Embedded Systems
• Automotive Electronics
• Communication Sub-Systems
• +۴ مورد دیگر

Prof. Martin Horauer is a Professor and Head of both the Bachelor in Electronics Engineering and Master in Embedded Systems programs at Technikum Wien, Austria. He holds a Doctoral degree from Vienna University of Technology and has over two decades of expertise in embedded systems, automotive electronics, and real-time communication protocols. His research focuses on fault tolerance, high-level synthesis, and verification of embedded systems. Education: Electrical Engineering from Vienna University of Technology (1994 Ph.D.) Affiliations: Research Group Embedded Systems, Vienna University of Technology (1997–2002), Technikum Wien (2002–present) His work spans FlexRay automotive networks , FPGA reliability , and system-on-chip verification . Notable contributions include the SynUTC synchronization project and contributions to the Josef Ressel Center for Verification of Embedded Systems. Awards include the Vienna MA27 Research Prize (2008) and best paper awards at FMICS 2011 and DSN 2001. Grants include EU EFRE-funded projects (AC-Centrope II) and FFG initiatives (Flexicount). He chairs review boards for academic programs and serves on technical committees for DATE, RTAS, and MESA conferences.

• Statistical Genetics
• High Performance Computing
• Bioinformatics
• +۳ مورد دیگر

Roles and Affiliations: Associate Professor of Applied Statistics at the Department of Mathematical Sciences, Purdue University Fort Wayne. Member of the College of Science. Active in Google Scholar and ResearchGate academic networks. Email: yorgovd@pfw.edu Research Interests: Focuses on statistical genetics, particularly in developing methods for analyzing human complex traits such as admixture mapping and association testing. Also engaged in kinship estimation in heterogeneous populations, high performance computing, and bioinformatics. Explores applications of R and Python in data science education and research. Teaching: Instructs courses like STAT242 (Introduction to Data Science with R) and provides free access to DataCamp courses for students. Known for creating R tutorials and computational tools for statistical analysis.

Bernhard Hollausمشاهده پروفایل

مدرس

• Sports Technology
• Machine Learning
• Sensor Systems
• +۳ مورد دیگر

Bernhard Hollaus is a Lecturer at the Management Center Innsbruck (MCI), where he teaches and conducts research in mechatronics, health technologies, and sports engineering. He leads the Health Tech Research Cluster at MCI and has held roles such as CEO of Auto Hollaus and Project Manager at Siemens AG. His academic affiliations include the University of Innsbruck, where he earned a PhD in Sport Science, and FH Technikum Wien for his MSc and BSc in Industrial Elec and Elec. His research focuses on applying machine learning and sensor systems to sports technology, including golf club impact detection, cycling cadence analysis, and American football training automation. He has supervised over 100 bachelor and master theses on topics ranging from VR sledding simulators to neural networks in sports analytics. Key awards include the Cluster Award Mechatronics (2016), Higher Degree Research Student Prize (2020), and distinctions from the University of Innsbruck (2021) and Technikum Wien (2011–2012). His work bridges engineering and sports science, with notable contributions to wearable sensor applications and sports safety education. Notable projects include developing the VRodel sledding simulator, a golf club impact detection system, and advanced passing machines for American football training. Collaborations span academic institutions and industry partners, reflecting his dual expertise in technical innovation and sports performance.

• Sustainable Consumption
• Food Waste Reduction
• Climate Change Mitigation
• +۳ مورد دیگر

Karin Dobernig is a Researcher at the Institute for Sustainability, specializing in sustainable consumption, food waste reduction, and climate change mitigation. Her work focuses on interventions to reduce meat consumption, household food waste practices, and policy implications of environmental behaviors. She has led and collaborated on projects like 'bewusstkaufen' and 'biscuit4all,' aiming to foster carbon-friendly diets and sustainable living practices. Her research spans interdisciplinary topics including consumer behavior, environmental policy, and urban agriculture. Dr. Dobernig has received the prestigious Ars Docendi Staatspreis für exzellente Hochschullehre (2019) for excellence in university teaching, reflecting her contributions to pedagogical innovation. Her publications analyze systemic approaches to sustainability, such as cloud computing’s energy impact and the intersection of consumption patterns with economic growth. Recent projects include developing web portals for climate-friendly living and exploring grassroots initiatives to mitigate food waste. Collaborations span academic and policy domains, addressing both behavioral and structural challenges in achieving environmental sustainability.

• Security
• Intrusion Detection
• Malware Detection
• +۲ مورد دیگر

Christopher Krügel is a Professor at Technische Universität Wien, affiliated with the Department of Automation Systems (E191-03). His research focuses on Security, Intrusion Detection, Malware Detection, Operating Systems, and Networks. He leads multiple research projects and has supervised 20+ graduate students. His research explores malware analysis techniques, web security vulnerabilities, hardware-level attacks (e.g., Rowhammer), and privacy threats in mobile/social systems. Recent work examines ARM TrustZone security and machine learning limitations in malware detection, while earlier publications cover P2P networks, phishing defenses, and binary analysis. Awards: Wirtschaftskammerpreis (2005) EIB Scientific Award for Excellent Diploma Thesis (2000) Grants & Projects: Includes SECoverer (web app security, 2008-2011), WOMBAT (malware observatory, 2008-2011), Pathfinder (malware analysis, 2007-2009), and 5+ other funded initiatives focusing on binary analysis, pervasive security, and malware detection. He leads the Automation Systems research group, collaborating with institutions like European Commission and Austrian Science Fund.

Jan Malyمشاهده پروفایل

استادیار

• Computational Social Choice
• Logic and Knowledge Representation
• Participatory Budgeting
• +۳ مورد دیگر

Jan Maly is an Assistant Professor at the Institute for Data, Process and Knowledge Management (DPKM) of the Vienna University of Economics and Business (WU Wien) and a postdoctoral researcher in the Database and Artificial Intelligence Group (DBAI) of TU Wien. He is currently on parental leave until September 15, 2024. His educational background includes mathematics and philosophy studies at Würzburg University, specialization in mathematical logic at Vienna University, and a PhD in computer science from TU Wien completed in 2020. Dr. Maly's research focuses on Computational Social Choice (COMSOC), Logic and Knowledge Representation, with particular emphasis on Participatory Budgeting and Fairness in Online Decision Making. His work aims to develop tools that help people make better decisions, individually or as a group, by investigating non-standard voting frameworks and computational complexity questions. His recent work addresses the critical problem that simple majority voting in online environments marginalizes minority opinions and reinforces filter bubble effects. Analysis of his publication record reveals a strong focus on developing fair voting mechanisms for participatory budgeting and online decision-making. His work bridges theoretical computational social choice with practical applications, particularly in developing algorithms that ensure proportional representation while maintaining computational feasibility. The publications demonstrate increasing sophistication in handling multi-issue decisions, perpetual voting scenarios, and complex budget allocation problems. netidee SCIENCE grant (2024, ~400,000 euros) Erwin Schrödinger Fellowship from FWF Dr. Maly has co-supervised PhD students including Simon Rey and Michael Bernreiter. His collaborative work spans multiple institutions, including the University of Amsterdam where he worked from September 2021 to April 2023 as a member of the COMSOC Group. He is also the co-founder of the European Digital Democracy Network, which organized its first conference in April 2024 bringing together over sixty scientists and practitioners.

• Artificial Intelligence
• Multi-Agent Systems
• Computational Social Choice
• +۳ مورد دیگر

Oliviero Nardi is a Universitätsassistent (Researcher) at the Vienna University of Technology (TU Wien) , affiliated with the Faculty of Informatics and the Department of Databases and Artificial Intelligence . His research focuses on computational social choice, multi-agent systems, and fair allocation mechanisms. He holds an MSc and currently pursues doctoral studies (PreDoc Researcher role). Research Interests : Nardi explores interdisciplinary topics including argumentation frameworks, voting systems, cybersecurity, and algorithmic fairness. His work bridges theoretical computer science with practical applications in decision-making processes and multi-agent environments. Recent Projects : HolisticPB (2021–2024) : Investigates fair allocation of indivisible resources in repeated settings. SuDeMa (2019–2025) : Examines free-riding behaviors in multi-issue decision-making scenarios. Teaching : He instructs courses like Formal Methods in Computer Science and Preferences in Artificial Intelligence , reflecting his expertise in theoretical foundations and AI applications. Key Research Trends : His publications emphasize algorithmic solutions for collective decision-making, cybersecurity etiology, and graph-based justification mechanisms. Recent work merges voting systems with argumentation theory to model online discussions. Grants & Lab Affiliations : Engaged in funded projects HolisticPB and SuDeMa, contributing to collaborative research environments addressing societal challenges in AI and decision science.

• Cyber-Physical Systems
• Runtime Verification
• Formal Methods
• +۵ مورد دیگر

Dejan Nickovic is a Professor affiliated with the Department of Cyber-Physical Systems at TU Wien. His research focuses on runtime verification, formal methods for cyber-physical systems (CPS), and software testing. He leads projects such as TAIGER (2023–2027) and ARTIST (2021–2026), advancing CPS safety and AI robustness. His work bridges theoretical formalisms with practical applications in robotics, IoT, and embedded systems. Key research themes include specification mining, fault localization, and automated verification. He has developed tools like DeepRIoT for robotic-IoT integration and FIM for Simulink fault analysis. His professorial dissertation (2020) formalized runtime verification techniques for CPS. Recent publications emphasize adaptive architectures, backdoor detection in AI, and hyperproperties for security. He collaborates extensively with academia and industry, addressing challenges in CPS testing and verification through grants like ZK 35-G (2019–2024). His lab supports platforms like CPS/IoT Ecosystem for research and education. Projects: TAIGER, ARTIST, EdgeAI, ProbInG Supervised students: N. Manjunath (PhD, 2021), Florian Exenberger (2019), Markus Heindl (2017)

• Machine Learning
• Graph Neural Networks
• Recommender Systems
• +۳ مورد دیگر

David Penz is a PreDoc Researcher at Technische Universität Wien (TU Wien), affiliated with the Faculty of Informatics and the Department of Machine Learning. He teaches courses such as Introduction to Machine Learning , Machine Learning Algorithms and Applications , and Theoretical Foundations and Research Topics in Machine Learning . His research focuses on advancing Graph Neural Networks (GNNs), particularly for outerplanar graphs, and developing ethical AI systems in recommendation engines. His work emphasizes data privacy, fairness in AI, and music information retrieval. Key projects include the StruDL initiative (2023–2027), exploring maximally expressive GNN architectures. He has supervised a diploma thesis on antibody-antigen binding affinity prediction using geometric deep learning. Recent publications highlight contributions to unlearning sensitive user attributes in recommendations and emotion-aware music recommendation systems. His academic output spans conferences like NeurIPS, SIGIR, and ICMR, demonstrating expertise in both theoretical and applied machine learning domains. He actively participates in academic challenges, such as the ACM Recommender Systems Challenge 2021, showcasing lightweight XGBoost approaches.

Majid Salimibeniمشاهده پروفایل

پژوهشگر ارشد

• High Performance Computing
• Parallel Computing
• Message Passing in Large-scale HPC systems
• +۶ مورد دیگر

Majid Salimibeni is a PostDoc Researcher and Research Fellow at Vienna University of Technology (TU Wien) , affiliated with the Parallel Computing research unit. He previously held a Postdoc position at the Department of Computer Science at the University of Salerno, Italy (2024), and has been a visiting researcher at TU Wien (2023). Currently based in Vienna, Austria, his work focuses on High Performance Computing (HPC) and its interdisciplinary applications. Education: Ph.D. in Computer Science, University of Salerno, Italy (2020–2024) M.Sc. in Computer Engineering (Software), Shiraz University, Iran (2017–2020) B.Sc. in Computer Engineering (Software), University of Birjand, Iran (2012–2016) Salimibeni's research explores High Performance and Parallel Computing , with a focus on optimizing GPU communication via NCCL , improving energy efficiency in heterogeneous computing environments , and enhancing MPI collective algorithms for large-scale systems. His work bridges HPC infrastructure with emerging applications in Distributed Deep Learning and AI . Recent publications highlight his expertise in GPU frequency scaling , NCCL profiling , and process arrival pattern analysis to improve distributed system performance. These works appear at top conferences like IPDPS , Cluster Computing , and ASHPC . Scientific Awards: Best Paper Award, Bench 2022 Salimibeni contributes to academic committees as a program committee member for IEEE ICPP 2025, ISC 2025, and BigHPC 2024, and serves on artifact evaluation committees for ASPLOS and CF conferences.

• Multimedia Systems
• News Distribution Networks
• Information Systems Engineering
• +۳ مورد دیگر

Markus Schranz is an External Lecturer in the Information Systems Engineering department at TU Wien. His expertise focuses on multimedia systems, news distribution networks, and collaborative network architectures. He has contributed to foundational works on pan-European news distribution systems and multimedia content management. He teaches the course Web Application Engineering & Access Management (2024W). His research emphasizes scalable media distribution, cross-platform adaptation, and collaborative network design. While no awards are explicitly listed, his work reflects significant contributions to media infrastructure engineering. His publications span technical encyclopedias and conference proceedings, addressing both theoretical frameworks and practical implementations in multimedia news systems.

• Music Recommender Systems
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Shahrzad Shashaani is a PreDoc Researcher at Vienna University of Technology , affiliated with the College of Engineering. Her work spans multiple domains including artificial intelligence, recommender systems, and biomedical applications. Research Focus : Explainability in music recommender systems, sequential recommendation algorithms, and AI-driven diagnostics Projects : HumRec (2021–2025) - Explainability in Music Recommender Systems Technical Expertise : Machine learning, network security, autonomous vehicles, and biomedical sensor analysis Recent publications highlight her contributions to contrastive learning in music recommendation, self-supervised intrusion detection, and Bayesian frameworks for graphene transistor-based enzyme activity analysis. She also explores AI applications in rapid pandemic screening protocols. Her academic career combines engineering rigor with interdisciplinary applications, particularly in health analytics and intelligent systems. Current responsibilities include course instruction in "Experiment Design and Execution" (VU 194.192).