جست‌وجوی استاد و پژوهشگر

• Computer Security
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Daniel Christopher Arp is an Assistant Professor at the Vienna University of Technology , affiliated with the Security and Privacy group (E192-06). His research focuses on building robust and explainable AI-based defenses for computer security, supported by the Vienna Science and Technology Fund (WWTF) project (2024–2030). He teaches courses including Machine Learning for Computer Security , Project in Computer Science , and the Seminar for PhD Students . Email: daniel.arp@tuwien.ac.at Phone: +43-1-58801-192614 Office: Favoritenstrasse 9, Room HA0111 Research Interests: Daniel specializes in applying machine learning techniques to enhance computer security, with a focus on explainability and robustness against adversarial attacks. His work addresses critical challenges in AI reliability for cybersecurity applications. Project: He leads the WWTF-funded initiative Building Robust and Explainable AI-based Defenses for Computer Security (2024–2030), aiming to develop interpretable AI models for security systems. Publications Trends: His recent work highlights pitfalls in ML-driven security systems, emphasizing the need for rigorous evaluation frameworks and adversarial robustness. Courses: Daniel supervises bachelor theses, teaches project-based computer science courses, and mentors PhD students in security-related research.

Florian Michahellesمشاهده پروفایل

استاد

• Human-Centered AI
• Ubiquitous Computing
• Internet of Things (IoT)
• +۵ مورد دیگر

Florian Michahelles is a Full Professor of Ubiquitous Computing at TU Wien, specializing in human-centered AI and IoT systems. He holds a Ph.D. from ETH Zurich and an MSc from Ludwig-Maximilians University Munich. His research focuses on integrating technology into everyday life while maintaining human control, including digital companions, AI-driven task optimization, and sustainable tech solutions. Roles include Full Professor in Artifact-based Design and User Research, Curriculum Coordinator for Human-Centered Computing, and Principal Member of TU Wien's Informatics department. He leads projects like the 'Eternity Bike' (self-balancing bicycle) and 'Urban-Engage' (citizen-driven urban planning). Education: Ph.D. (ETH Zurich), MSc (LMU Munich) Key Research Areas: Human-AI Collaboration, Wearable Tech, Sustainable Computing, Robotics Interaction Notable awards include the IoT 2023 Best Paper Award and the Ten Year Impact Award (MUM 2022). His work spans 20+ publications annually, addressing topics from textile interfaces to climate action systems. Active in entrepreneurship, he co-founded 42matters and advises on innovation management. His lab develops prototypes like the 'Smart Trash Can' and 'Skillab' AR training platform.

Gustau Camps-Vallsمشاهده پروفایل

استاد

• Remote Sensing for Terrestrial Biosphere Monitoring
• Explainable Artificial Intelligence
• Causal Discovery
• +۴ مورد دیگر

Gustau Camps-Valls is a Full Professor of Electrical Engineering at the University of Valencia, Spain, where he has been employed since 2002, progressing from Assistant Professor to Associate Professor and finally to Full Professor in 2017. He serves as Head of the Image and Signal Processing (ISP) group (http://isp.uv.es) and coordinates the 'Machine Learning for Earth and Climate Sciences' program within ELLIS.eu, Europe's premier AI network of excellence. Professor Camps-Valls specializes in the intersection of artificial intelligence and Earth sciences, with research spanning remote sensing for terrestrial biosphere monitoring, explainable AI and causal discovery, Earth system modeling through machine learning, and geoscience data analysis. His work bridges theoretical machine learning advances with practical Earth observation applications, creating interpretable models that integrate physical knowledge with data-driven approaches. With over 250+ peer-reviewed journal papers, 300+ conference papers, and an h-index of 78 (29,000+ citations), his publication record demonstrates significant impact across both technical (IEEE, PLOS One) and high-impact scientific journals (Nature, Science Advances, PNAS). His research consistently focuses on developing machine learning methods specifically tailored for Earth observation challenges. 2022 Fellow of the European Academy of Sciences (EurASc) 2022 Fellow of Asia-Pacific Artificial Intelligence Association (AAIA) 2021 Highly Cited Researcher 2020 ERC Synergy Grant recipient 2019 Google Classic Paper Award 2018 IEEE Fellow 2017 IEEE Distinguished Lecturer 2015 ERC Consolidator Grant recipient Professor Camps-Valls has (co)advised over 30 PhD theses and coordinated/participated in more than 60 research projects with industry and academia at national and European levels. He serves on the advisory boards of major space organizations including ESA Phi-Lab and EUMETSAT, demonstrating the practical impact of his research. His substantial grant portfolio includes two prestigious European Research Council grants across different scientific domains. Leading the Image and Signal Processing research group, Professor Camps-Valls maintains strong international collaborations with institutions including Max Planck Institutes in Germany and École Polytechnique Fédérale de Lausanne in Switzerland. He is a vocal advocate for open code/data science in Earth observation and regularly contributes to shaping research policy through his membership in the European Space Sciences Committee.

Giunchiglia Faustoمشاهده پروفایل

استاد

• Social computing
• Logics, formal methods
• Knowledge management, representation and organization
• +۳ مورد دیگر

Fausto Giunchiglia is a Full Professor at the Faculty of Science, University of Trento, Italy. He has held academic positions including Associate Professor at the Faculty of Economics (1992–1999) and visiting roles at Stanford University and the University of Edinburgh. His research focuses on Social computing, Logics, Knowledge management, Artificial Intelligence, Agents, and Semantics. Education: PhD in Computer Engineering (1987, University of Genoa), Master in Computer Science (1983, University of Genoa), and Laurea in Electronic Engineering (1981, University of Genoa). Research highlights include contributions to semantic matching (S-Match algorithm), planning via model checking, and multilingual systems. Notable awards include ECCAI Fellowship (2002), AI*IA Prize (1992), and multiple innovation awards for EU projects like WeNet and WhiteRabbit. Guided over 50 students, including PhD candidates from 2024 down to 1985. Active in interdisciplinary projects addressing knowledge representation, mobile sensing, and cross-cultural AI applications.

David Schinaglمشاهده پروفایل

پژوهشگر

• Computer Vision
• Autonomous Driving
• 3D Object Detection
• +۳ مورد دیگر

David Schinagl is affiliated with the Institute of Visual Computing at TU Graz. His research focuses on LiDAR-based 3D object detection , explainable AI , and autonomous driving technologies . He has contributed to methods like OccAM's Laser for model interpretability and MAELi for large-scale LiDAR processing. His work emphasizes sensor data analysis and geometric confidence enhancement. Publications span top venues including CVPR, ICCV, and WACV (2018–2024). His GitHub repository demonstrates implementations like the OccAM's Laser demo for LiDAR attribution maps.

Bettina Könighoferمشاهده پروفایل

استادیار

• Artificial Intelligence
• Reinforcement Learning
• Formal Methods
• +۳ مورد دیگر

Bettina Könighofer is an Assistant Professor at the Technische Universität Graz (TU Graz), affiliated with the Institute of Information Security. Her academic background includes a BSc, Dipl.-Ing. (Diplom-Ingenieur), and Dr.techn. (Technical Doctorate). She specializes in formal methods applied to AI safety, reinforcement learning, and autonomous systems. Her research emphasizes ensuring safety and ethical compliance in decision-making systems through techniques like shield synthesis and formal verification. Research Interests: Safe Reinforcement Learning Algorithmic Fairness and Ethical AI Formal Verification of Learning Systems Autonomous Systems Safety Legal Accountability in Automated Decision-Making Her recent work explores safety shields for real-time decision enforcement, tools for legal accountability (e.g., soid ), and adaptive architectures for safety-critical systems. Notable projects include contributions to QComp verification competitions and Dagstuhl seminars on trustworthy AI. She holds a leadership role in academic conferences, including co-chairing the AISoLA 2023 Track C2. Her research often intersects with practical applications in mobility systems and regulatory compliance.

Franz Pernkopfمشاهده پروفایل

استاد

• Machine Learning
• Probabilistic Graphical Models
• Signal Processing
• +۴ مورد دیگر

Franz Pernkopf is a Professor at the Signal Processing and Speech Communication Laboratory, Graz University of Technology, Austria. He holds a MSc from Graz University of Technology (1999) and a PhD from the University of Leoben (2002). His research focuses on machine learning, probabilistic graphical models, and signal processing applications in speech recognition, medical data analysis, and radar technology. He has led the Christian Doppler Laboratory for Dependable Intelligent Systems and contributed to national research networks in signal processing. His work emphasizes resource-efficient deep learning, Bayesian networks, and time-series analysis. Notable contributions include interference mitigation in radar systems and acoustic event detection. Awards include the Erwin Schrödinger Fellowship (2002) and Young Investigator Award (2010). He advises on projects ranging from lung sound analysis to embedded system optimizations. His lab hosts courses on signal processing and machine learning.

Can Liمشاهده پروفایل

استاد

• Physical Chemistry
• Environmental Catalysis
• Chiral Catalysis
• +۴ مورد دیگر

Can Li is a Full Professor at the Dalian Institute of Chemical Physics, affiliated with the Chinese Academy of Sciences (elected in 2003) and holds memberships in prestigious institutions such as the Academy of Sciences for Developing World (TWAS, 2005) and Academia Europaea (2008). His academic career spans decades, including leadership roles as Director of the State Key Laboratory of Catalysis (1998–2015), President of the International Association of Catalysis Societies (2008–2012), and Chairman of the Catalysis Society of China (2005–2012). Dr. Li’s research focuses on broad areas of catalysis and physical chemistry, including Environmental Catalysis , Chiral Catalysis , Photo(electro)catalysis , and Biocatalysis . His work also involves advanced analytical techniques like In-situ Characterization of Catalytic Reactions and Time-resolved Spectroscopy . While recent Google Scholar publications appear to focus on computer science topics like AI fairness and safety, his core research remains rooted in chemical sciences. His scholarly contributions have been recognized with multiple awards, including the National Catalysis Achievement Award of China (2014) , Natural Science Award of China (2011) , and the International Catalysis Award (2004) . He has held visiting professorships at institutions in Belgium, the USA, the UK, Japan, and France. 2014 National Catalysis Achievement Award of China 2011 Natural Science Award of China 2005 Outstanding Science and Technology Achievement Prize of the CAS 2005 HoLeungHoLee Prize 2004 International Catalysis Award 1998 National Award for Outstanding Young Scientists in China 1997 Hong Kong Qiu-Shi Outstanding Young Scientist Award

• Artificial Intelligence
• Smart Farming
• Digital Agriculture
• +۵ مورد دیگر

Stefan Schweng is a researcher at the University of Natural Resources and Life Sciences, Vienna (BOKU), affiliated with the Institute of Forest Engineering and the Institute of Viticulture and Pomology within the Department of Ecosystem Management, Climate and Biodiversity. His work bridges computer science, agricultural engineering, and environmental science, focusing on practical AI applications for sustainable resource management. His research interests center on the integration of artificial intelligence in agricultural and forestry systems. Schweng specializes in developing digital twin technologies for smart farming applications, with particular focus on optimizing economic outcomes while maintaining soil health. His work also extends to explainable AI frameworks for complex data analysis in natural resource management, including point cloud processing for 3D environmental modeling. Analysis of his recent publications reveals a strong trend toward practical AI implementations in agriculture and forestry. His work combines advanced machine learning techniques with domain-specific knowledge to create decision support systems that address real-world challenges in sustainable farming and forest management. The research demonstrates an interdisciplinary approach that connects computer science innovations with practical agricultural applications. As project staff on the FWF-funded "A reference model for explainable Artificial Intelligence" initiative running from 2022-2025, Schweng contributes technical expertise to developing transparent AI systems. His collaborative work spans multiple institutes at BOKU, reflecting the interdisciplinary nature of modern agricultural technology research. Schweng's technical work supports BOKU's research infrastructure in digital agriculture and forest engineering, contributing to the university's focus on sustainable resource management through technological innovation.

Matthias Zeppelzauerمشاهده پروفایل

استاد

• Human-Centered AI
• Computer Vision
• Multimodal Learning
• +۵ مورد دیگر

FH-Prof. Priv.-Doz. Dr. Matthias Zeppelzauer is a Professor of Computer Vision and Machine Learning at the University of Applied Sciences St. Pölten, leading the Media Computing Research Group at the Institute of Creative Media Technologies. His work focuses on human-centered AI, explainable machine learning, and clinical gait analysis. He holds a habilitation in Computer Science from TU Wien and has authored/co-authored over 90 publications. Education: PhD in Computer Science (2011, TU Wien) Habilitation (2020, TU Wien) Bachelor/Master in Media Informatics (1999-2006, TU Wien) Research Interests: Human-centered AI, computer vision, multimodal learning, explainable AI, clinical gait analysis, and social media analysis. Cross-cutting themes include interactive machine learning and trustworthy AI. Key Contributions: Developed methods for gait analysis (GaitRec dataset), explainable AI frameworks (KAVAGait), and multimodal fake news detection. Coordinated the Center for Artificial Intelligence at FH St. Pölten since 2021. Recognition: 2024 EuroVA Best Paper Award 2021 MTD Award for ReMoCapLab 2020 CVPR Outstanding Reviewer 2018 Austrian Open Source Award Grants & Projects: Coordinator of FAIRAI, TrustAI, and ReMoCapLab projects. Involved in EU-funded initiatives like Visual Heritage and Center for Digital Health. Labs/Teams: Leads Media Computing Research Group and collaborates with the Center for Artificial Intelligence and Center for Digital Health.

• Biomechanics
• Machine Learning
• Gait Analysis
• +۴ مورد دیگر

Bernhard Dumphart, MSc, is a Junior Researcher at the Department of Health Sciences, University of Applied Sciences South East Austria. His work focuses on biomechanical analysis, particularly gait assessment, using machine learning and motion capture technologies. He is affiliated with the Institute of Health Sciences and contributes to study programs like Physiotherapy (BA). Education: Holds degrees of Bakk. (Bachelors) and MSc. His research integrates clinical applications of virtual reality (VR) and smartphone-based markerless motion capture systems. He collaborates on projects such as PROGRESS, VReeze, and IntelliGait, aiming to improve tele-rehabilitation and gait classification algorithms. Research Interests: Key areas include gait event detection, machine learning integration in biomechanics, VR effects on human movement, and motion capture technology validation. His studies explore clinical implications of clothing effects on motion capture accuracy and inter-trial variability in markerless systems. Publications Trends: Recent work emphasizes algorithm validation for gait analysis, VR-induced biomechanical changes, and tele-rehabilitation solutions. Over 20 peer-reviewed articles (2018–2025) highlight his contributions to clinical biomechanics and technology-driven healthcare. Grants & Labs: Involved in projects like O3DGA (machine learning in gait analysis) and SONIGAIT II. His team develops tools like GaitRec-VR for 3D gait analysis in real-world environments. Work focuses on improving clinical decision-making through explainable AI (XAI) and automated gait classification systems. Labs/Teams: Collaborates with multidisciplinary teams on tele-rehabilitation, motion variability analysis, and VR applications. Active in conferences like the World Congress of Biomechanics and European Society of Biomechanics.

• Environmental Engineering
• Water Resource Management
• Smart Water Infrastructure
• +۳ مورد دیگر

Univ.-Prof. Robert Sitzenfrei holds a professorship in the Department of Environmental Engineering at the University of Innsbruck. His research focuses on advancing smart water infrastructure through interdisciplinary applications of graph theory, artificial intelligence, and IoT technologies. He leads the Environmental Engineering Section, addressing challenges in water distribution systems resilience, leakage management, and urban drainage network optimization. Key research interests include: Graph-based methodologies for critical infrastructure analysis Explainable AI applications in water demand forecasting Smart sensor integration for real-time network monitoring Resilience enhancement of interdependent water systems Optimization of decentralized urban water networks Recent work emphasizes digital twin development for water systems, leveraging hydraulic modeling and machine learning to improve infrastructure reliability. Collaborations include the Smart Water Campus initiative, a real-world testbed for integrated smart water solutions. His contributions bridge computational methods with practical urban water challenges, addressing both technical and socio-technical aspects of resource management.

• Machine Learning for Visual Computing
• Big Data Visualization and Analytics
• Data Science and High-Performance Computing (HPC)
• +۴ مورد دیگر

Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology Kanpur (IITK) since 2022. He previously held positions at Los Alamos National Laboratory as a Postdoctoral Researcher (2018-2019) and Scientist II (2019-2022). Dr. Dutta earned his Ph.D. and M.S. in Computer Science from The Ohio State University (2011-2018) and a B.Tech in Electronics and Communication Engineering from the West Bengal University of Technology (2005-2009). His research lies at the intersection of Machine Learning , Visual Computing , Big Data Analytics , and High-Performance Computing (HPC) . He focuses on developing scalable solutions for extreme-scale data, such as exascale simulations, social media, IoT, and healthcare. His work emphasizes uncertainty quantification in AI models and interactive visualization techniques. Dr. Dutta’s recent publications highlight his expertise in in situ visualization for climate modeling, implicit neural representations for uncertainty-aware rendering, and statistical sampling for exascale systems. His funded projects include AI-driven data analytics frameworks and deepfake defense mechanisms supported by ISRO, SERB, and C3iHub. Scientific Awards include Best Reviewer (TVCG), Best Paper (ISAV, TopoInVis), and LAAP Award (LANL).

Philipp Krondorferمشاهده پروفایل

پژوهشگر

• Biomechanics
• Machine Learning
• Gait Analysis
• +۲ مورد دیگر

Philipp Krondorfer is a Junior Researcher at the Center for Digital Health and Social Innovation, University of Applied Sciences St. Pölten in Austria. His primary affiliation involves biomechanics research with a focus on integrating machine learning and AI for gait analysis. He collaborates with researchers including Brian Horsak and Anita Kranzl on musculoskeletal modeling projects. Research Focus Krondorfer specializes in: Machine learning applications for predicting joint contact forces and ground reaction forces Validation of markerless motion capture technologies Development of automated musculoskeletal simulations Biomechanical analysis of pathological gait patterns Explainable AI in clinical biomechanics His work bridges computer science and health sciences, emphasizing practical clinical applications. Publication Trends Krondorfer's recent articles (2023-2025) demonstrate a consistent focus on machine learning applications in biomechanics. Key themes include: Comparative studies of physics-informed AI models for force prediction Validation frameworks for smartphone-based motion capture Methods to improve accuracy in musculoskeletal simulations Analysis of joint mechanics in pathological conditions His collaborations frequently involve interdisciplinary teams from biomechanics and computer science departments. Awards and Recognition No scientific awards or honors are mentioned in available sources. Research Infrastructure Krondorfer works within the Center for Digital Health and Social Innovation, which focuses on technology-driven health solutions. The center likely provides motion capture laboratories and computational resources for biomechanical simulations, though specific lab details aren't provided.

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Logic Programming
• +۵ مورد دیگر

Thomas Eiter is a Full Professor at the Vienna University of Technology (TU Wien) in the Department of Knowledge-Based Systems, Faculty of Informatics. His research focuses on artificial intelligence, knowledge representation and reasoning, logic programming, computational logic, and neurosymbolic AI integration. He leads projects in declarative problem-solving, intelligent agent systems, and stream reasoning frameworks like LARS. Eiter has contributed to foundational work in answer set programming (ASP), algebraic reasoning, and their applications in scheduling, robotics, and real-time data processing. He has extensive international collaborations, including EU-funded projects like HumanE-AI-Net and the Austrian Science Fund (FWF) initiatives. His work emphasizes bridging symbolic AI with modern machine learning techniques, particularly in visual question answering and neural-symbolic systems. Eiter has supervised numerous PhD students and maintains active roles in academic leadership, including editorial boards of journals like Theory and Practice of Logic Programming . Key contributions include development of the DLVHEX system for hybrid knowledge representation, optimization frameworks for ASP, and methodologies for stream reasoning in dynamic environments. His research also addresses ethical AI through projects like the TAIGER initiative, focusing on training AI agents with ethical rules.