جست‌وجوی استاد و پژوهشگر

• Forest Engineering
• Remote Sensing
• Digital Transformation in Forestry
• +۳ مورد دیگر

Christoph Gollob is a researcher at the Institute of Forest Growth within the Department for Economics and Social Sciences at the University of Natural Resources and Life Sciences, Vienna . His work focuses on integrating advanced technologies like LiDAR and personal laser scanning (PLS) into forestry operations for improved inventory, monitoring, and safety. Gollob leads projects such as Digitization of round wood measurements along the forestry/wood value chain using laser scanning (2024–2027) and contributes to subprojects like Lidar based forest monitoring and harvesting planning (2023–2026). Research Interests: Digital transformation in forestry, remote sensing, forest engineering, tree species classification, and sustainable forest management. Projects: 3 active or completed research projects funded by Austrian federal ministries and the Austrian Research Promotion Agency (FFG). Advising: Supervised 13 theses (7 master’s, 6 diploma) related to laser scanning applications, timber measurement, and forest safety. Media Appearances: 16 media mentions (print, web) discussing digital forestry solutions, including interviews and public outreach on forest monitoring and safety. Presentations: 78 lectures across 10 countries (Austria, USA, Italy, Germany, etc.), with keynotes on digital forestry and human-centered AI. Publications: 106 publications, including high-impact journal articles and conference proceedings on LiDAR, forest inventory, and smart sensor systems.

Valentine Ananikovمشاهده پروفایل

استاد

• Mechanistic studies of catalytic reactions
• Sustainable chemical processes
• Transition-metal-catalyzed transformations for organic chemistry
• +۶ مورد دیگر

Valentine Ananikov is a Professor at Moscow State University and Head of the Division of Structural Studies at the Zelinsky Institute of Organic Chemistry. Elected as a Member of the Russian Academy of Sciences in 2008 and Academia Europaea in 2018, his work focuses on catalysis, nanoparticle chemistry, and sustainable processes. Research Interests : Mechanistic studies of chemical reactions, development of nanoscale and molecular catalysts, biomass conversion, and AI integration in chemistry. His team combines experimental and theoretical methods to advance green chemistry. Scientific Awards : 2024 International Markovnikov Prize 2023 "Gravity" International Prize for AI research 2016 Organometallics Distinguished Author Award 2016 Hitachi High-Technologies Award 2004 Russian State Prize for Young Scientists Notable Contributions : Pioneering hybrid catalytic systems (homogeneous/heterogeneous), sustainable catalyst design from biomass, and AI applications in reaction discovery. His work addresses environmental safety and carbon-neutral chemical processes.

Philipp Grohsمشاهده پروفایل

استاد

• Mathematical Data Science
• Deep Learning Algorithms
• High-Dimensional Partial Differential Equations
• +۷ مورد دیگر

Prof. Dr. Philipp Grohs is a full professor of Mathematical Data Science at the University of Vienna and head of the Mathematical Data Science group at RICAM (Austrian Academy of Sciences). He holds a MSc from TU Vienna (2006) and a PhD from the same institution (2007). After postdoc positions at TU Graz, KAUST, and ETH Zurich, he became an assistant professor at ETH Zurich in 2011 before moving to the University of Vienna in 2016. His research focuses on designing efficient algorithms for signal processing, computational sciences, and finance, with recent contributions to understanding deep learning algorithms and solving high-dimensional PDEs using machine learning. He has received the ETH Latsis Prize (2014) and was selected for an Alexander von Humboldt Professorship (2019). Research Interests: Mathematical foundations of deep learning High-dimensional PDEs and their numerical solutions Signal and image processing (phase retrieval, Gabor systems) Approximation theory and function spaces Computational finance and mathematical modeling Recent Research Trends: His work explores theoretical guarantees for neural network performance, particularly in overcoming dimensionality challenges. Notable contributions include phase retrieval algorithms, analysis of DNN expressivity, and applications of deep learning in quantum chemistry and epidemiology modeling. Awards: ETH Latsis Prize (2014) Alexander von Humboldt Professorship (2019) Advising & Projects: Leads research initiatives such as the 'Data Science' hub at Vienna, and has coordinated projects on hybrid computational sciences and explainable AI models. Active in supervising interdisciplinary research teams across mathematics and computer science. Labs/Teams: Directs the Mathematical Data Science group at RICAM and oversees computational research collaborations with institutions like KAUST and ETH Zurich. Engages in applied projects like group testing strategies for SARS-CoV-2 and neural network-based electronic structure calculations.

Pascal Schöttleمشاهده پروفایل

استاد

• IT Security
• Machine Learning
• Adversarial Examples
• +۶ مورد دیگر

Pascal Schöttle is a Professor for IT Security and Machine Learning at the Department of Digital Business & Software Engineering at MCI - The Entrepreneurial University in Innsbruck. He previously held roles including Associate Professor (2020-2022), University Lecturer (2018-2020), and Post-Doc positions at the University of Innsbruck and University of Münster. His academic journey includes a PhD in Computer Science from the University of Münster (2014), and prior studies in IT Security and Mathematical Engineering. Research focuses on adversarial machine learning, IoT security, blockchain applications, and game-theoretic approaches to cybersecurity. Notable projects include leading the Josef Ressel Center for Security Analysis of IoT Devices (2023-present) and managing the SMiLE project on encrypted machine learning (2021-2023). Awards include the IEEE Best Student Paper (2012) and CAST IT Security Runner-Up (2008). Education: PhD (Computer Science, 2014), MSc (IT Security, 2011), BSc (Mathematical Engineering, 2008) Teaching: Courses include IT Security, Machine Learning, Algorithms & Data Structures, and Blockchain Grants: FFG (2021-2023), FWF (2019-2023), University of Innsbruck (2017-2019) Labs/Teams: Research Center CDG, Josef Ressel IoT Security Group

Markus Schedlمشاهده پروفایل

استاد

• Recommender Systems
• Information Retrieval
• Algorithmic Fairness
• +۶ مورد دیگر

Markus Schedl is a Full Professor at Johannes Kepler University Linz , Austria, where he leads the Multimedia Mining and Search (MMS) group within the Institute of Computational Perception . He also heads the Human-centered Artificial Intelligence (HCAI) group at the Linz Institute of Technology (LIT) AI Lab . Education: Computer Science (TU Wien), PhD (JKU Linz), Master's in International Business Administration (WU Wien, University of Gothenburg) Research Interests span recommender systems , information retrieval , algorithmic fairness , user modeling , and machine learning . His recent publications focus on: Multimodal and hybrid AI for human-centric personalization Context-aware music recommendation and emotion recognition Graph neural networks for session-based recommendation Technical and regulatory compliance in fair, transparent, and privacy-preserving systems Projects are funded by the Austrian Science Fund (FWF) , Austrian Research Promotion Agency (FFG) , and European Commission (EC) . He maintains industry collaborations with Siemens, Spotify, and Deezer. Teaching includes courses like Introduction to Machine Learning and Social Media Mining and Analysis at JKU, with guest lecturing experiences at Pompeu Fabra, Queen Mary London, and KTH Stockholm. Labs & Teams: MMS group at JKU's Institute of Computational Perception and HCAI group at LIT AI Lab.

• Theoretical Computer Science
• Complexity Analysis
• Automated Reasoning
• +۴ مورد دیگر

Univ.-Prof. Dr. Georg Moser is a Professor in the Department of Computer Science at the University of Innsbruck. His research focuses on theoretical computer science, complexity analysis, automated reasoning, and formal methods. He leads the Theoretical Computer Science (TCS) group, with expertise in term rewriting systems, programming language semantics, and algorithmic learning theory. Key research areas include: complexity analysis of programs via rewriting techniques, automated tools for resource analysis (e.g., ATLAS), and foundational work on proof theory and logic. His work bridges theory and practice, addressing challenges in program verification and probabilistic systems. Selected publications (2021–2025) highlight advancements in reinforcement learning, quantum program analysis, and modular rule-based systems. He teaches courses like 'Discrete Mathematics' and 'Introduction to Theoretical Computer Science'.

• Recommender Systems
• Music Information Retrieval
• Data Mining
• +۳ مورد دیگر

Assoc. Prof. Dr. Eva Zangerle is an Associate Professor at the Department of Computer Science, University of Innsbruck. Her research focuses on Recommender Systems, Music Information Retrieval, and Natural Language Processing. She leads projects on hypergraph-based recommendation, emotion-driven music systems, and NLP tasks like authorship analysis. Notable contributions include the PAN workshop series, the EMMA emotion database, and studies on algorithm fairness. Her work integrates machine learning with interdisciplinary applications, such as analyzing lyrical complexity trends and detecting generative AI content. She organizes international workshops like PERSPECTIVES on Recommender Systems Evaluation and contributes to initiatives like the MediaEval challenges. Her research bridges technical innovation with societal impact, addressing ethical AI and user-centric evaluation methodologies. Eva Zangerle is affiliated with the DBIS group and actively publishes in top-tier conferences/journals. Her lab develops open-source tools for music recommendation and text analysis, emphasizing reproducibility and real-world applicability.

Klaus Muellerمشاهده پروفایل

استاد

• Visual Analytics
• Explainable Machine Learning and AI
• Algorithmic Fairness and Transparency
• +۷ مورد دیگر

Klaus Mueller is a Professor in the Computer Science Department at Stony Brook University , with additional appointments in Biomedical Engineering and Radiology. He serves as Director of the Visual Analytics and Imaging (VAI) Lab, Liaison for the SUNY Korea CS Program, and Interim Chair of the Department of Technology and Society . His career spans roles at Brookhaven National Lab and leadership positions at SUNY Korea. Dr. Mueller earned his PhD in Computer and Information Science (1998), MS in Computer and Information Science (1996), and MS in Biomedical Engineering (1990) from The Ohio State University , alongside a BS in Electrical Engineering (1987) from the Polytechnic University of Ulm, Germany. His research focuses on visual analytics , explainable AI , algorithmic fairness , computational imaging , and medical imaging . He has pioneered GPU-accelerated CT reconstruction techniques, bias mitigation frameworks (e.g., D-BIAS), and tools like DOMINO for causal reasoning. His work bridges data science , human-computer interaction , and medical applications , often integrating large language models for visualization tasks. Recent publications highlight advances in multivariate volume rendering , LLM-driven bias detection , and mDDPM-based medical image synthesis . His articles span IEEE Transactions , Nature Machine Intelligence , and conferences like IEEE VIS and ACM CHI . Award highlights include NSF CAREER (2000), SUNY Chancellor Award (2011), IEEE Golden Core Award (2016, 2022), induction into the National Academy of Inventors (2018), and elevation to IEEE Fellow (2024). He has chaired major conferences and served as Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics (2019-2022). He teaches graduate and undergraduate courses in visualization , medical imaging , and GPGPU programming , and leads the Visual Analytics Seminar (CSE 648). His lab ( VAI Lab ) fosters interdisciplinary research in GPU-accelerated analytics and ethical AI.

• Artificial Intelligence
• IT Security
• Cyber-Physical Systems
• +۹ مورد دیگر

Dipl.-Ing. Oliver Eigner BSc is a Researcher in IT Security and Coordinator for AI & Digital Transformation at St. Pölten University of Applied Sciences' Department of Computer Science and Security. He holds a Master's degree in Information Security and Bachelor's degree in IT Security from the same institution. Master Information Security (2014-2016), St. Pölten University of Applied Sciences Bachelor IT Security (2011-2014), St. Pölten University of Applied Sciences HTL Ybbs a.d. Donau, Network Technology (2011) His research focuses on Trustworthy AI , Cyber-Physical Systems Security , and Resilient Artificial Intelligence . He leads projects like TrustAI (2026), FAIRAI, and Secure Supply Chains for Critical Systems (SSCCS). His work bridges AI ethics , security in multimodal logistics , and cybersecurity education . Recent publications demonstrate his expertise in AI security frameworks (2024), active learning applications (2024), and environmental monitoring via microwave links (2022). He contributes to conferences like Machine Learning Prague and IEEE COINS. Contact: oliver.eigner@fhstp.ac.at

• Cybersecurity
• Artificial Intelligence Security
• Blockchain Technologies
• +۴ مورد دیگر

Peter Kieseberg is a Lecturer and Senior Researcher at the St. Pölten University of Applied Sciences , affiliated with the Institute for IT Security Research and Department of Computer Science and Security . He has held his lecturer position since November 2017 and contributes to research in Cybersecurity , Artificial Intelligence , and Blockchain Technologies . His research spans AI Security , Cyber Resilience , Data Privacy , and Explainable AI . He has co-authored numerous publications on topics such as Secure Drone Integration , AI Procurement Guidelines , and Blockchain-based Auditing . His work often bridges technical and legal domains, emphasizing Ethics and Transparency . Key Trends: Recent articles focus on Cyber Resilience Fundamentals , Controllable AI , and Risk Factors in AI Applications . Projects: Active in initiatives like A3 (AI Act for Austria) , Dataskop , and Josef Ressel Center for Blockchain Technologies . Education: Studied Technical Mathematics in Computer Science at TU Wien (2001–2007).

• 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.

• Artificial Intelligence
• Knowledge Engineering
• Machine Learning
• +۳ مورد دیگر

Konstantin Schekotihin is an Associate Professor at the Institute of Artificial Intelligence and Cybersecurity , part of the Faculty of Technical Sciences at Alpen-Adria-Universität Klagenfurt. His research focuses on AI-driven solutions for semiconductor manufacturing, failure analysis, and multi-agent systems. He specializes in reinforcement learning, scheduling optimization, and neuro-symbolic AI applications. His work bridges theoretical advancements with industrial challenges such as semiconductor fab automation and real-time process monitoring. He has collaborated with organizations like the Austrian Research Promotion Agency (FFG) and Infineon Technologies. Key research areas include: AI for semiconductor failure analysis workflows Reinforcement learning in manufacturing scheduling Multi-agent systems in immersive VR environments Neuro-symbolic AI for defect routing Predictive modeling for equipment maintenance Recent projects involve optimizing ion implantation processes, developing FA ChatBots, and improving social presence in VR simulations. His work emphasizes scalability, explainability, and real-world applicability of AI systems.

Michael Reiterمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Pattern Recognition
• Computer Vision
• +۴ مورد دیگر

Michael Reiter is a Senior Lecturer at the Institute of Visual Computing & Human-Centered Technology, part of the Faculty of Informatics at TU Wien. His research focuses on Machine Learning, Pattern Recognition, and Computer Vision, particularly in medical applications such as flow cytometry data analysis for leukemia diagnosis and minimal residual disease (MRD) assessment. He leads the MyeFLOW project (2020–2025), which develops automated methods for immunophenotyping and MRD detection in pediatric leukemia. His work bridges machine learning and clinical diagnostics, emphasizing explainable AI in healthcare. Reiter collaborates with medical institutions to translate algorithms into clinical tools, such as the FlowMe decision support system. His research interests include transformer networks, UMAP visualization, and Gaussian mixture models for biomedical data. He has supervised diploma theses on topics like meta learning for blast cell classification and U-Net-based flow cytometry analysis. Reiter’s publications span over two decades, with recent emphasis on medical AI applications. His lab, the Computer Vision Lab, focuses on visual computing for healthcare, robotics, and cultural heritage digitization. Current projects address automated analysis of museum herbarium collections and empathic robotic systems for autism therapy.

Cem Ersoyمشاهده پروفایل

استاد

• Computer networks
• Pervasive computing
• Edge computing
• +۱ مورد دیگر

Cem Ersoy is a Professor at Bogazici University 's Department of Computer Engineering, with a career spanning over 30 years. He has held visiting positions at the University of Trento, Italy, and previously served as Department Head at Bogazici University multiple times. Education : Ph.D. in Electrical Engineering (Polytechnic University, 1992), M.S. in Electrical Engineering (Bogazici University, 1986), B.S. in Electrical Engineering (Bogazici University, 1984) His research focuses on computer networks , pervasive computing , edge computing , and wireless networks . Recent work highlights AI integration in air computing, stress monitoring with wearables, and edge network optimization. Articles span applications of multi-agent DRL, probabilistic tracking, and network slicing. Scientific awards : Elected to the Academy of Sciences of Turkey (2022) Bogazici University Research Excellence Awards (Junior 2007, Senior 2014) He has supervised 24 PhD and 58 MS theses. As principal investigator, he led 35 funded research projects by agencies such as the EU, TUBITAK, and Bogazici University Research Fund. His Google Scholar profile shows 11,856 citations, h-index 43, and i10-index 130.

• X-ray Computed Tomography
• Non-destructive Testing
• Deep Learning
• +۴ مورد دیگر

Patrick Weinberger is a Researcher at the University of Applied Sciences Upper Austria, affiliated with the School of Engineering and the Focal Area ICT - Information & Communications Technology. He leads and collaborates on multiple research initiatives including the VISION project on AI-driven non-destructive testing and HyperMAT for hyperspectral material characterization. His research focuses on advancing X-ray computed tomography analysis through deep learning and augmented reality visualization. Key areas include U-Net architectures for industrial inspection, explainable AI in non-destructive testing, and immersive material analysis systems. His work bridges computer vision with practical industrial applications in aerospace and automotive sectors. Weinberger actively contributes to 4 major research projects including VISION (2025-2028) developing AI systems for non-destructive testing, PEMOWE for energy transition testing networks, HyperMAT for hyperspectral material characterization, and WIFI for future welding industry systems. He operates within the Assistive Technology Lab and Center of Excellence Automotive/Mobility at the Wels campus. Research Output Highlights: 13 total publications (5 articles, 3 conference papers, 2 contributions) Key innovations in U-Net interpretability and AR-based CT visualization Strong focus on industrial AI transfer to manufacturing Technical Collaborations: Primary collaborator with Senck, Kastner, and Fröhler Network spans 6 similar research profiles in deep learning and CT analysis Active in EU Interreg Bavaria-Austria programs