جستجوی استاد راهنمای دانشگاهی

Georg Pölzlbauerمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Visualization
• Artificial Intelligence
• +۳ مورد دیگر

Georg Pölzlbauer is a researcher affiliated with the Department of Information and Software Engineering at Vienna University of Technology (TU Wien). His work focuses on machine learning, data visualization, and artificial intelligence, particularly leveraging self-organizing maps (SOM) for exploratory data analysis. He holds a Dipl.-Ing. (Diploma in Engineering) and a Dr.techn. (Doctor of Technical Sciences). His research emphasizes advanced visualization techniques for SOMs, including vector fields and graph-based methods, applied to diverse domains like petroleum data and political datasets. He has contributed to supervised learning algorithms inspired by self-organization, such as Decision Manifolds. Publications span algorithm design, cluster analysis, and pattern recognition, with applications in music feature extraction and industry-specific data visualization. No scientific awards are explicitly mentioned, but his work reflects sustained engagement with computational and visual data analysis. While no advising or grant information is provided, his affiliation with TU Wien’s research unit suggests active participation in collaborative academic projects.

• Cyber-Physical Systems
• Runtime Verification
• Formal Methods
• +۳ مورد دیگر

Dejan Nickovic is an Associate Professor in the Department of Cyber-Physical Systems at TU Wien. His primary affiliation is with the Research Area Cyber-Physical Systems (E191-01). Nickovic's research focuses on formal methods, runtime verification, and fault localization in cyber-physical systems (CPS). He has contributed to automated testing frameworks, specification mining, and formal validation techniques for embedded and reactive systems. His work emphasizes bridging the gap between theoretical formalisms and practical CPS applications. Notable research directions include mining specifications from data (e.g., timing diagrams), developing tools like TD-Magic and DeepSTL, and advancing hypernode automata for complex system modeling. He has also explored fault-injection methods (FIM) for Simulink models and mutation testing strategies for improving CPS reliability. Nickovic collaborates extensively with industry partners, as evidenced by his work on production-test coverage analysis in simulation environments. His research has been published in top-tier conferences and journals, focusing on topics like information-flow interfaces, hyperproperties, and adaptive testing strategies. He advises graduate students on CPS-related theses, including those on fault localization, analog-mixed signal verification, and failure explanation in CPS models. His contributions have advanced both academic and industrial applications of formal methods in CPS design and validation.

Thomas Eiterمشاهده پروفایل

استاد

• Answer Set Programming
• Knowledge Representation
• Logic Programming
• +۷ مورد دیگر

Thomas Eiter is a Professor at TU Wien's Institute of Logic and Computation. His research focuses on declarative programming paradigms, knowledge representation, and artificial intelligence. He leads projects in neurosymbolic systems, answer set programming (ASP), and stream reasoning, with applications in visual question answering, scheduling optimization, and semantic scene generation. Eiter has contributed to foundational work in ASP semantics, computational complexity, and hybrid reasoning frameworks. His work bridges logical formalisms with practical AI challenges, emphasizing explainability and scalability. Projects like ALASPO and neurosymbolic integration showcase his focus on advancing both theoretical and applied aspects of AI. Projects: HumanE AI Network, WASP, REWERSE Research Themes: Neurosymbolic AI, Answer Set Programming, Stream Reasoning Notable achievements include pioneering work on semiring-based reasoning frameworks and developing efficient ASP solvers like Alpha. His contributions span over 471 publications, emphasizing interdisciplinary applications in computer vision, robotics, and automated planning.

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Prof. Allan Hanbury is a full professor at TU Wien's Faculty of Informatics, leading the E-Commerce Research Area. His work focuses on domain-specific systems for information extraction and retrieval, with expertise in machine learning, natural language processing, and medical informatics. Key projects include developing tools like CRUISE-Screening for systematic literature reviews and VoMBaT for high-recall search evaluation. He coordinates initiatives in transparent automated content moderation and image mining for patents. His research bridges theoretical advancements with practical applications in healthcare, legal domains, and digital humanities. Publications span over 240 works, emphasizing reproducibility, explainability in AI, and cross-lingual systems. Active in organizing conferences like PatentSemTech and contributing to CLEF challenges. Advises on thesis topics involving hate speech detection, legal text analysis, and graph-based methods. Collaborates internationally on projects like CSMeD (clinical trial matching) and BRISE (legal corpus development). His work integrates NLP, IR, and data mining to address real-world challenges in information access and AI transparency.

Haisen Zhaoمشاهده پروفایل

استاد

• Computer Graphics
• Digital Fabrication
• Additive Manufacturing
• +۵ مورد دیگر

Haisen Zhao is a Professor at Shandong University (SDU), affiliated with the School of Computer Science and Technology and the Interdisciplinary Research Center. His research focuses on geometric processing and digital fabrication, including additive and subtractive manufacturing. He completed his PhD at SDU in 2018 under Baoquan Chen, followed by postdoctoral research at the University of Washington (with Adriana Schulz) and IST Austria (with Bernd Bickel). He has authored over 20 top-tier publications in venues like ACM SIGGRAPH/Asia and IEEE TVCG, and holds multiple patents. Education: PhD in Computer Science (2018, SDU), Master’s (2014, SDU), and Bachelor’s (2011, SDU). Key awards include the CCF Doctoral Dissertation Award (2019) and Shandong Natural Science Award (2020). He leads a research group focused on advanced manufacturing techniques, advising PhD and Master’s students in areas like robotics, computer-aided design, and fabrication optimization. Teaching responsibilities include courses on computer graphics, human-computer interaction, and advanced topics for both undergraduates and graduates. His lab actively explores hybrid manufacturing strategies, inverse design methods, and robotic fabrication systems. Recent projects include neural accessibility learning for CNC machining and generative design for microstructure fabrication.

• Pharmacophore Modeling
• Computer-Aided Drug Design
• Virtual Screening
• +۳ مورد دیگر

Thomas Seidel is a Professor in the Department of Pharmaceutical Sciences at the Faculty of Life Sciences. His research focuses on computational methods for drug discovery, including pharmacophore modeling, virtual screening, and ligand-receptor interaction analysis. He has contributed to the development of tools like GRADE/X-GRADE, PharmacoMatch, and QPhAR for enhancing the efficiency and accuracy of drug design processes. His work integrates molecular dynamics simulations, machine learning, and structure-based approaches to address challenges in neuropharmacology and medicinal chemistry. Key areas include GABAA receptor modulation, metabolite prediction, and the optimization of lead compounds for drug targets such as the large neutral amino acid transporter 1 (LAT1). Seidel’s collaborative projects span academic and industrial partnerships, with a focus on advancing predictive toxicology through initiatives like the NeuroDeRisk toolbox. His research consistently bridges computational methodologies with experimental validation, emphasizing translational applications in drug development.

• Machine Learning
• Data Mining
• Social Network Analysis
• +۴ مورد دیگر

Yllka Velaj is an Assistant Professor in the Faculty of Computer Science, affiliated with the Research Group on Data Mining and Machine Learning. Her research focuses on machine learning, data mining, and network analysis, with applications in social networks, recommendation systems, and AI-enhanced education. She actively contributes to advancing methodologies for uncertain graphs, game-based learning in IT project management, and educational technologies targeting non-computer science students. Her scholarly activities include organizing events such as the Austrian Computer Science Day 2024. Her work bridges theoretical computer science with practical applications, emphasizing learner-centered approaches in data science education and innovative solutions for complex network problems. Key research areas include node embedding, cluster analysis, and algorithm design for social influence maximization. She collaborates on projects addressing multi-relational graph clustering, semi-supervised learning, and optimizing communication in datacenters.

• Cybersecurity
• Code Obfuscation
• Malware Analysis
• +۳ مورد دیگر

Sebastian Schrittwieser is a Researcher in the Research Group Security and Privacy, part of the Faculty of Computer Science. His work focuses on cybersecurity, code obfuscation, malware analysis, and machine learning applications in security. He leads and contributes to projects like INODES (Cyber Defense Strategies) and EMRESS (Resilience Evaluation Models). His research bridges theoretical foundations and practical applications, addressing challenges in software protection and threat detection. Key research interests include: Code Obfuscation Techniques and Resistance Adversarial Machine Learning and Risk Assessment Malware Analysis and Program Simulation User Behavior in Cybersecurity Contexts Recent publications emphasize empirical studies on IT/OT infrastructure security, graph neural network vulnerabilities, and quantum-inspired machine learning. He actively collaborates with institutions like SBA Research and presents at international conferences. Grants include Research Funding for projects on optimal cyber defense strategies (INODES) and software resilience evaluation (EMRESS). His work aligns with interdisciplinary efforts in security engineering and privacy-preserving technologies.

Emanuele Rodolàمشاهده پروفایل

استاد

• Machine Learning
• Geometry Processing
• Computer Vision
• +۱ مورد دیگر

Emanuele Rodolà is a Full Professor of Computer Science at Sapienza University of Rome , leading the GLADIA group (Geometry, Learning and Applied AI) funded by an ERC Grant and a Google Research Award. Former Assistant (2017-2020) and Associate Professor at Sapienza Postdoc at USI Lugano (2016-2017), TU Munich (Alexander von Humboldt Fellow, 2013-2016), and University of Tokyo (JSPS Research Fellow, 2013) His research spans geometry processing , graph/geometric deep learning , computer vision , and language/sound processing , with over 100 publications. He has served on program/organizing committees of top conferences and founded successful workshops in machine learning and graphics. Ellis Fellow ERC Grant recipient Google Research Award Alexander von Humboldt Fellow JSPS Research Fellow

Mark Steedmanمشاهده پروفایل

استاد

• Computational musical analysis
• Computational linguistics
• Combinatory logic
• +۶ مورد دیگر

Mark Steedman is a Professor of Cognitive Science at the School of Informatics , University of Edinburgh, and an Adjunct Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research bridges Artificial Intelligence , Cognitive Science , and Computational Linguistics , with a focus on Combinatory Categorial Grammar (CCG) , Prosody and Intonation , and Temporal Semantics . He has led the Institute for Language, Cognition, and Computation and contributed to interdisciplinary research at the Human Communications Research Center and Centre for Speech Technology Research . Research Interests : Steedman's work explores the intersection of formal grammar, computational models, and cognitive processes. He investigates how CCG parsing can enhance semantic inference, how prosodic features improve speech processing, and the role of temporal semantics in language understanding. His projects often integrate language models with entailment graphs for question answering and dialogue systems. Scientific Awards : Fellow of the American Association of Artificial Intelligence (1993) Fellow of the Royal Society of Edinburgh (2002) Fellow of the British Academy (2002) Member of Academia Europaea (2006) Best Paper Awards at ACL 2023 and AACL/IJCNLP 2023 Influential Paper Award (IFAAMAS 2017) Recent Trends in Publications : His recent work emphasizes language models for semantic inference , entailment graphs in multilingual settings, and incremental parsing for brain-language interfaces. Papers address challenges in hallucination , cross-lingual transfer , and prosody-text alignment .

• Artificial Intelligence
• Artificial Neural Networks
• Information Systems
• +۳ مورد دیگر

Konstantin Schekotihin is an Associate Professor at the Department of Artificial Intelligence and Cybersecurity, Alpen-Adria University of Klagenfurt. His research focuses on artificial intelligence, machine learning, and semantic technologies with applications in industrial systems and semiconductor manufacturing. Reinforcement learning for industrial scheduling Answer Set Programming (ASP) and stream reasoning Failure analysis automation and ontology engineering Neuro-symbolic AI integration Knowledge-based systems in manufacturing Recent publications emphasize AI-driven optimization in semiconductor production, decomposition strategies for scheduling problems, and multi-agent systems for workflow management. His work combines symbolic reasoning with machine learning to address complex industrial challenges. Contact: Konstantin.Schekotihin@aau.at