جستجوی استاد راهنمای دانشگاهی

Alexey Ignatievمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Formal Methods
• Satisfiability Modulo Theories (SMT)
• +۳ مورد دیگر

Alexey Ignatiev is an Associate Professor in the Optimisation research group at Monash University's Faculty of Information Technology. Previously, he was a postdoctoral researcher and researcher at the University of Lisbon's Faculty of Sciences, focusing on SAT/SMT-based decision procedures. He holds a Ph.D. from the Matrosov Institute for System Dynamics and Control Theory (Russian Academy of Sciences), where his thesis explored parallel CDCL-BDD integration. His research emphasizes formal methods in AI, including explainable AI (XAI), SAT-based reasoning, and optimization for applications like software upgradability, model-based diagnosis, and fault localization. His work spans over 100 publications, with notable contributions to MaxSAT solving (RC2 solver), neuro-symbolic frameworks (NEUSIS), and rigorous explanations for machine learning models. He has collaborated extensively with institutions like the University of Lisbon and Monash University, contributing to advancements in formal verification and interpretable machine learning.

• 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.

• Formal Methods
• Model-Driven Engineering
• SAT/QBF Solving
• +۴ مورد دیگر

Martina Seidl is a researcher and principle investigator at TU Wien's Institut für Softwaretechnik und Interaktive Systeme (E188). Her main affiliation is within the Faculty of Informatics. She leads the FAME Project (Formalizing and Managing Evolution in Model-Driven Engineering), focusing on advancing formal methods in software engineering. Her research interests include formal verification techniques, SAT/QBF solving algorithms, model-driven engineering, and automated reasoning. She has contributed to advancements in quantified Boolean formula (QBF) solving, parallel computing methodologies for logical problems, and formal methods in software model analysis. Her work spans theoretical computer science and practical applications in automated theorem proving and model checking. Notable contributions include expansion-based QBF solving approaches, parallel solving frameworks, and feature-based classifications of formal verification techniques. Seidl co-organized the QBF Gallery initiative, which curates benchmark suites for quantified Boolean formula competitions. She has also published extensively on clause redundancy optimization, blocked clause analysis, and the integration of formal methods in educational contexts like UML@Classroom.

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.

Johannes Oetschمشاهده پروفایل

پژوهشگر

• Answer Set Programming
• Neural-Symbolic Computation
• Visual Question Answering
• +۳ مورد دیگر

Johannes Oetsch is a researcher at TU Wien's Forschungsbereich Knowledge Based Systems within the Faculty of Informatics. His work focuses on Answer Set Programming (ASP) , neuro-symbolic computing , and visual question answering systems . He holds a Diplom-Ingenieur (Dipl.-Ing.) and a Doctor of Technical Sciences (Dr.techn.) in informatics. Key research areas include: Integration of large language models with symbolic reasoning frameworks Optimization techniques in ASP for scheduling problems Explainability mechanisms for neuro-symbolic systems Recent work emphasizes visual question answering using graph-based representations and contrastive explanation methods. He has contributed to the development of ALASPO , an adaptive optimization framework for ASP solvers. His research also explores applications in manufacturing scheduling and automated testing of logic programs. Notable contributions include: Neuro-symbolic pipelines combining ASP with vision-language models Lexicographical makespan optimization in parallel machine scheduling Large-neighbourhood search strategies for ASP-based optimization

Nava Tintarevمشاهده پروفایل

استاد

• Explainable Artificial Intelligence
• Human-Computer Interaction
• Recommender Systems
• +۱۲ مورد دیگر

Nava Tintarev is a Full Professor in Explainable AI at Maastricht University, Department of Advanced Computing Sciences (DACS), Faculty of Science and Engineering. She also holds a visiting professorship at TU Delft. As Director of Research in DACS since 2023, she leads a department of 100 researchers and mentors junior faculty and PhDs. Her educational background includes a Magister in Computer Science from Uppsala University and a PhD from the University of Aberdeen, with early studies in Psychology in Australia. Nava's research focuses on human-centered AI, particularly explainable artificial intelligence (XAI), transparency, fairness, and user control in AI systems. Her work bridges Human-Computer Interaction and AI, emphasizing interactive explanations in recommender systems, search, and decision support. She investigates how users understand and trust AI advice, with applications in media, HR, and public discourse. Her recent publications show a strong trend toward mitigating cognitive biases (e.g., confirmation bias, urban bias), evaluating explanation effectiveness, and designing multimodal, user-adaptive interfaces. She explores logic-style explanations, stance detection, and long-term human-AI collaboration. Best Paper Award at CHI Best Paper Award at CHIIR Best Paper Award at Hypertext Best Paper Award at UMAP Best Paper Award at HCOMP Senior Member of ACM (since 2020) Nava has supervised numerous PhD students and leads major grants including the ROBUST program (~87M), EU ITN on Natural Language for XAI, and industry collaborations with IBM and Twitter. She is co-director of the ICAI TAIM lab on trustworthy media. She serves on editorial boards (UMUAI, TORS), program committees (ECAI, UMAP, RecSys), and advisory bodies including NWO and the incoming IPN board (2026). Her lab brings together interdisciplinary researchers in XAI, computational social science, causal inference, and affective computing.

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

• Artificial intelligence
• Computer Sciences
• Data & Knowledge Engineering
• +۳ مورد دیگر

Assoc.-Prof. Dipl.-Ing. Dr. Erich Christian Teppan is affiliated with the Department of Artificial Intelligence and Cybersecurity at Alpen-Adria-Universität Klagenfurt. His research areas include artificial intelligence, computer sciences, data & knowledge engineering, human-computer interaction, mathematical logic, and knowledge representation, processing, and management. Affiliation: Department of Artificial Intelligence and Cybersecurity University: Alpen-Adria-Universität Klagenfurt Research Interests: Focuses on interdisciplinary domains spanning artificial intelligence, cybersecurity, and human-computer interaction, with emphasis on knowledge representation, expert systems, and mathematical logic applications in data engineering.

• Innovation Sociology
• Sociology of Digital Work
• Artificial Intelligence
• +۵ مورد دیگر

Univ.-Prof. Dr. Uli Meyer is a prominent sociologist specializing in innovation, digitalization, and organizational studies. He currently serves as University Professor and Head of the Department of Sociology with a focus on Innovation and Digitalization (SID) at the Institute of Sociology, Johannes Kepler University Linz. His academic career spans multiple prestigious institutions including Technical University of Berlin, Munich Center for Technology in Society, and Ruhr University Bochum. Professor Meyer completed his doctorate in Sociology at Technical University of Berlin with the dissertation "Innovation Paths: Evolution and Institutionalization of Complex Technology." His interdisciplinary background in sociology, computer science, and economics informs his unique approach to studying technology-society relations. His research focuses on the social dimensions of digital transformation, particularly examining how organizations make sense of and implement technologies like Industrie 4.0. Professor Meyer investigates the interplay between technological innovation and social change, with particular attention to work organization, organizational dynamics, and the social construction of technological futures. His approach integrates theoretical perspectives from organizational sociology, institutional theory, and science and technology studies to understand how digitalization reshapes work practices, organizational structures, and societal relations. Professor Meyer's recent publications (2019-2024) demonstrate a consistent focus on organizational recombination processes, sensemaking around technological futures, and the everyday practices of digitalization. His work reveals how organizations navigate complexity and uncertainty in the digital age through creative adaptation rather than wholesale transformation. Organizational recombination and social change Sensemaking processes around technological futures Digital transformation of work practices Power dynamics in innovation ecosystems Methodological approaches to studying digitalization Throughout his career, Professor Meyer has secured research funding for projects examining digitalization and innovation. His leadership roles, including heading the Department of Sociology with a focus on Innovation and Digitalization at JKU Linz and directing the Post-Doc Lab Reorganizing Industries at TU Munich, demonstrate his standing in the field. He has collaborated with scholars across Europe on projects related to innovation society and technological transformation, contributing to both theoretical advances and practical understanding of how organizations navigate digital change.

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

Van Quoc Huynh is a Researcher at the Institute for Application-oriented Knowledge Processing, Johannes Kepler University Linz (JKU), Austria. His work focuses on advancing machine learning and data mining techniques with emphasis on rule-based systems and symbolic artificial intelligence. His primary research interests include Machine Learning , Data Mining , and Symbolic AI , specializing in rule extraction algorithms, classification systems, and knowledge representation. He develops novel approaches for efficient pattern mining and interpretable model construction, bridging neural and symbolic paradigms. Recent publications demonstrate a clear trajectory toward hybrid neural-symbolic models for classification tasks and memory-efficient rule mining architectures. His work addresses critical challenges in scalability, parallel processing, and interpretability within knowledge discovery systems, with strong emphasis on practical implementations for complex datasets. Dr. Huynh actively contributes to FFG-funded research projects including the ongoing Automated Rule Extraction and Interpretation from Symbolic Regression Trees (2025-2026) and completed PreMoBAF (2021-2025). He engages with the academic community through conference organization (e.g., 20th International Conference on Foundations of Digital Games, 2025) and technical presentations on frequent itemsets mining. He operates within JKU's Institute for Application-oriented Knowledge Processing, which provides interdisciplinary infrastructure for applied AI research focusing on practical knowledge processing solutions.

• Number Theory
• Uniform Distribution Theory
• Discrepancy Theory
• +۳ مورد دیگر

Christoph Baxa is an Associate Professor in the Department of Mathematics at the University of Vienna, where he has been a faculty member since 2000. His academic career at the University of Vienna began in 1990 as an Assistant Professor in the Department of Statistics, then moved to the Department of Mathematics in 1992, where he was promoted to Associate Professor in 2000. He maintains an active research profile and teaching load, with courses scheduled through 2025. Dr. Baxa completed his academic training at the University of Vienna, earning his M.A. in 1990 with a thesis on prime representing functions and Hilbert's Tenth Problem, his Ph.D. in 1994 on the discrepancy of sequences (nα), and his Habilitation in 2000 on estimates for discrepancies of special uniformly distributed sequences. His research focuses on Number Theory, particularly Uniform Distribution Theory, Discrepancy Theory, Continued Fractions, and Diophantine Approximation. His work often connects number theory with mathematical logic, as evidenced by his early research on Diophantine representations. His recent scholarly activity is dominated by book reviews for Monatsh. Math., covering diverse areas including algebraic geometry, number theory, history of mathematics, and mathematical education. Dr. Baxa has received several prestigious awards including the Ph.D. sub auspiciis Praesidentis rei publicae, the Würdigungspreis from the Austrian Ministry of Science and Research, the Jubiläums-Studienpreis from the Austrian Mathematical Society, and the Edmund und Rosa Hlawka Preis from the Austrian Academy of Sciences. He has held research grants from the Austrian Science Fund and was an Erwin Schrödinger Fellow at the University of Colorado at Boulder from 2001-2003. As a professional service, he has been a regular reviewer for Mathematical Reviews since 1996 and reviews books for Monatsh. Math. He is a member of numerous mathematical societies including the AMS, DMV, EMS, LMS, ÖMG, and SMF.