جست‌وجوی استاد و پژوهشگر

Friedrich Slivovskyمشاهده پروفایل

پژوهشگر

• Quantified Boolean Formulas
• Propositional Model Counting
• Knowledge Compilation
• +۵ مورد دیگر

Friedrich Slivovsky is a researcher at the Institute of Logic and Computation within the Faculty of Informatics at Technische Universität Wien (Vienna University of Technology). His work focuses on theoretical and practical aspects of computational logic, with particular expertise in Quantified Boolean Formulas (QBFs), Propositional Model Counting (#SAT), and Knowledge Compilation. His research interests span the theoretical foundations and practical applications of computational logic. Slivovsky investigates the complexity of logical reasoning problems, develops efficient algorithms for solving them, and creates practical tools that implement these theoretical advances. His work bridges the gap between theoretical computer science and practical applications in areas like hardware verification, artificial intelligence, and electronic design automation. Analysis of his publication trends reveals a consistent focus on QBF solving techniques, with increasing emphasis on circuit minimization, proof complexity, and practical solver engineering. His recent work (2023-2024) shows a strong focus on circuit minimization techniques, combining QBF and SAT approaches to solve complex optimization problems in hardware design. Earlier work (2019-2021) emphasized dependency schemes, certification methods, and theoretical foundations of QBF solving. Slivovsky leads several significant software projects that have become important tools in the computational logic community: Qute : A dependency learning QBF solver with GitHub repository showing active development (latest commit December 2024) Unique : A preprocessor for (D)QBF that computes unique Skolem and Herbrand functions Pedant : A certifying DQBF solver These projects demonstrate his commitment to translating theoretical advances into practical tools that benefit the broader research community.

• 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.

Sylvie Coste-Marquisمشاهده پروفایل

مدرس

• Knowledge Representation and Reasoning
• Artificial Intelligence
• Argumentation Frameworks
• +۵ مورد دیگر

Sylvie Coste-Marquis is a Lecturer at the Institute of Technology of Lens, part of the University of Artois. Her research focuses on knowledge representation, argumentation frameworks, and computational logic, particularly in artificial intelligence applications. PhD in Computer Science (1994) from Henri Poincaré University of Nancy Co-supervised three PhD theses on argumentation systems, QBF, and speech recognition Administrative roles: Vice-President for Digital Affairs (2016-), Project Manager (2012-2016), and Head of IT Department (2010-2013) Her work explores abstract argumentation, belief revision, and quantified Boolean formulae, with recent contributions to enforcing extensions via optimization and translating argumentation frameworks. She actively participates in program committees for top-tier conferences like IJCAI and COMMA. She has contributed to ANR projects on multi-agent argumentation, configurable product recommendation, and preference handling in combinatorial domains. Her teaching includes modules on computer systems, object-oriented design, and AI, with recognized innovations in educational methods.