جست‌وجوی استاد و پژوهشگر

Joaquín Ariasمشاهده پروفایل

دانشیار

• Constraint Logic Programming
• Answer Set Programming
• Event Calculus
• +۴ مورد دیگر

Joaquín Arias is an Associate Professor at King Juan Carlos University since 2025. His career includes 2020-2025 as Assistant Professor at the same institution and prior pre-doctoral research at IMDEA Software Institute (2013-2020). He has collaborated with institutions like University of Texas at Dallas and Aalto University. Education: Ph.D. in Computer Science (2020, Universidad Politécnica de Madrid) M.Sc. in Computer Science (2015) B.Sc. in Computer Science (2014) M.Arch. in Architecture (2002) Research interests revolve around Constraint Logic Programming , Answer Set Programming , and their applications in Event Calculus , Stream Data Analysis , and Value-Aware Systems . He has developed frameworks like s(CASP) for non-grounded reasoning and Mod TCLP for tabled constraints. Recent articles demonstrate expertise in integrating Large Language Models with logic programming, automated legal reasoning , and real-time system verification . His work emphasizes explainability, constraint handling, and semantic coherence in AI systems. Scientific awards include the best paper prize at CAEPIA 2021 . He has contributed to proceedings as editor for ICLP workshops and authored numerous papers in TPLP , PADL , and AI & LAW .

Esra Erdemمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Answer Set Programming
• +۶ مورد دیگر

Esra Erdem is a Professor in the Department of Computer Science and Engineering at Sabanci University, Faculty of Engineering and Natural Sciences. Her research focuses on the mathematical foundations of knowledge representation, automated reasoning, and answer set programming, with applications in bioinformatics, logistics, cognitive robotics, and economics. She has developed frameworks for hybrid planning, spatial-temporal reasoning, and multi-agent coordination using logic-based methods. She earned her Ph.D. in Computer Sciences (2002) at the University of Texas at Austin under Vladimir Lifschitz. Her academic genealogy traces back to Nikolai Shanin, Pavel Aleksandrov, and Karl Weierstrass. Her recent publications emphasize answer set programming for robotics, including collaborative assembly, path finding, and spatial reasoning. She applies these techniques to real-world challenges in cognitive factories and rehabilitation robotics. Erdem co-edited proceedings for KR 2021 and ICLP 2019 Technical Communications. She serves as an academic advisor and has contributed to educational tools like ReAct! for AI planning in robotics.

Omar El Khatibمشاهده پروفایل

استادیار

• Artificial Intelligence
• Computer Science
• Answer Set Programming
• +۲ مورد دیگر

Omar El Khatib is an Assistant Professor in the Department of Mathematics and Computer Science at Loyola University New Orleans, where he joined in Fall 2017. Previously, he served as an Assistant Professor at Taif University for seven years and as a Software Developer at Epic Systems Corporation in Madison, WI for two years. His educational background includes: Ph.D. in Computer Science from New Mexico State University (2007) Dr. El Khatib's research focuses on Artificial Intelligence with specialized expertise in Answer Set Programming, Machine Learning, and Deep Learning. His work applies declarative programming techniques to solve complex combinatorial optimization challenges across industrial domains. His publication history reveals a sustained emphasis on Answer Set Programming applications, addressing problems like assembly line balancing, pixel puzzle resolution, and job shop scheduling. These works demonstrate the versatility of logic-based approaches for modeling constrained real-world systems in manufacturing and computational problem-solving. Scientific awards: No scientific awards mentioned in the provided text Student advising and grant details are not specified in the source material. The text also contains no references to research laboratories, collaborative teams, or ongoing funded projects.

• Operations Research
• Combinatorial Optimization
• Healthcare Logistics
• +۳ مورد دیگر

Paola Cappanera is an Associate Professor of Operations Research at the Department of Information Engineering (Università di Firenze), affiliated with the School of Engineering. She is a member of the IBIS Lab. Her expertise spans combinatorial optimization, healthcare logistics, telecommunication networks, and scheduling problems. She teaches courses such as Fondamenti di Ricerca Operativa , Combinatorial Optimization , and Ottimizzazione su Reti di Flusso at both undergraduate and graduate levels. Her research focuses on optimizing healthcare delivery systems, including home care services, surgical scheduling, and resource allocation. She also addresses challenges in telecommunication networks, such as latency-aware scheduling and routing. Recent work emphasizes robustness in home care planning, equitable resource distribution, and mitigating risks in hazardous material transportation. Her articles highlight advancements in decomposition methods for scheduling, genetic algorithms for network functions, and integration of healthcare service models. Key contributions include models for emergency department physician rostering and optimizing tele-assistance systems.

• Answer Set Programming
• Artificial Intelligence
• Cybersecurity
• +۱۲ مورد دیگر

Prof. Martin Gebser is a University Professor and Deputy Director at the Institute for Artificial Intelligence and Cybersecurity, University of Klagenfurt. His work bridges theoretical advancements in Answer Set Programming (ASP) with practical applications in industrial scheduling, semiconductor manufacturing, and explainable AI systems. Institute for Artificial Intelligence and Cybersecurity, University of Klagenfurt His research focuses on Answer Set Programming and its extensions for complex scheduling problems, particularly in semiconductor production. Key areas include: Multi-shot ASP solving for job-shop decomposition Hybrid AI systems integrating reinforcement learning and logic programming Explainable AI for battery health monitoring and semiconductor dispatching Recent publications emphasize temporal planning, constraint learning, and real-world data integration. He has developed customizable simulators and optimization frameworks for industrial applications.

• Constraint Logic Programming
• Answer Set Programming (ASP)
• Stream Data Analysis
• +۳ مورد دیگر

Joaquín Arias is an Associate Professor at the Universidad Rey Juan Carlos (URJC) in Madrid, Spain, since 2025. He previously served as an Assistant Professor at URJC (2020–2025) and as a Pre-Doctoral Researcher at the IMDEA Software Institute (2013–2020). He holds a Ph.D. in Computer Science (2020), a Master of Science in Computer Science (2015), a Bachelor of Science in Computer Science (2014), and a Master of Architecture (2002), all from the Universidad Politécnica de Madrid (UPM). His research focuses on constraint logic programming, answer set programming (ASP), and their applications in stream data analysis, spatial reasoning, and legal reasoning systems. His main research interests include (Constraint) Logic Programming, TCLP, s(CASP), and value-aware systems. He has developed frameworks like Modular TCLP and ATCLP for constraint integration and aggregate computation. Notable projects include applying s(CASP) to legal reasoning (s(LAW)), BIM modeling, and automated conversational agents using LLMs and ASP. His work emphasizes explainable AI and ethical alignment in automated decision-making. Key contributions include the Automated Legal Reasoning with Discretion framework (2024), a socialbot leveraging LLMs and ASP (2024), and a legal reasoning system for administrative processes (2024). He has contributed to proceedings of the ICLP workshops and edited volumes for conferences like AAAI and CAEPIA. His academic awards include the Best Paper Prize at CAEPIA 2021. Collaborations include the University of Texas at Dallas (2017), Aalto University (2019), and IMDEA Software Institute. His teaching includes promoting logic programming in computer science curricula, as highlighted in his 2023 SIIE conference paper.

• Answer Set Programming
• Knowledge Representation and Reasoning
• Cyber-Physical Systems
• +۳ مورد دیگر

Dr. Peter Schüller is a Professor affiliated with the Department of Knowledge-Based Systems at TU Wien (Vienna University of Technology). He holds the academic title of Privatdozent (Priv.-Doz.) and has a background in Technical Engineering (Dipl.-Ing. Dr.techn. / Bakk.techn.). His roles include academic research, consulting services for intelligent automation, and partnership with Potassco Solutions. He is based at Favoritenstrasse 11, Room HG0312, and can be contacted via peter.schueller@tuwien.ac.at or contact@peterschueller.com. Education : Master's Thesis: 'Reconstructing borders of manually torn paper sheets using integer linear programming' (2008) PhD (Dr.techn.) in Informatics Habilitation (Privatdozent) qualification Research Interests : Schüller specializes in declarative problem solving through Answer Set Programming (ASP), with focus areas including hybrid knowledge integration systems, inconsistency management, and applications in robotics, traffic optimization, and industrial automation. His work bridges theoretical advances in computational logic with practical implementations in enterprise software architecture and database systems. Projects & Grants : He has led projects funded by the Austrian Research Promotion Agency (FFG), Austrian Science Fund (FWF), and Vienna Science and Technology Fund (WWTF). Key projects include: 'Dynamic knowledge-based (re)configuration of cyber-physical systems' (2017–2020) 'Integrated Evaluation of Answer Set Programs' (2015–2018) 'Inconsistency Management for Knowledge-Integration Systems' (2009–2012) Consulting & Partnerships : Provides services in enterprise software design, database optimization, GDPR compliance, and hybrid knowledge systems. Official partner of Potassco Solutions. Collaborates on initiatives like AI4EU and HumanE-AI-Net. Labs & Teams : Active in TU Wien's Knowledge-Based Systems Group. Involved in developing the DLVHEX and Hexlite solvers.

Leroy Chewمشاهده پروفایل

پژوهشگر ارشد

• Proof Complexity
• Quantified Boolean Formulas
• SAT Solving
• +۳ مورد دیگر

Dr. Leroy Chew is a Research Fellow at the Institute of Logic and Computation at Technische Universität Wien. He specializes in theoretical computer science with focus areas in proof complexity, quantified Boolean formulas (QBF), and SAT solving. His educational background includes a PhD from the University of Leeds and postdoctoral research at Carnegie Mellon University. Dr. Chew's research explores the boundaries of computational complexity and formal verification systems. His current projects include developing novel proof systems for quantified formulas and expansion-based approaches for constraint satisfaction problems. He leads research funded by the ESPRIT Grant on QBF Proofs and Certificates. His publication record demonstrates consistent contributions to formal verification and computational logic, with recent advances in strategy extraction techniques and dual proof systems. He maintains academic collaborations across Europe and the United States, and has served on program committees for major conferences including SAT and QBF Workshops. Dr. Chew has received the EPSRC Postdoctoral Prize Research Fellowship and continues to develop computational tools for the research community, including proof generators and strategy extraction software.

Nysret Musliuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Optimization
• +۶ مورد دیگر

Nysret Musliu is an Associate Professor and Head of the Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling at TU Wien (Vienna University of Technology). He holds a PhD in Computer Science from TU Wien (2001) and a Habilitation (2007). His research focuses on AI techniques, optimization, scheduling, and timetabling, with applications in workforce scheduling, combinatorial optimization, and constraint satisfaction. Education: PhD in Computer Science, TU Wien, 2001 Habilitation in Applied Computer Sciences, TU Wien, 2007 MSc (Dipl.-Ing) in Computer Science, University of Prishtina, 1996 Research Interests: His work addresses AI-driven solutions for scheduling problems, including staff scheduling, timetabling (e.g., high school timetabling), and combinatorial optimization. He has developed algorithms for tree/hypertree decompositions and hybrid methods combining constraint programming with metaheuristics. Grants & Leadership: Project leader of the Christian Doppler Laboratory (2017–2024) Funded projects: ARTE (employee scheduling), Softnet Austria, Hypertree Decompositions Professional Activities: Conference chairs for CPAIOR 2021/2020 and PATAT 2018 Editorial roles in Constraints Journal and Journal of Mathematical Modelling and Algorithms Program committee member for IJCAI, AAAI, and GECCO Teaching: He teaches courses on artificial intelligence, problem-solving techniques, and machine learning at TU Wien. Lab & Teams: Leads the Christian Doppler Laboratory and collaborates with industry partners like Bosch and Ximes Corp.

• Logic Programming
• Computational Complexity
• Nonmonotonic Reasoning
• +۴ مورد دیگر

Mantas Simkus is an Assistant Professor at TU Wien's Institute of Logic and Computation, affiliated with the Database and Artificial Intelligence Group. He previously held an Associate Professor position at Umeå University (Sweden) within the Wallenberg AI, Autonomous Systems and Software Program (WASP). He leads the FWF-funded project 'KtoAPP: Compiling Knowledge into Applications' and contributes to the Cluster of Excellence 'Bilateral Artificial Intelligence'. His research focuses on logic-based data management, knowledge representation, and nonmonotonic reasoning, with applications in semantic web technologies and ontology engineering. Education: Bachelor's in Computer Science from Vilnius University. Research interests include logic programming, computational complexity, description logics, and their integration with databases. He explores techniques for efficient query answering, schema validation (e.g., SHACL), and reasoning under incomplete information. His work bridges theoretical foundations (e.g., complexity analysis, formal semantics) with practical systems (e.g., ontology-mediated query processing). Key projects include 'KtoAPP' (2018–2025), investigating automated knowledge compilation, and contributions to the 'SemDat' and 'OMEGA' initiatives. He teaches courses on deductive databases and semi-structured data at TU Wien. He actively participates in academic service: co-chair of RuleML+RR 2024, editorial board member of the Artificial Intelligence journal, and former co-chair of DL 2019. His research group collaborates on topics like graph databases, answer set programming, and hybrid knowledge representation systems.

Stefan Szeiderمشاهده پروفایل

استاد

• Algorithms
• Computational Complexity
• Theoretical Computer Science
• +۴ مورد دیگر

Stefan Szeider is a full professor and chair of the Algorithms and Complexity Group at the Faculty of Informatics, Technische Universität Wien (TU Wien). He also serves as a visiting scientist at UC Berkeley's Simons Institute for the Theory of Computing. His academic journey includes positions at the University of Durham (UK) and the University of Toronto (Canada), and he earned his Mathematics PhD from the University of Vienna in 2001. Dr. Szeider's research focuses on designing efficient algorithms for problems in Artificial Intelligence, automated reasoning, and combinatorial optimization. He leads several initiatives, including the Vienna Center for Logic and Algorithms (VCLA), and has secured funding from the ERC, EPSRC, FWF, and others. His Erdős number is 2, reflecting his collaborative network in mathematics and computer science. Key achievements include the first ERC Starting Grant awarded to an Austrian computer scientist (2009), and awards such as the Highlighted Paper Award at SAT 2023 and Best Paper at CP 2020. He advises numerous PhD students and postdocs, fostering the next generation of researchers in algorithms and complexity. Notable contributions extend beyond academia to public outreach, including initiatives like the 'Algorithms Think Differently' educational program and the 'Algorithms in 60 Seconds' video competition. His work bridges theoretical foundations and practical applications, influencing both academic and real-world computational challenges.

David Carralمشاهده پروفایل

پژوهشگر

• Knowledge Representation
• Database Theory
• Artificial Intelligence
• +۳ مورد دیگر

Dr. David Carral is a former Research Associate at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at TU Dresden. His work focuses on Knowledge Representation , Database Theory , and Computational Logic , particularly in the context of ontology-based data access, existential rules, and chase termination analysis. He contributed to projects like CFAED and collaborated on systems such as VLog and ROWL . David supervised theses including "Don’t Repeat Yourself: Termination of the Skolem Chase on Disjunctive Existential Rules" by Lukas Gerlach. His research bridges formal methods with practical applications in AI and database systems. Research interests include ontology design patterns , query rewritability , and efficient reasoning algorithms . He actively participated in conferences like KR and IJCAI, contributing to advancements in nonmonotonic reasoning and hyperproperties. His work integrates description logics, answer set programming, and semantic web technologies to address challenges in knowledge-based systems.

Marcello Balducciniمشاهده پروفایل

دانشیار

• Knowledge Representation & Reasoning
• Ontologies
• Agent Architectures
• +۶ مورد دیگر

Marcello Balduccini is the Department Chair and Associate Professor of Decision and System Sciences at Saint Joseph's University's Erivan K. Haub School of Business. His research focuses on knowledge representation & reasoning, ontologies, agent architectures, and cybersecurity applications in cyber-physical systems (IoT) and cognitive robotics. He previously held roles as an Assistant Research Professor at Drexel University and Principal Research Scientist at Kodak Research Labs. Research Interests: Knowledge Representation & Reasoning Cyber-Security and Cyber-Analytics Ontology-Based Systems Natural Language Understanding Constraint Satisfaction Problems Trustworthiness in AI/Robotics Recent work emphasizes explainable AI (XAI) systems for Answer Set Programming (ASP), cybersecurity frameworks, and formal methods for cyber-physical systems. His over 100 publications span conferences like LPNMR and ICLP, addressing topics from actual causation to autonomous UAV mission planning. Dr. Balduccini has organized international conferences and received grants supporting AI research, including travel grants for knowledge representation conferences. His work bridges theoretical advancements with practical applications in smart grids, supply chain management, and SDG-aligned AI systems.

• Declarative Programming Languages
• Logic Programming
• Answer Set Programming
• +۷ مورد دیگر

Dr. Ajay Bansal is a Teaching Professor and Associate Program Chair of the Software Engineering program at Arizona State University's School of Computing and Augmented Intelligence. He joined ASU in 2011 and previously served as a visiting assistant professor at Georgetown University. His academic journey includes a Ph.D. in Computer Science from the University of Texas at Dallas, an MS from Texas Tech University, and a B.Tech. from NIT Warangal. His research focuses on declarative programming languages, including Logic Programming, Answer Set Programming, and Knowledge Representation. He also explores Computing Education, particularly Individualized Learning and Game-based approaches. Notable achievements include the Test of Time Award at ICLP 2016 for coinductive logic programming and multiple teaching awards, including the Fulton Teaching Excellence Award (2019, 2022). Dr. Bansal has over 80 peer-reviewed publications spanning journals, conferences, and book chapters. His work bridges theoretical contributions (e.g., stableKanren integration) and practical applications like automated assessment systems. He actively contributes to courses such as Intro to Programming Languages and Database Management. Education: Ph.D. Computer Science, University of Texas-Dallas (2007) M.S. Computer Science, Texas Tech University (2002) B.Tech. Computer Science and Engineering, NIT Warangal, India (1999) Awards: Test of Time Award (ICLP 2016) Fulton Teaching Excellence Award (2019, 2022) Best Teacher Award (Top 5%) (6x) Advising & Grants: While no specific student names are listed, his roles involve academic advising and curriculum development. Industry experience includes software engineering roles at SIEMENS India, Metallect Corp., and Tyler Technologies. Labs/Teams: Engaged in educational technology initiatives and declarative programming research groups.

• 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.