Answer Set Programming Professors and Supervisors in Germany

Michael Gertzمشاهده پروفایل

استاد

Heidelberg UniversityGermany

Michael Gertz is a full professor at Heidelberg University, leading the Data Science Group within the Faculty of Mathematics and Computer Science. He holds a diploma from TU Dortmund University and a Dr. rer. nat. from Leibniz University Hannover (1996). Before joining Heidelberg in 2008, he was faculty at the University of California, Davis (1997–2008). His research spans Data Science, Natural Language Processing, Machine Learning, Complex Networks, and Legal Technology, with applications in law, medicine, economics, and political sciences. Academic roles include Dean of the Faculty of Mathematics and Computer Science (2014–2016), Managing Director of the Institute of Computer Science (2016–2023), and co-speaker of the Helmholtz Information & Data Science School for Health (since 2019). He has held editorial roles at ACM Transactions on Spatial Algorithms and Systems, ACM Journal of Data and Information Quality, and The VLDB Journal. Research focuses on interdisciplinary projects, such as legal text analysis, network models, and scientific data management. He has presented widely, including keynote speeches on AI in law and legal technology. His work emphasizes exploring quantities in text, temporal networks, and event detection in social media. Professional activities include program committee memberships for conferences like SIGIR, EMNLP, and CIKM, as well as reviewing for funding agencies like the Helmholtz Society and DFG. He is a member of ACM, IEEE Computer Society (inactive since 2012), and Gesellschaft für Informatik (GI). Key projects include developing frameworks like EVELIN, CQE, and QuantPlorer, and datasets such as EUR-Lex-Sum and Klexikon. His contributions bridge computational methods and domain-specific applications, fostering innovation in data-driven solutions across disciplines.

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Judy Robertsonمشاهده پروفایل

استاد

Schloss Dagstuhl - Leibniz Center for InformaticsGermany

Judy Robertson is a prominent researcher in computing education and child-computer interaction with an extensive publication record spanning over two decades. She has published in top venues including Communications of the ACM, Computers & Education, and International Journal of Child-Computer Interaction. Her research focuses on how children interact with technology, particularly in educational contexts. Key areas include computational thinking development, game-based learning, AI education for children, and the relationship between cognitive development and computing education. She has made significant contributions to understanding how children perceive and interact with emerging technologies like smart speakers and embodied conversational agents. Robertson's work shows a consistent pattern of collaboration with researchers such as Judith Good, Katherine Howland, and Andrew Manches. Her publications reveal a strong commitment to designing technologies that support children's learning across various contexts including schools, hospitals, and informal settings like Girl Guiding. She has developed several educational games and tools, with a particular focus on how physical activity and cognitive development intersect with computing education. Her recent work has expanded into data science education for young learners and understanding children's conceptualizations of artificial intelligence. The trajectory of her research shows increasing attention to how emerging technologies can be designed specifically for children's cognitive and developmental needs.

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David Carralمشاهده پروفایل

پژوهشگر

Dresden University of TechnologyGermany

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.

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Philipp Hanischمشاهده پروفایل

پژوهشگر

Dresden University of TechnologyGermany

Philipp Hanisch is a Researcher at the Technische Universität Dresden within the Faculty of Computer Science , specifically in the International Center for Computational Logic (ICCL) . He serves as the scientific coordinator of the School of Embedded Composite Artificial Intelligence (SECAI) , overseeing its development. Hanisch holds a diploma in Computer Science from TU Dresden (2021) and previously worked as a Young Graduate Trainee at the European Space Agency before returning to academia. His research focuses on Knowledge Representation and Reasoning , Answer Set Programming , and broader aspects of Artificial Intelligence . Recent publications explore topics like chase termination in database systems and set simulation techniques in logic programming. He has contributed to projects such as indexing for Datalog materialization and collaborative efforts within the SECAI initiative. As part of the Knowledge-Based Systems research group led by Prof. Markus Krötzsch, Hanisch bridges theoretical advancements with practical applications in computational logic. His work reflects interdisciplinary engagement between formal methods and AI systems design.

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Alex Ivlievمشاهده پروفایل

پژوهشگر

Dresden University of TechnologyGermany

Alex Ivliev is a Research Associate and PhD student at the Technische Universität Dresden, affiliated with the International Center for Computational Logic (ICCL) within the Faculty of Computer Science. He is part of the Knowledge-Based Systems group led by Prof. Dr. Markus Krötzsch. His research focuses on optimizing rule-based systems, with expertise in database theory, logics, complexity theory, and natural language processing. Education: Master's Thesis (Diplom): Reliance-Based Optimization of Existential Rule Reasoning , TU Dresden (2021) Project Thesis: Parsing of Lexicalised Linear Context-Free Rewriting Systems via Supertagging , TU Dresden (2020) Research Interests: Alex's work centers on advancing rule-based systems, particularly through the development of the Nemo engine, which addresses scalability and versatility in Datalog and logic programming. His contributions span dependency analysis for ontologies, explainable AI via proof tree visualization, and applications in mass spectrum analysis using Answer Set Programming. Key Projects: Nemo: A scalable rule engine for efficient reasoning (https://knowsys.github.io/nemo-doc/) InnoSale: Leveraging AI for optimizing sales and planning in industrial products (https://www.innosale.eu/) Grants & Advising: As a PhD student under Prof. Krötzsch, Alex's work is supported by collaborative projects focusing on computational logic and rule-based systems. Labs/Teams: Active member of the Knowledge-Based Systems group and ICCL, contributing to interdisciplinary research in computational logic and AI.

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Sarah Alice Gagglمشاهده پروفایل

پژوهشگر

Dresden University of TechnologyGermany

Dr. Sarah Alice Gaggl is a Researcher at the International Center for Computational Logic (ICCL) within the Technische Universität Dresden , where she has served as Group Leader for Logical Programming and Argumentation since October 2020. She also leads the BMBF-funded project NAVAS - Navigation in the Solution Space of Answer Sets and was a Principal Investigator in the Collaborative Research Center 248 (CPEC) from 2019 to 2022. Her research centers on Abstract Argumentation , Answer Set Programming (ASP) , and Knowledge Representation . Doctorate in Computer Science, Vienna University of Technology, March 2013 Her work bridges Answer Set Programming with practical applications in Knowledge Representation and Nonmonotonic Reasoning , focusing on navigation in plan spaces, efficient algorithms for ASP, and multi-criteria answer set selection. She has supervised numerous theses on topics like Multi-Shot ASP , Abstract Argumentation Frameworks , and Reinforcement Learning in game environments. Recent publications highlight trends in Answer Set Navigation (e.g., PlanPilot , IASCAR ), Algorithm Refinement (e.g., Winning Snake ), and Rule-Based Argumentation (e.g., Grounding Rule-Based Argumentation ). Her contributions span Computational Logic , Automated Reasoning , and Software Systems . She has held editorial and organizational roles for journals and conferences, including the Argument & Computation journal, KR , IJCAI , and ICLP . Dr. Gaggl leads the Logical Programming and Argumentation group and has been affiliated with the Computational Logic Group at TU Dresden since 2013. Her teaching includes courses like Theoretical Computer Science & Logic and Advanced Problem Solving .

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Carmine Dodaroمشاهده پروفایل

دانشیار

University of TrierGermany

Carmine Dodaro is an active researcher in Answer Set Programming (ASP) at the University of Calabria's Department of Mathematics and Computer Science. With over 130 publications from 2011-2025, his work bridges theoretical advances in logic programming with practical healthcare applications. His primary research interests include: Answer Set Programming theory and implementation Compiler techniques for ASP solvers Healthcare scheduling optimization (operating rooms, nurse staffing, chemotherapy) Integration of ASP with other AI paradigms Real-world constraint satisfaction problems Dodaro's recent work demonstrates exceptional focus on healthcare applications of ASP, with multiple 2023-2024 publications addressing nurse scheduling, operating room management, rehabilitation planning, and nuclear medicine scheduling. His approach typically involves developing specialized ASP encodings that handle complex real-world constraints while maintaining computational efficiency. He maintains a highly productive collaboration network, particularly with Marco Maratea (52 co-authored papers), Mario Alviano (40), and Giuseppe Galatà (23), forming one of Italy's leading ASP research groups. His publications appear consistently in top venues including Theory and Practice of Logic Programming, IJCAI, AAAI, and specialized logic programming conferences. Dodaro has contributed significantly to both theoretical foundations (unsatisfiable core analysis, paracoherent reasoning) and practical implementations (WASP solver extensions, CNL2ASP translation tools). His 2024-2025 publications indicate ongoing research momentum with no signs of reduced activity.

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Ute Schmidمشاهده پروفایل

استاد

Freiburg University of EducationGermany

Professor Ute Schmid is a Full Professor of Cognitive Systems at the University of Bamberg, where she has been a faculty member since September 2004. She leads the Cognitive Systems Group within the Bamberg Center of AI (BaCAI), focusing on creating AI systems that generate human-like explanations and reasoning processes. Her research bridges cognitive science and artificial intelligence to develop methods for explanation generation, inductive programming, and interactive machine learning. Professor Schmid's work emphasizes practical applications of explainable AI across diverse domains including image classification, medical diagnosis, and educational technologies. Her research on contrastive explanations, near misses, and human-AI alignment has significantly advanced the field of XAI. She has also pioneered research on AI literacy, recognizing the growing importance of basic AI understanding for responsible tool usage by non-experts. Her publication record demonstrates exceptional productivity and impact, with numerous articles in top-tier venues including Nature Machine Intelligence, IEEE Transactions on Visualization and Computer Graphics, and the Journal of Web Semantics. Her 2025 paper 'Aligning generalization between humans and machines' represents a significant theoretical contribution to understanding human-machine cognitive alignment. Professor Schmid actively contributes to gender diversity research in computer science through studies examining why women pursue PhDs in the field. She has also made important contributions to computing education, investigating how students acquire programming skills and how AI tools like code generators are integrated into learning processes. As an educator and researcher, Professor Schmid maintains strong international collaborations, with co-authors spanning multiple countries and institutions. Her interdisciplinary approach is evident in her diverse publication venues and collaborative work that bridges computer science, cognitive science, education, and application domains.

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Jakub Kuzilekمشاهده پروفایل

پژوهشگر

Humboldt UniversityGermany

Dr. Jakub Kuzilek is a researcher at the Institute of Computer Science, Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin. He is a member of the "Didaktik der Informatik / Informatik und Gesellschaft" (Computer Science Education | Computer Science and Society) research group, also affiliated with the Educational Technology Lab at the German Research Center for Artificial Intelligence. He joined the CSES research group in January 2020 after previously working at the Faculty of Mechanical Engineering, CTU in Prague, and completing a 4-year research stay at the Open University. Dr. Kuzilek completed his Ph.D. in Biomedical Signal Processing using Blind Source Separation methods. His academic journey transitioned from biomedical signal processing to educational data mining when Professor Zdenek Zdrahal from the Knowledge Media Institute (Open University) invited him to work on data mining of student data in the OU Analyze project. Dr. Kuzilek's primary research interests focus on the intersection of machine learning and education. His work centers on Educational Data Mining (EDM) and Learning Analytics (LA), with particular emphasis on explainable AI applications in educational settings. He investigates student behavior in online learning systems, develops predictive models for student success, and explores methods for providing actionable feedback to learners. His research bridges the gap between signal processing techniques from his earlier work and modern educational technology applications, with a strong focus on practical implementations that can directly benefit students and educators. Analysis of Dr. Kuzilek's recent publications (2021-2024) reveals a clear focus on advancing Learning Analytics and Educational Data Mining. His work consistently explores how machine learning can be applied to understand and improve educational outcomes, with particular attention to explainability of predictive models. Key research threads include student success prediction using behavioral data, algorithmic approaches to student grouping, automated feedback generation, and the use of self-assessments in higher education. His methodological approach combines rigorous statistical analysis with practical educational applications, often using real-world educational datasets to validate his findings. Best paper award for "An Automatic Method for Holter ECG Denoising Using ICA" (2011) Second place in CINC Challenge 2011 for "Simple Scoring System for ECG Signal Quality Assessment on Android Platform" (2011) Dr. Kuzilek actively mentors students at various levels, supervising numerous bachelor's and master's theses on topics related to Learning Analytics, Educational Data Mining, and machine learning applications in education. His grant portfolio demonstrates sustained research activity, including leadership roles on projects funded by the Czech Science Foundation, University Development Foundation, and BMBF. His current research focuses on AI-supported personalization in vocational training and implementing AI-based feedback systems in higher education institutions. As a core member of the Computer Science Education | Computer Science and Society research group at Humboldt University, Dr. Kuzilek collaborates with colleagues including Prof. Dr. Niels Pinkwart and other researchers to advance the field of educational technology. His work bridges theoretical research in machine learning with practical applications in real educational settings, contributing to both academic knowledge and tangible educational improvements.

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