Find Academic Supervisors

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Logic Programming
• +5 more

Tomi Janhunen is a Professor in Computing Sciences at Tampere University, part of the Faculty of Information Technology and Communication Sciences. His expertise spans Artificial Intelligence, Logic Programming, and Theoretical Computer Science. He actively contributes to academic leadership roles, serving on boards of the Finnish Association of Computer Science and Finnish Artificial Intelligence Society. His research focuses on Answer Set Programming (ASP), probabilistic reasoning systems, explainable AI, and optimization techniques applied to cybersecurity and maintenance scheduling. Key grants include the XAILOG and AI-ROT projects funded by the Research Council of Finland. Notable achievements include three prestigious awards: Best Paper Awards at the European Conference on Logics in AI (2021) and International Conference on Logic Programming (2023), and the Harold Boley award for system description (2022). Dr. Janhunen’s work emphasizes practical applications of declarative programming paradigms. Recent projects include Plingo—a probabilistic ASP system—and cybersecurity compliance frameworks in DevSecOps environments. His contributions bridge theoretical advancements with real-world challenges in AI security and industrial optimization.

Labushev Mikhail MikhailovichView profile

Associate Professor

• Mathematical Modeling in Geology
• Information Theory Applications
• Periodic System of Chemical Compounds
• +2 more

Labushev Mikhail Mikhailovich serves as an Associate Professor and Head of the correspondence department at the Department of Geology of Deposits and Exploration Methods, School of Mining, Geology and Geotechnology, Siberian Federal University. With 37 years of specialized service and a Candidate of Geological and Mineralogical Sciences degree (1993), he teaches core disciplines including Geology of Mineral Deposits and Mathematical Methods of Modeling in Geology. His educational background includes: Mining Engineer-Geologist specializing in Geological Survey, Prospecting and Exploration of Deposits from Krasnoyarsk Institute of Non-Ferrous Metals (1980) Candidate of Geological and Mineralogical Sciences (1993) Advanced training in Cloud Technologies for Educational and Scientific Activities (SFU, 2017) Labushev's research centers on mathematical modeling of equal and unequal masses across physics, chemistry, geology and ecology using information theory equations and numerical proportionality indices. His pioneering work focuses on establishing the scientific foundation for the Declaration of the Periodic System of Chemical Compounds, bridging geological modeling with theoretical chemistry. This integrates mineral classification, earth systems analysis, and computational methods to solve fundamental problems in substance classification. His recent publications demonstrate consistent application of mathematical frameworks to geological challenges, particularly in mineral/rock classification systems and ore body modeling. These works reveal strong interdisciplinary connections between geological survey techniques, environmental mass balance studies, and computational chemistry, with increasing emphasis on data transformation methodologies for geological modeling. No scientific awards were documented in the source material. While no formal advisees are listed, Labushev leads the correspondence department and drives the Declaration of the Periodic System of Chemical Compounds project. His advanced training in cloud technologies indicates active integration of modern computational approaches into geological research and education. The department's focus on exploration methods and deposit geology provides the operational framework for his research activities.

Badran RaddaouiView profile

Senior Lecturer

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Argumentation Frameworks
• +7 more

Badran Raddaoui is a Senior Lecturer (Maître de Conférences) at Telecom SudParis, where he conducts extensive research in artificial intelligence with a focus on knowledge representation, reasoning under uncertainty, and data mining. His work bridges theoretical foundations with practical applications in knowledge management and pattern discovery. His primary research interests include: Argumentation frameworks and logical reasoning systems Measurement and quantification of inconsistencies in knowledge bases SAT-based approaches for data mining problems Ontology reasoning under uncertainty Community detection in complex networks High utility itemset mining from transaction databases Dr. Raddaoui's recent publications demonstrate a strong emphasis on symbolic AI techniques for pattern extraction and robust reasoning with imperfect information. His work on inconsistency measurement through minimal inconsistent subsets and prime implicates has established him as a notable contributor to the field. He frequently applies constraint programming and SAT solving to problems in data mining and knowledge representation. His publication record shows consistent output from 2010 through 2024, with numerous papers in top-tier AI venues including IEEE Intelligent Systems, IJCAI, ECAI, and AAMAS. His research often involves collaborations with Saïd Jabbour and other researchers across multiple institutions. As an academic, Dr. Raddaoui contributes to the scholarly community through conference reviewing and participation in research projects focused on AI theory and applications. His current work continues to advance frameworks for knowledge base repair and conceptual clustering through pattern mining techniques.

Carla MeyerView profile

Professor

• Adolescent Literacy
• Literacy Education
• Disciplinary Literacy
• +5 more

Dr. Carla Meyer is a Professor at the School of Education of Duquesne University, where she teaches undergraduate and graduate courses in the Department of Instruction and Leadership in Education (DILE) since 2014. Her expertise spans adolescent literacy, disciplinary literacy, and teacher education, informed by a pragmatic philosophical stance that combines research and practice to address socially-situated educational challenges. Ph.D. in Literacy Education, University of Delaware (2009) M.S. in Reading Education, McDaniel College (2002) B.S. in Elementary Education, Pennsylvania State University (1997) Her research focuses on adolescent literacy , particularly the intersection of pragmatic philosophy with pedagogical solutions for democratic education. She has explored multimodal texts (e.g., graphic novels), disciplinary literacy , and strategies for struggling readers under the Common Core State Standards. Her work emphasizes teacher collaboration, reflective practice, and scaffolding instruction for diverse learners. Recent publications highlight trends in multimodal literacy , self-study methodology , and critical language awareness . She advocates for responsive learning communities that blend declarative knowledge with collaborative problem-solving and peer feedback. Scientific Awards & Grants Richard L. Venezky Award for Creative Dissertation Research (2009) Over $460,000 in grants from the U.S. Department of Education and state institutions for literacy programs (2010-2016) Chair of Middle Grades Reading Special Interest Group (2013) Board member, American Reading Forum (2013) Dr. Meyer has served on editorial review boards for Journal of Adolescent and Adult Literacy and Reading in the Middle , and as a program reviewer for major literacy conferences. Her teaching philosophy emphasizes modeling high-quality instructional strategies and fostering lifelong learning through peer collaboration and reflective practice.

• Answer Set Programming
• Logic Programming
• Knowledge Representation
• +7 more

Mario Alviano is a Professor at the University of Calabria's Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) in Rende, Italy. With a prolific publication record spanning from 2008 to 2025, he has established himself as a leading researcher in Answer Set Programming (ASP), logic programming, and knowledge representation. His work bridges theoretical foundations with practical applications across diverse domains including cybersecurity, environmental monitoring, and explainable AI. Alviano's research focuses on advancing the theoretical understanding and practical applications of Answer Set Programming. His key contributions include developing ASP Chef, a visualization and development framework for ASP; advancing theoretical foundations of conditional reasoning and typicality in weighted knowledge bases; and creating bridges between neural networks and symbolic AI through preferential interpretations. His recent work explores temporal extensions to conditional logics, applications of ASP to digital twin visualization, and tools for improving explainability in AI systems. Alviano's publications consistently appear in top-tier venues including ICLP, LPNMR, JELIA, and Theory and Practice of Logic Programming. His research demonstrates strong trends toward practical applications of theoretical advances, with increasing focus on explainability, visualization, and real-world problem solving. The recent surge in publications (26 in 2024 alone) indicates significant ongoing research activity and leadership in the field. His work increasingly integrates ASP with other AI paradigms, particularly neural networks, to create more interpretable and robust AI systems. Alviano has made substantial contributions to the development of tools and frameworks that lower barriers to entry for ASP, most notably ASP Chef which provides visualization capabilities for complex logic programs. His work on Hashcash Tree demonstrates applications to security problems, while his research on marketplace logistics shows practical business applications of declarative programming. His collaborative network is extensive, with frequent co-authorship with researchers including Wolfgang Faber, Nicola Leone, Francesco Ricca, Carmine Dodaro, Laura Giordano, and Daniele Theseider Dupré, reflecting strong connections across European AI research institutions. Alviano has also organized workshops including multiple editions of Datalog 2.0, demonstrating leadership in the logic programming community.

Antonis C. KakasView profile

Professor

• Computational Logic
• Abduction
• Argumentation
• +8 more

Antonis C. Kakas is a Professor in the Department of Computer Science at the University of Cyprus. His work spans computational logic, abduction, argumentation, and reasoning about actions and change. Education: B.Sc. in Mathematics, Imperial College London (1980) M.Sc. in Computer Science, Imperial College London (1987) Ph.D. in Theoretical Physics, Imperial College London (1984) Research focuses on Computational Logic , including Abductive Logic Programming , Argumentation , and Autonomous Agents . His projects involved EU-funded initiatives like Computing II, CompuCyprus, and SOCS. Key software developments include ACLP (Abductive Constraint Logic Programming), E-RES (reasoning about actions), and Gorgias (argumentation systems). Recent publications (2000-2005) explore integration of abduction with machine learning, agent deliberation, and answer set programming.

• Artificial Intelligence
• Knowledge Representation
• Constraint Answer Set Programming
• +4 more

Dr. Yuliya Lierler is a Professor in the Department of Computer Science at the University of Nebraska Omaha's College of Information Science & Technology. She has been a faculty member since 2012, reaching the rank of full professor, and was appointed to the Cheryl Prewett Diamond Professorship in 2020. Her work focuses on artificial intelligence, particularly in knowledge representation, automated reasoning, and declarative problem solving. PhD in Computer Science (University of Texas at Austin, 2010) Dr. Lierler's research bridges logic programming with practical AI applications, including natural language understanding, constraint satisfaction, and SMT-based solvers. She is a co-director of the NLPKR lab and has authored over 70 peer-reviewed publications in venues like Artificial Intelligence Journal and AAAI. Her contributions include open-access textbooks and tools like text2alm for semantic information extraction. Her recent publications explore advancements in answer set programming (ASP) semantics, automated reasoning frameworks, and hybrid knowledge representation systems. She has served as program co-chair for major conferences like ICLP (2022) and PADL (2017), and received awards such as the IS&T Outstanding Research Award (2024). Dr. Lierler also mentors students and leads initiatives in teaching innovation through online education and professional development programs. Mentor of the Year Award, Aksarben Foundation (2025) IS&T Outstanding Research and Creativity Award (2024) Best Student Paper Award (with Amelia Harrison, 2016) Dr. Lierler contributes to academic service through leadership roles in international conferences and program committees. Her lab, NLPKR, focuses on integrating natural language processing with formal logic, while her teaching emphasizes formal methods and AI foundations.

Rustin WebsterView profile

Associate Professor

• Engineering Design Education
• Geometric Dimensioning & Tolerancing (GD&T)
• Project-Based Learning (PBL)
• +3 more

Rustin Webster is an Associate Professor at Purdue Polytechnic Institute's School of Engineering Technology, specializing in mechanical engineering and computer graphics technology. His work focuses on engineering design education, particularly in geometric dimensioning & tolerancing (GD&T), gateway/capstone courses, and project-based learning (PBL). PhD in Interdisciplinary Engineering (2014, University of Alabama at Birmingham) MS in Management of Technology (2008, Murray State University) BS in Engineering Graphics and Design (2007, Murray State University) Webster's research bridges practical engineering skills with pedagogical innovation, emphasizing industry-critical competencies and immersive technologies. Recent publications highlight iterative design processes in capstone courses and GD&T applications. His awards include: 3× SME Distinguished Faculty Advisor Award (2019, 2023, 2024) ASEE National Engineering Technology Teaching Award (2021) Purdue Teaching Academy Fellow (2023) SOLIDWORKS Champion (2022) Webster actively serves as reviewer for engineering education journals and faculty advisor for Purdue's SME student chapter. He integrates certifications (SIX SIGMA, SOLIDWORKS Expert, ASME GDTP) into his teaching and industry engagement, including field trips to companies like Toyota and GE.

Justin LiView profile

Associate Professor

• Artificial Intelligence
• Cognitive Science
• Computational Models
• +3 more

Justin Li is an Associate Professor in the Computer Science and Cognitive Science departments at Occidental College, serving as Chair of the Computer Science Department since 2015. He holds a B.S. from Northwestern University and a Ph.D. from the University of Michigan. Office: Swan Hall #B102 Email: justinnhli@oxy.edu His research focuses on knowledge search strategies in problem-solving, including how humans and artificial agents recognize knowledge gaps, develop search strategies, and retrieve information efficiently. Key areas include memory mechanisms, computational models of cognition, and reinforcement learning. Recent publications explore computational models of feeling-of-knowing dynamics, integrating memory into reinforcement learning systems, and AI education tools for non-majors. He develops tools like Subitize for advanced course planning and a Google Sheet graduation-planning aid. Justin advises students and actively contributes to curriculum development, including writing about his experiences in How to Start a CS Department . His work emphasizes accessibility and inclusive education in STEM fields.

Michael GeneserethView profile

Professor

• Computational Logic
• Enterprise Management
• Computational Law
• +4 more

Michael Genesereth is a Professor of Computer Science at Stanford University and a professor by courtesy in the Stanford Law School. He serves as director of the Logic Group at Stanford and is the founder and research director of CodeX - the Stanford Center for Legal Informatics. Genesereth is renowned for his groundbreaking work on Computational Logic and its diverse applications in Enterprise Management, Computational Law, and General Game Playing. His academic journey includes an Sc.B. in Physics from M.I.T. and a Ph.D. in Applied Mathematics from Harvard University (1978). These foundational studies paved the way for his influential career at the intersection of logic, computer science, and law. Genesereth's research program spans from theoretical foundations of logic programming to practical implementations across multiple domains. His work on General Game Playing has established frameworks enabling AI systems to understand and play games based purely on logical descriptions of rules. In Computational Law, he pioneers approaches for representing legal rules in machine-processable formats to automate compliance checking and legal reasoning. His Logic For All initiative has successfully introduced logic education to high schools across the United States, with teachers reporting significant improvements in students' preparedness for college-level coursework. The publication pattern reveals a sustained research trajectory focused on logic foundations, with recent work expanding into practical applications in computational law, insurance portfolio management, and knowledge representation systems. His 2022-2025 publications demonstrate continued innovation in logic programming frameworks and their real-world implementations. Genesereth has founded multiple technology companies including Teknowledge, CommerceNet, Mergent Systems, and Symbium, translating theoretical work into practical solutions. His educational impact extends through widely adopted textbooks and his free massive open online course (MOOC) on Introduction to Logic, which has enrolled hundreds of thousands of students globally. His current initiatives include the International Logic Olympiad, launched in 2024, which engages high school students worldwide in logic problem-solving competitions. This effort builds on his observation that high school students consistently perform well in his university-level logic courses, demonstrating the readiness of secondary students for formal logic education.

• Databases
• Database Theory
• Complexity Theory
• +2 more

Christoph Koch is an Associate Professor at Technische Universität Wien specializing in databases and artificial intelligence. His research spans database theory, complexity theory, and logic in computer science, with a focus on XML processing, query optimization, and parallel data science techniques. Lead projects: KnowledgeGraph (2020–2028), HINT (2012–2017), Weblearn (2005–2008) Developed the Lixto visual extraction system and contributed to DLV knowledge representation framework Supervised T. Lukasser's diploma thesis on XPath query processing

• Databases
• Artificial Intelligence
• Text Mining
• +2 more

Ingo Feinerer serves as an Associate Professor at Vienna University of Technology's Faculty of Informatics, specifically within the Institute of Information Systems Engineering (E192-02). His research bridges theoretical computer science and practical applications in database systems, artificial intelligence, and text mining, with significant contributions to configuration management and formal methods. His educational background includes: PhD Dissertation (2007): A formal treatment of UML class diagrams as an efficient method for configuration management Diploma Thesis (2005): Formal program verification: a comparison of selected tools and their theoretical foundations Feinerer's research focuses on database theory (particularly schema mapping and dependencies), AI-driven configuration systems using integer linear programming, and text mining infrastructure development in R. His work combines theoretical rigor with practical tool development, notably through the textcat and tm packages. The integration of formal methods in software engineering remains a consistent thread throughout his publications. Analysis of his 15 most recent publications reveals strong specialization in database constraints (40%), text mining applications (30%), and software configuration systems (30%), with increasing interdisciplinary work connecting computer science to digital humanities. His notable recognition includes: INiTS Award (2007) for technology commercialization potential Feinerer has supervised doctoral research including F. I. Chertes' work on schema mapping languages and diploma theses on UML semantics and probabilistic databases. He led major research projects HINT (2012-2017) and SEE (2012-2016) focusing on database technologies and information systems. His work demonstrates consistent funding through Austrian national research programs. As a core member of the Databases and Artificial Intelligence research group, he collaborates extensively on R package development for text analysis and contributes to international workshops on theoretical aspects of software engineering.

• Answer-Set Programming (ASP)
• Explainable AI
• Logic in Artificial Intelligence
• +3 more

Tobias Geibinger is a PreDoc Researcher at the Institute of Logic and Computation of Vienna University of Technology , supported by a DOC Fellowship from the Austrian Academy of Sciences. His work focuses on explainability in Answer-Set Programming (ASP), particularly for advanced language features and hybrid ASP systems. Education: BSc, Dipl.-Ing. (Master's equivalent) in Computer Science Projects: CD Laboratory for AI and Optimization (2017–2025), KIRAS-PrEMI (2019–2022), ARTIS (2017–2025) His research combines logic programming with constraint programming and hybrid methods to solve industrial scheduling problems. This includes automated test laboratory scheduling, photolithography job scheduling, and pandemic-era physician scheduling. Recent work investigates neurosymbolic integration and contrastive explanation frameworks. Selected publications analyze ASP optimization techniques for parallel machine scheduling, large-neighborhood search strategies, and nonmonotonic paraconsistent logics. These contributions align with broader trends in declarative AI and logic-based optimization. Scientific Recognition: ASAI Master Thesis Prize (2023)

• Automated Deduction
• Logic in Computer Science
• Formal Methods
• +7 more

Jose Miguel Rivero Almeida is a researcher at the Department of Computer Sciences, Faculty of Computer Science of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He is a member of the LOGPROG - Logic and Programming research group, focusing on formal reasoning, automated deduction, and algorithmic solutions in computer science. His research interests lie at the intersection of logic and computation, including automated deduction, constraint satisfaction, formal methods, and parallel algorithms. His work emphasizes theoretical foundations and practical implementations in theorem proving and logical reasoning systems. The recent publications reflect a sustained engagement in computational logic, with applications extending into engineering simulations and educational logistics. His research trajectory shows a consistent focus on logical frameworks and algorithm design, evolving from foundational work in term rewriting and unification to parallel computational methods in mechanics. Rivero Almeida has participated in several competitive R&D projects such as Sweetlogics-UPC and Soluciones Efectivas basadas en la Lógica, demonstrating collaborative and applied research efforts. While no specific scientific awards are listed, his long-standing contributions to the field are evident through his publications and research grants. He has advised or collaborated on various research initiatives, particularly within formal methods and logic-based computing. His work often involves interdisciplinary collaboration, including with the Barcelona Supercomputing Center. He has contributed to educational organization projects, indicating involvement in academic administration. Rivero Almeida is active in research labs and teams centered around logic and programming, notably the LOGPROG group. His work integrates with broader UPC research networks in automated reasoning and formal technologies.

Michael GelfondView profile

Professor

• Knowledge Representation
• Logic Programming
• Answer Set Programming
• +3 more

Michael Gelfond is a Professor in the Department of Computer Science at the University of Texas at El Paso, affiliated with the Knowledge Representation Lab and the Texas Action Group. His research focuses on logic programming, knowledge representation, and non-monotonic reasoning, with significant contributions to Answer Set Programming (ASP) and the stable model semantics. M.S. in Mathematics, St. Petersburg University (1968) Ph.D. in Mathematics, Steklov Mathematical Institute (1974) His work bridges mathematical logic with AI, enabling intelligent agents to reason about dynamic domains. Recent publications explore A-Prolog for diagnostic reasoning, concurrent action effects, and declarative programming frameworks. He has received numerous accolades, including two Most Influential Paper in 20 Years Awards from the Association for Logic Programming, Fellowships from the European Academy of Sciences and AAAI, and the Burlington Northern Distinguished Achievement Award. His teaching includes courses on artificial intelligence, algorithms, and automata theory. Area Editor, Journal of Theory and Practice of Logic Programming Executive Editor, Journal of Logic and Computation Co-founder, Knowledge Representation Lab Co-chair, 27th International Conference on Logic Programming (2011)