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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.

• Arctic Technology
• Sea Ice Mechanics
• Structural Analysis of Marine Structures
• +4 more

Ekaterina Kim is an Associate Professor in the Department of Marine Engineering at NTNU's Faculty of Engineering. Her research focuses on Arctic technology, sea ice mechanics, and structural analysis of marine structures under ice loads. She leads the Risk Group and co-founded the Arctic Hive Mind research initiative, emphasizing AI-driven solutions for Arctic navigation safety. Key affiliations include the Norwegian University of Science and Technology and collaborations with institutions like ASME and SNAME. Her work spans experimental and computational studies of ice-structure interactions, risk assessment models for ice-covered waters, and autonomous systems for environmental monitoring. Notable contributions include Bayesian network risk models for icebreaker operations and deep learning frameworks for sea ice classification under adverse weather conditions. Kim has advised over 20 master’s and PhD students on topics ranging from structural analysis of icebreakers to AI applications in maritime safety. Her interdisciplinary approach integrates mechanical engineering, data science, and environmental monitoring to address challenges in Arctic shipping and infrastructure resilience. Recent research highlights include developing a probabilistic approach to correct biases in shipborne sea ice mapping and creating material models for polycrystalline ice behavior during impacts. Her work is published in journals like Ocean Engineering , International Journal of Impact Engineering , and IEEE Sensors Journal .

• Digital Image Processing
• Signal Processing
• Soft Computing
• +5 more

Prof. Annamária Várkonyiné Kóczy is a Professor at the Department of Informatics within the Faculty of Economics and Informatics at J. Selye University. She holds a joint appointment as a professor at Óbuda University's Institute of Software Design and Software Development. Her academic journey includes: Electrical Engineering studies (Measurement and Control) at Technical University of Budapest (1975-1981) Mechanical Engineering degree (Mechanical Engineer-Teacher) from the same institution (1978-1983) PhD in Computer Science from Technical University of Budapest (1996) DSc (Doctor of Sciences) from Hungarian Academy of Sciences (2010) Habilitation from Széchenyi István University (2017) Her research spans digital signal processing, soft computing, and hybrid techniques for complex systems, with applications in medical diagnostics, intelligent control, and sustainable technologies. She leads projects in computational intelligence and has developed novel algorithms for real-time processing in resource-constrained environments. Recent publications demonstrate strong focus on AI-driven solutions: machine learning for bioacoustic analysis (beehive swarming), fuzzy logic optimizations for edge computing, medical imaging diagnostics (COVID-19 detection), and sustainable energy control systems. Her work consistently bridges theoretical models with practical implementations in healthcare, environmental monitoring, and industrial diagnostics. She has secured multiple research grants including: OTKA K 105846: Intelligent Space and robotics (2012-2017) OTKA K 78576: Intelligent techniques in signal/image processing (2009-2013) GVOP-3.3.1: Intelligent 3D modeling systems (2006-2007) EU Structural Funds projects on vehicle industry collaborations She mentors graduate researchers in applied informatics and leads international collaborations across Europe and Asia, focusing on intelligent systems development.

• 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

• Statistical Mechanics
• Critical Phenomena
• Learning Theory
• +5 more

Juan Neirotti is a Senior Lecturer in Applied Mathematics & Data Science at the School of Computer Science and Digital Technologies, Aston University, United Kingdom. He has been affiliated with Aston University since 2004, previously holding post-doctoral positions at Purdue University, University of Rhode Island, and University of Sao Paulo. Research Interests: Statistical Mechanics, Critical Phenomena, Learning Theory, Optimization Problems Teaching: Artificial Neural Networks, Statistical Machine Learning (Masters in Data Sciences) His research focuses on the intersection of statistical mechanics and machine learning, particularly in modeling opinion dynamics and neural network systems. Recent works include studies on anisotropic societies, delayed information effects, and neural network modeling of social systems. Notable research trends involve applying physics-based models to computational problems, with a strong emphasis on committee machines, genetic algorithms, and dynamical systems. His work spans both theoretical and applied domains, including CDMA interference cancellation and quantum spin systems.

Alexander GegovView profile

Associate Professor

• Computational Intelligence
• Artificial Intelligence
• Explainable Artificial Intelligence (xAI)
• +13 more

Alexander Gegov is Associate Professor in Computational Intelligence at the School of Computing, Faculty of Technology, University of Portsmouth, and also serves as Visiting Professor in Control Theory at the English Faculty of Engineering, Technical University of Sofia. He is affiliated with the Centre of Excellence in Defence, Risk & Resilience and the Portsmouth AI and Data Science Centre. He has previously held leadership roles as Associate Dean for Research and has been actively involved in numerous international research collaborations. PhD in Cybernetics, Bulgarian Academy of Sciences DSc in Artificial Intelligence, Bulgarian Academy of Sciences Humboldt Guest Researcher, Germany EU Visiting Researcher, Delft University of Technology, Netherlands His research focuses on the development and application of artificial intelligence and machine learning methods—particularly fuzzy systems, neural networks, and evolutionary algorithms—for modeling and simulating complex systems characterized by nonlinearity, uncertainty, and high dimensionality. His work emphasizes explainability, transparency, and real-world feasibility in domains such as public security, financial forecasting, medical diagnosis, and environmental modeling. He actively contributes to the advancement of Explainable Artificial Intelligence (xAI) and its integration with fuzzy logic and causal models. The most recent publications highlight a strong trend toward explainable, interpretable, and ethically grounded AI, with applications in security, healthcare, project management, and intelligent control systems. His work frequently involves hybrid models, ensemble methods, and real-world validation in collaboration with NATO and other international bodies. National award for best young researcher, Bulgarian Union of Scientists Award for Outstanding Innovation in Digital Health and Social Care (2020) Excellence in Creating a Smarter World International Innovation Award (2020) Alexander Gegov has supervised numerous PhD students and served as Principal or Co-Investigator on research projects funded by NATO, EU, EPSRC, and Innovate UK. He is an active PhD supervisor and examiner. His leadership extends to editorial roles in top journals such as IEEE Transactions on Artificial Intelligence and IEEE Transactions on Fuzzy Systems, as well as participation in IEEE and NATO technical committees on explainable AI and computational intelligence. He has also organized international events such as the Alan Turing Institute Fringe Festival. He is a key member of several research groups including the Soft Computing Technical Committee of IEEE SMC, the IEEE Working Group on Explainable Artificial Intelligence, and the NATO Exploratory Team on Explainable AI. His work contributes to UN Sustainable Development Goals related to health, industry innovation, and responsible AI.

• Process Mining
• Business Process Management
• Process Discovery
• +7 more

Sander J.J. Leemans is an Associate Professor at Queensland University of Technology's Faculty of Science, School of Information Systems, specializing in process mining and business process management. With over 50 publications spanning from 2013 to 2023, his work has significantly contributed to advancing process mining methodologies and applications across various domains. Leemans' research interests focus on process discovery, conformance checking, stochastic process modeling, data quality in process mining, and the integration of process mining with exogenous data sources. His work bridges theoretical foundations with practical applications, addressing both technical challenges in process mining algorithms and organizational considerations for successful implementation in business settings. His publication record reveals a clear progression from foundational process mining techniques to more sophisticated approaches incorporating stochastic modeling, data quality considerations, and contextual factors. The research demonstrates increasing focus on practical implementation challenges, organizational adoption barriers, and the integration of process mining with broader business analytics initiatives. Leemans has made significant contributions to conformance checking methodologies, particularly through entropy-based and stochastic approaches. Leemans has supervised several researchers including Shiva Shabaninejad, Kanika Goel, Adam Burke, and Rehan Syed, indicating an active role in mentoring the next generation of process mining researchers. His collaborative work spans multiple institutions and demonstrates strong connections with leading researchers in the field such as Wil van der Aalst and Moe Wynn. His research group at Queensland University of Technology appears to focus on advancing process mining techniques while addressing practical implementation challenges in organizational settings. The work spans both theoretical algorithm development and practical applications across various industries and domains, including higher education as evidenced by his research on PhD student journeys.

Michael GreenbergView profile

Researcher

• Programming Languages
• Static Analysis
• Type Systems
• +4 more

Michael Greenberg is a researcher at Stevens Institute of Technology. He actively contributes to programming language design, static analysis, and formal methods, with a focus on Datalog, SMT solvers, and type systems.

• Artificial Intelligence
• Machine Learning
• Fuzzy Logic
• +4 more

Martin Tabakow, PhD, is a researcher at the Department of Artificial Intelligence within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His work focuses on artificial intelligence applications in medical imaging and computational biology. University: Wrocław University of Science and Technology School: Faculty of Information and Communication Technology Department: Artificial Intelligence Academic Rank: Researcher His research interests include: Deep learning for histopathology and retinal disease detection Fuzzy logic systems and type-2 fuzzy inference Data bias mitigation and class imbalance solutions Medical image segmentation and computational healthcare Publications (2019–2024) demonstrate expertise in using AI for biomedical applications. Notable trends include: Advancements in histopathological image analysis Development of type-2 fuzzy models for improved classification Focus on synthetic data generation and bias reduction Integration of AI with clinical diagnostic systems Current laboratory affiliations include the Artificial Intelligence Department's research teams focused on medical applications.

Alexander I. JordanView profile

Researcher

• Computational Statistics
• Probabilistic Forecasting
• Machine Learning
• +3 more

Dr. Alexander I. Jordan is a Staff Scientist in Computational Statistics at the Technical University of Denmark (DTU), affiliated with the Department of Informatics and Mathematical Modeling. His research focuses on probabilistic modeling, forecast evaluation, and statistical methodology development. Research interests span probabilistic forecasting, classifier validation, quantile analysis, and computational methods in statistics. He has pioneered tools like Pandora for genotype data stability and scoringRules for probabilistic model evaluation, with applications across meteorology, astrophysics, and finance. His publication record from 2012-2025 reveals expertise in Probabilistic Forecast Evaluation Bayesian Modeling Quantile-Based Risk Analysis Stellar Evolution Prediction AI Calibration Techniques Phylogenetic Probability Calculations

Sebastian WeichwaldView profile

Associate Professor

• Causal Modelling
• Machine Learning
• Structural Equation Models
• +3 more

Sebastian Weichwald is an Associate Professor at the Department of Mathematical Sciences , University of Copenhagen. He leads the Copenhagen Causality Lab (CoCaLa) and co-leads the Causality and Explainability (CX) collaboratory at the Pioneer Centre for AI. His academic journey includes a PhD at ETH Zurich (2019) and postdoctoral work at the University of Copenhagen. Research Interests: Causal Modelling, Causal Discovery, Structural Equation Models, Time Series Analysis, Neuroimaging, Biomedical Data Analysis. Awards: Best student paper at CIP 2014, Winner of Causality 4 Climate NeurIPS 2019 competition. Collaborations: Active in interdisciplinary projects with applications in cardiology, neuroscience, and biomedical imaging. Software Tools: Developed open-source libraries including CausalDisco , coroICA , tidybench , and Pymanopt for causal discovery, signal processing, and manifold optimization.

• Artificial Intelligence
• Machine Learning
• E-commerce Platforms
• +7 more

Jozsef Mezei is a Professor at the School of Business and Economics, Faculty of Social Sciences, Economics and Law, at Åbo Akademi University. His research lies at the intersection of artificial intelligence, information systems, and business analytics, with a strong focus on customer satisfaction, sentiment analysis, and e-commerce platforms. His research interests include Artificial Intelligence , Machine Learning , Large Language Models , Customer Review Analysis , Fuzzy Logic , and Configurational Methods in digital business environments. He applies computational intelligence techniques to understand user behavior, trust, and satisfaction in online platforms. The most recent publications highlight a strong trend toward integrating Large Language Models with traditional data analysis frameworks like CRISP-DM and qualitative comparative analysis (QCA), as well as continued innovation in fuzzy systems for sentiment and decision modeling. His work bridges technical AI methods with practical business applications, especially in e-commerce and digital health. He has contributed to research recognized under the UN Sustainable Development Goals, particularly in digital innovation and inclusive economic growth. While no specific scientific awards are listed, his work has been published in high-impact journals and top-tier conferences such as HICSS and Bled eConference. Jozsef Mezei supervises academic work, with at least two supervised projects documented. His research is supported by active collaboration networks across Europe, particularly in Finland and neighboring countries. He has developed frameworks combining sentiment analysis with fuzzy logic, applied to areas like mobile banking, recommendation systems, and customer feedback integration. He is affiliated with research in digital health usage, exploring how information systems facilitate healthcare innovation. His lab or research group appears to focus on intelligent systems for business decision-making, though no explicit lab name is provided. Future work is likely to further explore generative AI, trust modeling, and hybrid AI systems in digital platforms.

• 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.

Artur d'Avila GarcezView profile

Professor

• Neural-Symbolic Computation
• Neural Networks
• Applied Logic
• +10 more

Professor Artur d'Avila Garcez is a leading academic in Neural-Symbolic Computation at City St George's, University of London, where he serves as Professor of Computer Science and Director of the Research Centre for Machine Learning. He earned his PhD in Computing from Imperial College London (2000) and holds prestigious fellowships from the British Computer Society (FBCS) and Higher Education Academy (FHEA). With over 150 publications across journals like Artificial Intelligence and Neural Computation , and conferences including AAAI and NeurIPS, he pioneers hybrid AI systems merging machine learning with symbolic reasoning. Education: PhD (2000) and MEng (1993) in Computing Awards: Nuffield Foundation Grant (2002-2004), Daiwa Foundation Grant (2006) His research focuses on Neural-Symbolic Computing , integrating logic-based reasoning with deep learning for explainable AI. Key contributions include Logic Tensor Networks and Knowledge Extraction from CNNs, impacting fields like medical diagnostics and financial technology. He has mentored 22 PhD students and co-authored two foundational books in the field. He serves as Editor-in-Chief for Neurosymbolic AI and on editorial boards of Machine Learning and Journal of Logic and Computation . His work addresses trust in AI , accountability , and human-like computing through EU and industry-funded projects like Smart Big Data Platform and Safety Validation of Autonomous Vehicles.

Sophie TourretView profile

Researcher

• Automated Reasoning
• Formal Logic
• Artificial Intelligence
• +8 more

Sophie Tourret is a Researcher at Inria (Nancy & LORIA, France) and a guest researcher in the Automation of Logic group at the Max Planck Institute for Informatics (MPII, Germany). Her research focuses on extending automated reasoning techniques to higher-order logic, improving SMT solvers, and generating prime implicates in equational logic. Education: PhD in Computer Sciences, Grenoble Alpes University (2016) Master in Web and Artificial Intelligence, Joseph Fourier University (2012) Engineering Degree, Grenoble INP - Ensimag (2012) Research Interests include automated reasoning, formal logic, artificial intelligence, and logic programming. She leads projects like Matryoshka (extending superposition to higher-order logic) and CPEC (explanation generation in description logics). Recent Publications span topics such as superposition calculus, prime implicate generation, and neural-to-symbolic learning. Her work bridges symbolic reasoning and machine learning in formal logic contexts. Scientific Awards: ILP Best Paper Award (2019) Lise Meitner Award (2018) 2 Minutes Madness Best Poster and Presentation (2014) Teaching Experience includes formal languages theory, database management, and communication networks at Grenoble INP - Ensimag. She also served on program committees for conferences like CADE, IJCAI, and IJCAR.