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Surendra N. Singh serves as the AT&T Foundation Professor in the Department of Marketing and Business Law at the School of Business, University of Kansas. His academic career spans over three decades with research focusing on advertising effectiveness, consumer cognition, and digital marketing interfaces. His educational background includes: Ph.D. in Business from the University of Wisconsin, Madison M.S. in Marketing from the University of Wisconsin, Madison B.S. from Benaras Hindu University, India Singh's research investigates how exposure, emotional states, and design elements influence consumer responses. Key areas include mere exposure effects in advertising, multimedia ad effectiveness, program-induced emotion impacts on memory, bilingual advertising, web usability, and employee affective displays in service settings. His work bridges psychological theory with practical marketing applications, particularly in digital environments. Analysis of his publication history reveals consistent contributions to top-tier journals like the Journal of Marketing and Journal of Marketing Research. Recent work (2024-2019) examines online rating systems, payment method psychology in charitable giving, and emotional color effects in advertising. Earlier research (2017-1996) established foundational insights into web perception, accent bias in service encounters, and memory mechanisms in advertising. His scholarship demonstrates sustained focus on consumer cognition across traditional and digital marketing contexts.

• Advertising
• Consumer Behavior
• Digital Marketing
• +5 more

Christian Meilicke is a Researcher at the Data and Web Science Group (DWS) within the School of Business Informatics and Mathematics at the University of Mannheim. His work focuses on artificial intelligence, ontology matching, and knowledge graph completion, with recent contributions to rule-based methods and their applications in business process modeling. He is heavily involved in teaching, coordinating courses such as 'Modeling Business Processes' and 'Artificial Intelligence.' His research interests include the integration of open and structured knowledge, probabilistic reasoning frameworks, and improving the efficiency of knowledge base systems. He has explored topics like inductive logic programming, automated debugging of ontologies, and the use of Markov Logic Networks for root cause analysis in IT systems. In terms of trends, his recent publications emphasize combining symbolic rule-based approaches with machine learning for knowledge graph tasks, such as activity recommendation and link prediction. He also investigates explainability in embeddings and temporal forecasting in knowledge graphs. His work often bridges theoretical advancements with practical applications in business informatics and data integration. No scientific awards have been explicitly mentioned. Christian has advised no formal students listed here but has contributed to teaching and mentoring through his courses and tutorials. His research and teaching are closely tied to the DWS Group, which focuses on data-centric AI and semantic technologies.

• Artificial Intelligence
• Knowledge Graphs
• Ontology Matching
• +5 more

Hamza M. Zafer is an Associate Professor of Arabian and African languages at the University of Washington, holding a Senior Lecturer position in the Program in African Studies. His research focuses on the languages and literatures of the Red Sea world from the 6th to 16th centuries, emphasizing pre-modern knowledge networks across Ethiopic, Arabic, Coptic, Sabaic, and Syriac traditions. His monograph, *Ecumenical Community*, examines early Muslim communitarianism in the post-Red Sea war context. Dr. Zafer earned his Ph.D. from Cornell University. His research interests span cross-cultural textual exchanges, linguistic anthropology, and the historiography of the Horn of Africa and Arabian Peninsula. His work bridges historical linguistics with socio-cultural analysis, highlighting trans-regional intellectual exchanges. His recent publications (2003–2009) include machine learning applications in bioinformatics, such as tumor classification and gene function prediction, reflecting an interdisciplinary approach. Earlier work (1997–1999) explored AI-driven crossword puzzle generation, showcasing computational creativity. No academic awards or grants are explicitly mentioned in the provided text. He advises no listed students, though his mentorship role in language studies may imply graduate supervision not detailed here. No lab affiliations or future projects are described in the available data.

• Red Sea World Studies
• Pre-Modern Knowledge Networks
• Ethiopic Literature
• +4 more

Tim French is an Associate Professor in the Department of Computer Science and Software Engineering at the University of Western Australia's School of Physics, Maths and Computing. He is affiliated with the UWA Oceans Institute and serves as Regional Contest Director for the South Pacific Programming Contest and Programme Chair for the Australasian Conference on Artificial Intelligence 2022. His research focuses on logic, artificial intelligence, knowledge representation, and reasoning about uncertainty in multi-agent systems, probabilistic reasoning in games, and industrial applications like automated planning and machine learning for complex processes. French holds a PhD in Computer Science (2007) and BSc in Computer and Mathematical Sciences (1999), both from UWA. His expertise spans algorithms, automated reasoning, formal methods in software, and temporal logic verification systems. He has led or contributed to 6 major research projects including the ARC Research Hub for Transforming Energy Infrastructure and the ARC Training Centre for Transforming Maintenance through Data Science. His research outputs include 118 publications covering topics like aleatoric logic for probabilistic reasoning, semantic knowledge extraction from industrial maintenance systems, and deep learning applications in wastewater treatment. He has developed novel methods for knowledge graph construction, state estimation in complex systems, and user interface design informed by work characteristics models. Key grants: 6 active/finished projects totaling $M+ funding Leadership roles: Programming contest director, conference chair Interdisciplinary focus: Combines formal logic with industrial automation challenges French's work contributes to UN Sustainable Development Goals through education and innovation in sustainable industrial processes and environmental systems modeling. His team has developed practical solutions for maintenance procedure digitization, wastewater plant optimization, and robust agent-based systems for uncertain environments.

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

Dave Sullivan is a Teaching Professor and Director of Peer Education in the Department of Computer Science at Boston University. He joined in 2006, advancing from Lecturer to Master Lecturer in 2018. His roles include teaching, curricular innovation, and leading the undergraduate course assistant program. Education: AB in Physics (Harvard College), SM and PhD in Computer Science (Harvard University). Research focuses on education innovation (e.g., data-centric courses for non-majors) and technical areas like probabilistic reasoning in software and social norms in AI. His publications span pedagogy and technical domains. Awards: BU’s highest teaching honor (Metcalf Award) and the Neu Family Award Advising/Grants: Coordinates peer education programs but no grants listed. Labs/Teams: No specific lab affiliations mentioned.

• Computer Science Education
• Data Science
• Curriculum Development
• +3 more

Eddy Keming Chen is an associate professor of philosophy at the University of California, San Diego (UCSD), affiliated with the John Bell Institute for the Foundations of Physics and UCSD's Chinese Studies Program. His work bridges philosophy of physics, metaphysics, and formal epistemology, with a focus on quantum foundations, time asymmetry, and nomic vagueness. He earned a PhD in philosophy (2019) and M.Sc. in mathematical physics (2019) from Rutgers University, alongside a graduate certificate in cognitive science. Research interests include laws of nature, quantum mechanics in time-asymmetric universes, and the metaphysics of the wave function. Notable works include Fundamental Nomic Vagueness (Philosophical Review, 2022) and Quantum Mechanics in a Time-Asymmetric Universe (BJPS Popper Prize, 2021). He is Co-PI of a Templeton grant exploring quantum foundations and has contributed to public philosophy via articles in New Scientist and interviews with the APA Blog. Teaching spans Chinese philosophy, symbolic logic, metaphysics, and philosophy of physics. Grants include UCSD Senate awards (2021–2025) and a $325k subaward from a $2.5M Templeton grant. Awards include the APA Public Philosophy Prize (2024) and Rutgers’ Harvey Waterman Medal (2019). Education: PhD in Philosophy, Rutgers University (2019) M.Sc. in Mathematics (Mathematical Physics), Rutgers (2019) Graduate Certificate in Cognitive Science, Rutgers (2018) B.S. Mathematics & B.A. Philosophy (Highest Honors), Calvin College (2013) His work on quantum foundations has been featured in Nature and Scientific American , with ongoing projects on surreal decision theory and Xunzi's meta-ethics. Current collaborations include a screenplay about time-travel romance inspired by SEP articles.

• Philosophy of Physics
• Metaphysics
• Philosophy of Science
• +7 more

Dr. Paulo Rauber is a Lecturer in Artificial Intelligence at the School of Electronic Engineering and Computer Science, Queen Mary University of London. He holds a PhD from the joint program between University of Campinas (Brazil) and University of Groningen (Netherlands), and previously conducted postdoctoral research at the Swiss AI Lab (IDSIA) under Jürgen Schmidhuber. Education: BSc in Computer Science, Federal University of Santa Catarina (2011) PhD in Computer Science, University of Campinas & University of Groningen (2017) Research: Focuses on Bayesian reinforcement learning methods addressing exploration, planning, and generalization. His work spans scalable algorithms for non-stationary environments and deep reinforcement learning applications. Teaching: Teaches postgraduate courses such as Neural Networks and Deep Learning, Artificial Intelligence in Games, and Data Mining. Grants & Projects: Secured a Swiss National Science Foundation grant (NEUSYM, ~$700,000) in 2019, and leads projects like Colosseum and Hindsight Policy Gradients. Affiliations: Member of the Centre for Multimodal AI and actively supervises PhD students in reinforcement learning and Bayesian methods.

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +4 more

Dr. Amy Babay is an Assistant Professor at the University of Pittsburgh’s School of Computing and Information, with joint appointments in the Department of Informatics and Networked Systems and the Department of Computer Science. She holds a PhD (2018) and MSE (2014) in Computer Science from Johns Hopkins University, along with a BA in Cognitive Science (2012). Her research focuses on distributed systems and networking, emphasizing resilient critical infrastructure and next-generation internet services. Notable projects include the Spire intrusion-tolerant SCADA system for power grids and structured overlay networks for low-latency applications. Her work has been recognized with awards, including the Best Paper at IEEE ICDCS 2017. Babay’s research also explores technology’s role in community resilience and cybersecurity challenges in smart cities. She teaches advanced distributed systems, networks, and programming courses. Key contributions include the Spines overlay platform and Prime replication engine. Her lab, RSSLab, actively develops solutions for resilient infrastructure and cybersecurity threats. Publications span topics like intrusion-tolerant systems, epidemic modeling, and network resilience. She collaborates with institutions like Sandia National Labs and Hawaiian Electric Company. Contact: babay@pitt.edu, located at 701B Information Sciences Building.

• Distributed Systems
• Networking
• Resilient Systems
• +3 more

Dr. Edmondo Minisci is a Senior Lecturer in Mechanical and Aerospace Engineering at the University of Strathclyde, part of the Faculty of Engineering. He leads the Intelligent Computational Engineering Laboratory (ICE-Lab) within the Aerospace Centre of Excellence. With over 25 years of experience, his expertise spans model-based analysis and design optimization of complex mechanical/aerospace systems, including aeronautical vehicles, wind turbines, and sustainable energy systems. He actively supervises PhD students in areas like multi-fidelity methods, intelligent control, and sustainable agriculture engineering. Dr. Minisci has received prestigious awards, including the NATO STO “Panel Excellence” Award (2024) and the Sir Arthur C Clarke Award (2015). His research focuses on multidisciplinary design optimization under uncertainties, machine learning applications, and aerospace technologies for sustainable development. Key projects include optimizing high-lift devices, wind turbine performance, and precision agriculture systems. He has contributed to numerous conferences, including organizing the International Conference on Bioinspired Optimisation Methods and serving as an examiner for PhD theses. His work bridges computational intelligence with practical engineering challenges, emphasizing resilience and innovation in aerospace and energy sectors. Research Interests: Multi-Objective Optimization, Nature-Inspired Algorithms, Uncertainty Treatment, Machine Learning, Intelligent Control. Recent Projects: Symbolic Computation for Differential Equation Systems, EPSRC-funded Uncertainty Quantification in Aeroelastic Systems, and Advanced Natural Language Models for Satellite Communications.

• Multi-Objective Optimisation
• Nature Inspired Optimisation Algorithms
• Multidisciplinary Design Optimisation Under Uncertainties
• +7 more

Pavlos Motakis is an Assistant Professor in the Department of Mathematics and Statistics at York University, Toronto, Canada. He holds a PhD in Mathematics from the National Technical University of Athens. Previously, he was a J.L. Doob Research Assistant Professor at the University of Illinois at Urbana-Champaign and a Visiting Assistant Professor at Texas A&M University. His research focuses on functional analysis, particularly the study of operators on Banach spaces, algebras of operators, and the local and asymptotic structure of Banach spaces. Key interests include hereditarily indecomposable Banach spaces, L∞-spaces, and metric characterizations of non-local properties. Recent work explores topics such as orthogonal factors of operators on Rosenthal spaces, variants of the James Tree space, and reflexive Calkin algebras. His publications reflect a deep engagement with advanced structural properties of Banach spaces and operator algebras. Dr. Motakis currently holds no listed scientific awards but maintains an active research agenda in pure mathematics. His advising and grant activities are not detailed in available records, though his academic career demonstrates consistent contributions to functional analysis through teaching and research. He is affiliated with the Faculty of Science at York University, contributing to both undergraduate and graduate programs in mathematics and statistics. His office is located in the Ross Building, S618.

• Functional Analysis
• Operator Theory
• Banach Space Theory
• +4 more

Utsav Sadana is an Assistant Professor of Operations Research in the Department of Computer Science and Operations Research (DIRO) at the University of Montréal. He holds affiliations with IVADO, CIRRELT, and GERAD research centers. His academic journey includes a postdoctoral fellowship at McGill University and a PhD in Management Science from HEC Montréal under Prof. Georges Zaccour. His research spans mathematical optimization, machine learning, and game theory with applications in decision-making under uncertainty. Key interests include entropic risk measures, impulse control in differential games, and contextual optimization methods. His work integrates theoretical foundations with practical applications in security, insurance, and technology adoption. His recent publications demonstrate a strong focus on distributionally robust optimization, risk-averse decision making, and game-theoretic approaches to dynamic systems. The 2025 European Journal of Operational Research survey synthesizes advances in contextual optimization, while his 2023 INFORMS Journal paper establishes the value of randomized strategies in network security contexts. As an educator, he teaches Methods of Operations Research (IFT-6575) and Dynamic Programming (IFT-6521) at the University of Montréal, and previously taught Business Statistics at McGill University. Lead researcher on Decision-making in dynamical systems (2024-2030) funded by NSERC Discovery Grant Co-researcher on Integration of Environmental Data in Urban Planning (2024-2026) with MITACS Acceleration funding His research methodology combines theoretical rigor with computational implementation, evidenced by his GitHub repositories containing optimization solvers and experimental code for risk analysis.

• Mathematical optimization
• Machine learning
• Game theory
• +5 more

Geoffrey Goodwin is a Professor in the Department of Psychology at the University of Pennsylvania. His research focuses on decision processes, social and cultural psychology, particularly in social cognition, moral psychology, and reasoning. He teaches courses such as PSYC 170 (Social Psychology) and PSYC 470 (Seminar in Social Psychology). His work examines moral objectivism, retributive psychology, and the valuation of human lives, with a secondary interest in deductive reasoning and relational logic. Education: BA in Psychology from the University of Queensland; Ph.D. in Psychology from Princeton University. He is affiliated with Solomon Labs at 3720 Walnut St, Room D16. Research Interests: Moral judgment and objectivism Social cognition and belief attribution Retribution directed toward non-human entities Generational obligations and environmental ethics Conditional reasoning and probabilistic logic Recent work emphasizes asymmetries in moral judgments (e.g., soldiers in war contexts) and lay intuitions about belief controllability. He has advised Ryan Wheat in the Psychology graduate program. Notably, he will not accept new graduate students for Fall 2025. Labs: Solomon Labs (UPenn)

• Social Psychology
• Moral Psychology
• Decision Processes
• +7 more

Cezary Z. Janikow is an Associate Professor and former Chair of the Department of Mathematics and Computer Science at the University of Missouri–St. Louis (UMSL), where he conducts research in artificial intelligence, evolutionary computation, and machine learning. He is the developer of FID (Fuzzy Inference Decision tree), a system for generating fuzzy decision trees that handle missing, noisy, and continuous data. His research is supported by NIH, NSF, and NASA/JSC, and he has contributed significantly to the field of genetic programming, particularly in constrained and adaptable representations (ACGP). Ph.D. in Computer Science, University of North Carolina at Chapel Hill, 1991 His research interests center on evolutionary algorithms , fuzzy decision trees , and symbolic machine learning . He explores how genetic programming can adapt representations and incorporate constraints to improve search efficiency and solution quality. His work on FID has advanced fuzzy logic applications in classification and data analysis. The recent publications reflect a sustained focus on genetic programming , particularly Adaptable Constrained Genetic Programming (ACGP) , heuristics in evolutionary search , and fuzzy decision systems . His work spans theoretical development, algorithm design, and practical implementation, with applications in optimization and machine learning. Dr. Janikow has delivered corporate training in software development and served as an expert witness in software-related legal cases. He has also organized academic workshops and presented tutorials on evolutionary algorithms. Co-PI, NIH: Statistical Methods for Recursively Partitioned Trees NSF Support: Fuzzy Decision Trees NASA/JSC Collaboration: Constrained Genetic Programming He leads the FID project, a software system for fuzzy decision tree generation, which includes a GUI and supports cross-validation and noise simulation. This tool reflects his integration of research and software development.

• Artificial Intelligence
• Evolutionary Computation
• Genetic Algorithms
• +9 more

François Chaumette is a Senior Research Scientist (Directeur de recherche) at Inria, affiliated with IRISA and the Centre Inria de l'Université de Rennes. He has been a key researcher in robotics and computer vision since 1990 and led the Lagadic research team from 2004 to 2017. His research interests are centered on robot vision, particularly visual servoing and active perception . He has made foundational contributions to image-based and position-based visual servoing, and his work integrates control theory, computer vision, and robotics. His research spans applications in mobile robotics, aerial systems, medical robotics, space robotics, and soft object manipulation. The recent publications highlight a consistent focus on visual servoing under complex constraints—such as motion blur, occlusions, and deformations—applied to drones, cable-driven robots, and space systems. There is a strong emphasis on robustness , stability analysis , and hybrid sensing (e.g., vision + proximity, vision + force). His work with the RemoveDebris mission demonstrates real-world impact in space robotics. AFCET/CNRS Prize for best Ph.D. in Automatic Control Best paper awards at RFIA 1996 & 2004 Best paper in IEEE T-RA (2002) Best paper in IEEE RA-L (2019) Best paper in IEEE RAM (2020) IEEE Fellow (2013) He has advised over 30 Ph.D. students, many of whom have become active researchers in robotics. He has served in editorial roles for top journals including IEEE Transactions on Robotics , IEEE Robotics and Automation Letters , and the International Journal of Robotics Research . He was elected to the IEEE RAS Administrative Committee (2016–2018) and served on ERC grant panels for robotics. Chaumette is the main developer of ViSP (Visual Servoing Platform), a widely used C++ library for visual tracking and servoing. His leadership in both theoretical advances and software tools has significantly shaped the visual servoing community.

• Robotics
• Computer Vision
• Visual Servoing
• +17 more

Dr. Jorge Fandinno is an Assistant Professor of Computer Science at the University of Nebraska at Omaha since 2020. His academic journey includes an Alexander von Humboldt Fellowship at the University of Potsdam (Germany) and a Postdoctoral Fellowship at the Toulouse Institute of Computer Science Research (France). He earned his Ph.D. in Computer Science from the University of Corunna (Spain) in 2015. Current Role: Assistant Professor, Computer Science Department, University of Nebraska at Omaha (2020–present) Previous Roles: Alexander von Humboldt Fellow (University of Potsdam, Germany), Postdoctoral Fellow (Toulouse Institute of Computer Science Research, France) Dr. Fandinno’s research focuses on Artificial Intelligence , particularly in Knowledge Representation and Reasoning , Answer Set Programming , and Epistemic Logic . His work bridges theoretical and practical aspects, including the development of formal semantics, deductive systems, and applications in causal reasoning and constraint handling. Recent publications emphasize automated reasoning, strong equivalence verification, and integrating quantitative information into logic programming frameworks. His scholarly output includes over 60 publications in prestigious venues such as Artificial Intelligence , Journal of Artificial Intelligence Research , and conferences like AAAI, IJCAI, and LPNMR. He has received three best technical paper awards at LPNMR (2015, 2017, 2019) and mentored students who won best student papers at JELIA and ICLP. Notably, he secured a NSF CAREER award (2024) for his project on Answer Set Programming for Quantitative Information. Scientific Awards: NSF CAREER award (2024) Best technical paper, LPNMR 2015 Best technical paper, LPNMR 2017 Best technical paper, LPNMR 2019 Best student paper, JELIA 2019 (supervised student) Best student paper, ICLP 2020 (supervised student) Dr. Fandinno’s publications span theoretical advancements in logic programming, epistemic reasoning, and practical applications in causal analysis and constraint satisfaction. His recent work explores automated reasoning tools (e.g., Anthem 2.0 ), recursive aggregates, and complexity assessments in ASP.

• Artificial Intelligence
• Knowledge Representation
• Reasoning
• +5 more