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Timothy J. MadiganView profile

Professor

• Medical ethics
• Business ethics
• Computer ethics
• +2 more

Timothy J. Madigan is Professor and Chair of the Department of Philosophy and Classical Studies at St. John Fisher College. He holds a Ph.D., M.A., and B.A. from the State University of New York at Buffalo. His research spans multiple ethical domains including medical, business, and computer ethics, with special focus on philosophy of religion and popular culture. Madigan's publications demonstrate broad philosophical engagement: Explorations of friendship theory across historical contexts Ethical analyses of sports culture and leadership Reappraisals of Bertrand Russell's philosophical legacy Critical examinations of literary-cultural phenomena His scholarly honors include the St. John Fisher College Trustees' Distinguished Scholars Award (2013). With extensive publications in peer-reviewed journals and edited volumes, Madigan maintains active research collaborations while mentoring emerging scholars in philosophical ethics and cultural criticism.

Gaël Le MensView profile

Professor

• Judgment Under Uncertainty
• Learning Processes
• Social Dynamics
• +3 more

Gaël Le Mens is Professor (Catedràtic) in the Department of Economics and Business at Universitat Pompeu Fabra, with affiliations at Barcelona School of Economics and UPF-BSM. An ICREA Acadèmia Awardee (2023-2027), he researches learning processes in individuals, organizations, and machines, examining how information sampling affects judgment and belief formation. Current research explores: Social media feedback dynamics and opinion polarization Conceptual categorization using LLMs (GPT-4, Llama 3) Reinforcement learning biases Wisdom-of-crowds phenomena His work combines computational modeling with experimental methods. Publications appear in top journals including Psychological Review (4 papers), PNAS (4 papers), and Management Science. Research themes show consistent focus on decision biases arising from sampling limitations, with recent expansion into AI-assisted text analysis and political scaling applications. Honors include: ERC Consolidator Grant for selective information sampling research Multiple publications in PNAS and Psychological Review ICREA Acadèmia recognition Mentored doctoral students include Elizaveta Konovalova, Nikolas Schöll, and Thomas Woiczyk. Leads the ERC-funded project 'Implications of Selective Information Sampling' examining belief polarization mechanisms. Affiliated with the Rebel Governance Network and Strategic Organization Design Unit, with visiting positions at INSEAD, London Business School, and Stanford GSB.

Mustafa OcalView profile

Teaching Professor

• Artificial Intelligence
• Natural Language Processing
• Data Science
• +4 more

Mustafa Ocal is an Assistant Teaching Professor in the Department of Computer Science at Florida International University (FIU), part of the College of Engineering and Computing. He holds both a PhD (2022) and MS (2021) in Computer Science from FIU, focusing on Artificial Intelligence and Natural Language Processing. His research emphasizes temporal analysis, TimeML, and timeline extraction, leading to publications, patents, and software libraries like pyTLEX and jTLEX. Education: Ph.D., Computer Science, Florida International University (2022) M.S., Computer Science, Florida International University (2021) Research Interests: Ocal’s work centers on AI applications in temporal analysis, including automated timeline extraction, TimeML corpus validation, and narrative duration estimation. His contributions address core challenges in natural language processing and data science, with applications in computational linguistics and information extraction. Publications reflect a focus on temporal modeling, corpus evaluation, and algorithm development. His work spans theoretical advancements in timeline extraction to practical tools like Python/Java libraries. Notable Achievements: Holds a US patent for an AI model and co-authored a Cambridge University Press book. His research has been recognized through peer-reviewed articles at top conferences and publishers. Teaching & Mentoring: Ocal actively mentors students at both undergraduate and graduate levels, emphasizing effective communication of complex AI concepts. He teaches across various universities, contributing to the next generation of computer scientists.

• Heuristic search
• Program synthesis
• Deep learning for sound

Vadim Bulitko is a Professor in the Faculty of Science at the University of Alberta, affiliated with the Department of Computing Science. He holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. Research Interests: His work spans heuristic search, program synthesis, and deep learning applications for sound. Recent projects focus on AI-driven puzzle generation, pathfinding in dynamic game environments, and neural network classification. Publications: His 15 most recent articles explore AI advancements in puzzle design, video-game navigation, and bioacoustic classification, emphasizing heuristic optimization and explainable AI techniques.

Thomas A. RunklerView profile

Adjunct Professor

• Machine Learning
• Data Analytics
• Pattern Recognition
• +3 more

Thomas A. Runkler is an Adjunct Professor at the Technical University of Munich (TUM), holding a Chair in the Foundations of Software Reliability and Theoretical Computer Science within the TUM School of Computation, Information and Technology. Since 1999, he has taught computer science at TUM while concurrently serving in various expert and managerial roles at Siemens AG, where he currently holds the position of Senior Principal Research Scientist. His academic journey includes a Master’s and PhD in electrical engineering from TU Darmstadt (1992/1995), followed by postdoctoral research at the University of West Florida (1996–1997). Runkler’s research spans machine learning , data analytics , fuzzy systems , and optimization . He has authored/co-authored over 200 publications, focusing on topics like clustering algorithms, neural networks, and decision-making frameworks. Professional contributions include leadership roles in organizations such as the IEEE CIS committees and the German Association for Computer Science’s Fuzzy Systems group. His teaching includes courses on Data Mining and Knowledge Discovery . Key publications include foundational works on fuzzy preference structures, Bayesian decomposition of dynamical systems, and interpretable reinforcement learning policies. Runkler’s work bridges academia and industry, emphasizing practical applications in automation, recommendation systems, and industrial procurement forecasting. His research often addresses challenges in uncertainty handling, algorithm design, and scalable data analysis.

Noelani AristaView profile

Associate Professor

• Indigenous History
• Hawaiian Governance
• Language Archives
• +3 more

Noelani Arista (Kanaka Maoli) is Associate Professor and Canada Research Chair specializing in 19th-century Hawaiian legal history and Indigenous knowledge systems. She directs McGill's Indigenous Studies Program and authored the award-winning book The Kingdom and the Republic , which reframes colonial histories through Hawaiian-language documents. Her research examines governance, law, and epistemology prior to US annexation. Current projects explore artificial intelligence applications for Indigenous language archives and traditional knowledge organization. She founded the '365 days of aloha' initiative to share Hawaiian language and cultural knowledge through social media. Her publications frequently analyze intersections of indigenous sovereignty, archival practices, and decolonization methodologies.

Amalia SkiltonView profile

Assistant Professor

• Field Linguistics
• Linguistic Anthropology
• Child-Caregiver Interaction
• +7 more

Amalia Skilton is a Chancellor's Fellow (Assistant Professor) in Linguistics & English Language at the University of Edinburgh's School of Philosophy, Psychology & Language Sciences. She is a field linguist and linguistic anthropologist specializing in Indigenous languages of the Americas, with extensive research experience in the Amazon Basin. Her research focuses on child-caregiver interaction, language acquisition, pragmatics, and documentation of endangered and Indigenous languages . Dr. Skilton has conducted over 26 months of fieldwork with speakers of Ticuna (an isolate language) and Máíhɨ̃ki (Tukanoan) in the Amazon Basin, making significant contributions to linguistic documentation of these endangered languages. Her recent scholarly work demonstrates strong trends in language documentation tools and methodologies , particularly focusing on improving workflows between ELAN and FLEx software for linguistic fieldwork. Her publications bridge theoretical linguistics with practical applications for language preservation, showing particular strength in developing tools that support Indigenous language communities. Chancellor's Fellow (prestigious early-career fellowship) Member of the Language and Society Scotland network Active participant in the linguistic academic community through conferences and collaborations Dr. Skilton has advised on language documentation projects and contributed to knowledge exchange initiatives focused on endangered languages. Her work extends to developing practical tools like Flibl that address specific challenges in linguistic fieldwork documentation. She maintains active research collaborations with institutions including MPI Nijmegen and has presented her work at major linguistic conferences including SSILA. She leads research teams focused on Amazonian language documentation and has established fieldwork protocols that prioritize community engagement and ethical research practices with Indigenous communities.

Ilia KuznetsovView profile

Researcher

• Natural Language Processing
• Peer Review Assistance
• Cross-document Modeling
• +5 more

Dr. Ilia Kuznetsov is a Senior Postdoctoral Researcher at the Ubiquitous Knowledge Processing Lab (UKP Lab) of the Technical University of Darmstadt. He coordinates the InterText group, focusing on cross-document NLP and AI-assisted peer review systems. His research emphasizes responsible AI, human-AI collaboration, and empirical approaches to AI evaluation. He co-leads the "Ethics for Natural Language Processing" course at TU Darmstadt and has advised numerous students in PhD and master’s programs. Education includes a Candidate of Sciences (Mathematical and Applied Linguistics) from Lomonosov Moscow State University and work at the Higher School of Economics, Russia. His PhD at TU Darmstadt explored "The Role of Linguistics in Probing Task Design" . Research interests span peer review automation, cross-document modeling, and interpretable NLP. Key projects include NLPeer (a peer review resource) and CARE (a collaborative AI-assisted reading environment). Recent work focuses on large-language model applications in citation generation and task design. Publications emphasize peer review analysis, document structure modeling, and probing tasks. He actively collaborates on interdisciplinary projects addressing AI ethics and scholarly communication challenges.

Jonathon S. ReinhardtView profile

Professor

• English as a Second Language
• Computer-assisted Language Learning
• Digital Game-based Language Learning
• +3 more

Jonathon S. Reinhardt is a Professor at the University of Arizona, affiliated with the College of Information Science. His research focuses on the integration of technology in second and foreign language education, particularly with emerging technologies like digital gaming and social media. He teaches courses in language pedagogy, sociolinguistics, and technology-enhanced language instruction for English, MATESL, and SLAT PhD programs. Reinhardt serves on the editorial boards of CALICO Journal and Language Learning and Technology , and previously served as President of the Computer Assisted Language Instructional Consortium (CALICO) from 2019-2020. His work bridges theoretical frameworks with practical applications in digital game-based learning, social media pedagogy, and L2 digital literacies. His publications analyze the role of digital games in language acquisition, the sociolinguistic dimensions of social media use, and the ecological factors shaping L2 identity development. Recent work emphasizes 'gameful' pedagogy, digital mediation, and the evolving landscape of technology-enhanced language learning. Reinhardt's research highlights innovative methodologies such as genre analysis, metaphor frameworks, and ecological systems theory applied to digital contexts. His contributions advance both theoretical understanding and practical implementation of technology-mediated language education strategies.

Daniel S. HainView profile

Associate Professor

• Data Science
• Network Analysis
• Machine Learning
• +7 more

Professor Daniel S. Hain is an Associate Professor at Aalborg University Business School, part of Aalborg University's Faculty of Social Sciences and Humanities. He is affiliated with the IKE Research Group (AI for the People) and the MASSHINE Cluster for Research on Innovation and Development. His research focuses on innovation dynamics, artificial intelligence, network analysis, and technology forecasting. Hain has been actively involved in strategic initiatives such as the Danish Industry Foundation's AI Denmark program and the Generative Ethnographic AI (GE-AI) seed project, demonstrating his commitment to advancing AI applications in societal contexts. His work bridges academic research with practical industry challenges, particularly in areas like patent analytics (e.g., Patentsberta model), renewable energy innovation, and global venture capital patterns in developing economies. Hain has received the OECD IPSDM “Big Data Analytics” Challenge award (2018) for collaborative research on inventive activity visualization. He contributes to policy discussions on AI governance, the privatization of research talent, and regional green technology development. Key projects include the Danish Research Unit for Industrial Dynamics (DRUID) since 1995, exploring innovation systems, and the EIS project on energy innovation competitiveness. Hain’s recent focus on generative AI ethics and SME predictive analytics reflects his dual expertise in technical methodologies and socio-economic impacts.

Christos ThrampoulidisView profile

Assistant Professor

• Machine Learning
• Deep Learning Theory
• Mathematical Optimization
• +4 more

Christos Thrampoulidis is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), Vancouver. Previously, he held positions at the University of California, Santa Barbara (UCSB). His research focuses on machine learning theory, deep learning principles, mathematical optimization, and statistical signal processing. He leads the Machine Intelligence and Learning Design (MILD) group at UBC, emphasizing foundational studies of large language models, neural collapse, and high-dimensional data analysis. Education: PhD and MS in Electrical Engineering from the California Institute of Technology (Caltech), with a minor in Applied and Computational Mathematics. Undergraduate degree (Diploma) in Electrical and Computer Engineering from the University of Patras, Greece. Research interests include the implicit geometry of next-token prediction, optimization bias in neural networks, and robust generalization in overparameterized models. Key contributions include work on adversarial training, neural collapse phenomena, and safe reinforcement learning. His group actively explores theoretical frameworks for language models and develops methodologies to analyze their mechanics and limitations. Awards: NSERC Discovery Grant (2021). Teaching includes courses on optimization and signals & systems at UBC. He has advised numerous graduate students (PhD and MSc) and undergraduates, focusing on rigorous mathematical approaches to machine learning problems. Active collaborations span academia and industry, with publications in top venues such as NeurIPS, ICASSP, and AAAI. Current projects include understanding language model geometry, decentralized learning, and mitigating imbalances in training data. The group prioritizes equity and diversity, welcoming students with strong mathematical backgrounds and curiosity about foundational ML questions.

Gudrun KlinkerView profile

Professor

• Industrial Augmented Reality
• Sensing
• Ubiquitous Tracking (Sensor Fusion)
• +4 more

Overview Prof. Gudrun Klinker is a Professor at Technische Universität München (TUM), leading the Chair of Computer Science Applications in Medicine & Augmented Reality. She holds a PhD from Carnegie-Mellon University and has extensive experience in academia and industry, including roles at Digital Equipment Corporation and Fraunhofer Institute. Affiliations Professor (C3/W2) since 2000 Head of the FAR (Augmented Reality) research group Member of the CAMP (Computer Aided Medical Procedures) interdisciplinary team Research Dr. Klinker specializes in augmented reality (AR) with focuses on industrial applications, sensor fusion, 3D interaction, and healthcare integration. Her work bridges computer science with medical and industrial challenges, emphasizing user-centric design and real-world deployment. Key projects include: AR-based maintenance systems for complex machinery Healthcare applications like serious games for nutrition education VR/AR training environments for medical and aerospace domains Awards and Recognition Robert Sauer Prize (2010) for contributions to Bavarian science ISMAR Lasting Impact Award (2014) for influential AR research Teaching and Mentorship Prof. Klinker teaches courses on 3D user interfaces, AR fundamentals, and game design. She advises students in thesis projects, focusing on AR applications, HCI, and medical computing. Labs and Collaborations Her research is supported by partnerships with industry and institutions, leveraging TUM's interdisciplinary environment to advance AR/VR systems for real-world impact.

Claudia SchonView profile

Researcher

• Automated Reasoning
• Artificial Intelligence
• Knowledge Representation
• +3 more

Claudia Schon is a Researcher at the Institute of Computer Science, Department 4, University of Koblenz-Landau. Her work focuses on automated reasoning, cognitive systems, and knowledge representation. She has contributed to projects like the Corg Project, exploring cognitive reasoning mechanisms and integrating commonsense knowledge into automated theorem provers. Her research bridges logic-based AI with human reasoning models, addressing challenges in uncertainty management, associative reasoning, and deontic logic. Key areas of research include commonsense reasoning, deductive systems, and the application of large language models to knowledge selection. She has published extensively on topics such as negation in cognitive reasoning, concept contraction in description logics, and intentional forgetting in AI systems. Her work often emphasizes interdisciplinary approaches, combining formal logic with cognitive science and philosophy. Her publications from 2019 to 2024 reflect a steady focus on advancing automated reasoning methodologies, integrating human-like cognitive processes into AI systems. Recent work explores the next generation of deduction systems and the use of word embeddings for axiom selection. She has also contributed to workshops on linguistic and cognitive approaches to dialog agents, highlighting her interest in human-AI collaboration. Despite no listed scientific awards, her contributions are recognized through active publication in top-tier conferences and journals. She is affiliated with the E-KRHyper project, developing reasoning tools for description logics. Her advising roles and grants are not explicitly documented in the provided text.

• Riemannian Geometry
• Symplectic Geometry
• Geometric Group Theory
• +3 more

Mikhael Gromov is the Jay Gould Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a D.Sc. and Ph.D. from the University of Leningrad (now Saint Petersburg State University), specializing in Mathematics. University: New York University School: Courant Institute of Mathematical Sciences Department: Mathematics His research interests span Riemannian Geometry, Symplectic Geometry, Geometric Group Theory, and interdisciplinary studies on learning processes. Recent work includes applying geometric principles to biology and probability theory. Recent publications explore isoperimetric inequalities, entropy in geometric structures, and topological probability frameworks. He has also contributed to understanding protein conformation spaces and mathematical linguistics. Education: D.Sc. Mathematics, University of Leningrad, 1973 Ph.D. Mathematics, University of Leningrad, 1969 Gromov teaches advanced courses on topology, probability, and geometry, including a 2022-23 course on 'Probability and Topology' and 2024 lectures on geometric theorems. His work often bridges pure mathematics with applications in biology and computer science.

Richard ZowallaView profile

Researcher

• Health Informatics
• Web Data Mining
• Text Mining
• +4 more

Dr. sc. hum. Richard Zowalla is a researcher at the Faculty of Computer Science, Heilbronn University, specializing in health informatics and software engineering. He works at the Interdisciplinary Center for Machine Learning (ZML) and focuses on health web analysis, text mining, and open source software. Education: Doctorate in human sciences (Dr. sc. hum.), dissertation on German health web data analysis (2022) Research interests include health information systems, focused web crawling, text mining, and software quality management in agile environments. His work bridges healthcare and computer science through data-driven analysis of online medical resources. Publication trends show expertise in health web readability, multilingual health data comparison, and AI-driven service innovation. His articles emphasize practical applications of machine learning and data visualization in healthcare contexts. Teaching: Software Lab (2016-2025), Advanced Programming Techniques (2020-2024), Database Internship (2014-2025), Distributed Systems (2016-2017) Labs & Teams: Active member of the Interdisciplinary Center for Machine Learning (ZML) at Heilbronn University, contributing to collaborative research in health data analysis.