Henriette de Swart is a full Professor of French Linguistics and Semantics at Utrecht University , affiliated with the Institute for Language Sciences . Her research spans cross-linguistic semantics , focusing on tense and aspect , negation , and bare nominals , while integrating artificial intelligence methodologies. She has made significant contributions to bidirectional optimality theory and language evolution studies.
Jiřina Vejnarová is an Associate Professor and the Director of the Institute of Information Theory and Automation at the Czech Academy of Sciences. She also teaches at the Faculty of Informatics of the University of Economics and the Faculty of Nuclear Sciences and Physical Engineering of the Czech Technical University. Her educational background includes graduation from the Faculty of Mathematics and Physics at Charles University in Prague in 1985, with a focus on probability and mathematical statistics. She received her RNDr. title in 1986 and her CSc. (PhD equivalent) in theoretical cybernetics in 1991. Professor Vejnarová's research focuses on structured multidimensional models within interval probabilities, particularly in possibility theory and evidence theory. Her work spans artificial intelligence, uncertainty modeling, belief functions, and probabilistic reasoning. She has developed compositional models for credal sets and has extensively researched the marginal problem in evidence theory. Her recent publications demonstrate a consistent focus on evidence theory, possibility theory, and related areas of artificial intelligence. She has made significant contributions to the understanding of credal networks, composition operators, and the interpretation of various probability extensions. Professor Vejnarová has been actively involved in the academic community, having organized the 5th International Symposium on Imprecise Probabilities and served on the editorial board of the journal Kybernetika. She was a member of the board of the Czech Society for Cybernetics and Informatics from 2003-2010. As Director of the Institute since 2017 (previously serving as Deputy Director for Research from 2009), she leads research initiatives in information theory and automation. Her teaching responsibilities include courses such as Mathematical Informatics, Information Theory and Inference, Logic and Semantics, and Probabilistic Models of Artificial Intelligence.
Kenichi Asai is a Professor in the Department of Information Science, Faculty of Science at Ochanomizu University . His research focuses on functional programming languages, type systems, and program analysis tools. Research Interests: Partial evaluation, continuations, reflection, type debugging, and algebraic effects. Projects: Developed the OCaml Type Debugger to improve error messages through interactive debugging. Conference Involvement: Active in PEPM, ICFP, and ML workshops as author, session chair, and committee member. The articles analyzed show expertise in type theory , continuation handling , and functional language design across OCaml and its extensions. Keywords include type debugging , metaOCaml , and algebraic effects . Scientific Awards: Peter Landin Prize (2013) for "An Embedded Type Debugger" Students: Collaborated with Yuki Ishii and Kanae Tsushima on type systems and functional programming education.
Iryna Smushchynska serves as Professor and Head of the M. Zerov Department of Theory and Practice of Translation from Romance Languages at Taras Shevchenko National University of Kyiv since 2011, following academic progression from Lecturer (1994) to Associate Professor (1995-2004) and Professor of French Philology (2004-2011). Her academic credentials include: 1984: Graduate, Faculty of Romance-Germanic Philology, Taras Shevchenko National University of Kyiv 1993: PhD in Philology (Romance Languages) 2003: Doctor of Philological Sciences 2011: Full Professor Professor Smushchynska's research centers on Translation Studies and Stylistics of Romance Languages, with emphasis on stylistic translation phenomena, pragmatics, literary translation, and computational lexicography. Current investigations examine micro/macro discourse elements in translation, artistic image transfer, stylistic tropes, denotative/connotative meaning preservation, and author-translator idiolect dynamics, utilizing interdisciplinary frameworks from linguistics, psycholinguistics, and computer science. Her 15 most recent publications (2009-2018) reveal strong thematic continuity in semantic analysis of translation challenges, particularly regarding archaisms, enantiosemy, and metaphorical language. Methodologically, she integrates corpus-based approaches with cognitive and computational perspectives, focusing predominantly on French-Ukrainian translation while expanding to Spanish, Italian, and Portuguese language pairs. As research group leader, she has supervised 17 PhD theses over the past decade, producing monographs, textbooks, dictionaries, and software tools. Her team edits the scientific collection 'Style and Translation' and provides expertise in conference interpreting training and comparative stylistics. The research group operates through interdisciplinary collaboration, employing parallel/comparable corpora to analyze translation phenomena across stylistics, literary studies, sociolinguistics, and computational linguistics, with emphasis on empirical validation of theoretical frameworks.
Laurence Jolivet is a researcher at IGN (Institut national de l'information géographique et forestière) and member of the LASTIG (Laboratoire des Sciences et Technologies de l'Information Géographique) laboratory, affiliated with the STRUDEL and MEIG teams. She holds a PhD in Geography (2014) from Université Paris 1 Panthéon-Sorbonne and works on spatiotemporal analysis, landscape modeling, and human-wildlife interaction studies. Research Interests : Landscape description and modeling for fauna movement simulation (BD TOPO, RGE ALTI) Volunteered Geographic Information (VGI) qualification for land use monitoring Sensitive/perceptual mapping protocols for urban space analysis Multi-source data integration in urban ecosystem management Recent Article Trends focus on: Wildlife disturbance from recreational activities Multi-source land use classification workflows Tick bite risk modeling in peri-urban zones Citizens' Books analysis for policy input Deep learning applications in landscape dynamics Supervision : Co-supervisor of Martin Cubaud (PhD, 2022-ongoing): Land use classification via deep learning Supervisor of Colin Kerouanton (PhD, 2020): Hiker mobility analysis Key Projects : IntForOut (2024-2027) : Integrating multisource data for ecosystem monitoring under outdoor recreation pressure SOTIQUE (2024-2025) : Socio-ecological tick bite risk modeling LandSense (2016-2021) : Citizen observatory for land cover monitoring CHOUCAS (2017-2022) : Heterogeneous data integration for mountain rescue
Azelle Courtial is a Researcher at Institut Géographique National (IGN) , specializing in deep learning applications for cartographic generalization within the MEIG research team (Modélisation des Environnements Inhabités et Généralisation). Her work focuses on multi-scale mapping automation and spatial data representations , with recent contributions in GAN-based mountain road generalization and deep learning benchmark development . Education: PhD in Geographic Information Sciences (Université Paris-Est, 2023), Master's in GIS (2019), Licence in Math & Informatics (2017) Projects: ERC Consolidator Grant recipient (2021-2026) Software: Developer of CartAGen4Py and DeepMapGen libraries Her publications demonstrate a research trajectory combining deep learning for map generalization multi-scale cartography principles constraint-based evaluation frameworks spatial relation modeling in GIS Scientific recognition includes: Best short paper award at AGILE Conference 2022 Teaching contributions span spatial databases , webmapping , and GIS programming , with responsibilities in course material development and student project supervision.
Fariba Sadri is a Senior Tutor in the Department of Computing at Imperial College London, where she serves as Director of Studies for multiple MSc programs including Artificial Intelligence, Machine Learning, and Software Engineering. Her research focuses on logic-based approaches to artificial intelligence, with emphasis on multi-agent systems, intention recognition, and abductive logic programming. Her recent work explores applications of computational logic in reactive systems, underwater dredging control, and ambient intelligence. She has contributed to the development of the KGP agent model and the CIFF proof procedure for abductive reasoning. Awarded grants for projects like CLOUT (2016) and LPS (2011-12), Sadri has held leadership roles in conferences such as RuleML+RR (General Co-Chair, 2017) and CLIMA (Steering Committee, 2000-2011). She has also co-edited special issues on logic-based agents and ambient intelligence.
Dr. Amir Pirayesh is an Associate Professor in Operations, Supply Chain and Information Management at KEDGE Business School, France. His research focuses on supply chain optimization, process modeling, and sustainability in manufacturing and logistics systems. He holds a PhD in Industrial and Mechanical Engineering from École nationale supérieure des arts et métiers (ENSAM) and previously worked at ENSAM as an ATER (Associate Professor of Teaching and Research). He has contributed extensively to European research projects under the Factories of Future and Horizon 2020 programs. PhD in Industrial and Mechanical Engineering (ENSAM, 2014) Current affiliation: KEDGE Business School Prior affiliation: InterOP-VLab His research spans four key domains: supply chain optimization (vaccine distribution, waste recycling), smart manufacturing (collaborative robots, assembly line balancing), enterprise modeling (interoperability, conceptual frameworks), and sustainability analytics (circular economy, environmental/social integration). Recent work applies reinforcement learning to scheduling and semantic frameworks for IoT interoperability. Article trends show a strong focus on pandemic response logistics (2021-2025), followed by collaborative robotics (2024), smart grid energy management (2022-2023), and circular economy applications (2023-2025). Co-authors include M. Mohammadi, M. Karimi Mamaghan, and O. Battaia, indicating collaborations across French, Iranian, and Portuguese institutions. As a former InterOP-VLab researcher and ENSAM ATER, Dr. Pirayesh has extensive experience in model-driven enterprise management, with applications in healthcare logistics, sustainable manufacturing, and digital dentistry. He teaches courses on supply chain modeling, simulation, and information systems optimization.
Sven Ove Hansson is a Professor in Theoretical Philosophy at Uppsala University's Department of Philosophy. His research bridges formal logic and epistemology, focusing on probabilistic belief revision systems and their philosophical implications. Department: Theoretical Philosophy, Uppsala University Email: sven.ove.hansson@filosofi.uu.se Location: English Park, Thunbergsvägen 3 H, Box 627, 751 26 Uppsala His work explores the intersection of: Probability theory and logical reasoning Belief revision frameworks (AGM theory) Bayesian epistemology Formal models of knowledge dynamics Recent publications analyze iterated revision mechanisms, probability-based belief dichotomy, and limitations of dyadic probability models in logical frameworks.
Emmanuel Dean is a Senior Researcher in the Automation research group at Chalmers University of Technology, School of Electrical Engineering. His work focuses on robotics, human-robot interaction, and assistive technologies. Dean's research interests span multiple areas of robotics and human-computer interaction: Robotics and autonomous navigation systems Human-robot collaboration and interaction Tactile sensing and physical interaction Motion prediction and obstacle avoidance Brain-computer interfaces for robotic control Assistive robotics for physical therapy His recent publications show a strong focus on developing advanced navigation systems for mobile robots that can predict human motion and avoid collisions in dynamic environments. Dean has also made significant contributions to tactile-based interaction methods, particularly for assistive robotics applications in physical therapy contexts. Dean has received research funding for the CRAFT (Collaborative-Robot Assistant for Technicians) project (2021-2023), which focused on developing robotic assistants for industrial technicians. His work demonstrates strong interdisciplinary collaboration, with publications spanning robotics, neuroscience, and rehabilitation engineering.
Rodrigo M Braga, PhD, serves as Assistant Professor in Neurology (Epilepsy/Clinical Neurophysiology) at Northwestern University's Feinberg School of Medicine and holds a concurrent appointment in the Weinberg College of Arts and Sciences. He is affiliated with the Mesulam Center for Cognitive Neurology and Alzheimer's Disease and the Northwestern University Clinical and Translational Sciences Institute (NUCATS), where his research bridges clinical neurology and cognitive science. His academic credentials include: BS from University of Edinburgh (2006) MRes from Imperial College London (2010) PhD from Imperial College London (2014) Postdoctoral Fellowship at Harvard University (2018) Instructor position at Stanford University (2020) Dr. Braga's research centers on brain networks governing recollection, language, and social cognition, with emphasis on their disruption in Alzheimer's disease, aphasia, and vascular conditions. His methodology integrates electrophysiology and MRI to map neural mechanisms of cognitive functions, particularly examining how neurodegenerative processes alter large-scale brain organization. This work has significant implications for understanding cognitive decline and developing diagnostic frameworks for neurological disorders. His 2025 publications reveal a cohesive research trajectory exploring geometric principles of brain architecture, aperiodic neural activity development, and lateralized network compensation. These studies collectively advance network neuroscience by linking structural geometry to functional dynamics in language and memory systems, while demonstrating methodological innovation in analyzing human brain connectivity patterns across health and disease states. No scientific awards are listed in the current profile. Details regarding student mentorship and grant funding are not specified in the available documentation, though his active publication record indicates ongoing research productivity. His industry relationship with Unearthly Materials, Inc. (ownership interest reported for 2024) reflects engagement with translational applications of neuroscience research. Dr. Braga's primary research environment operates through the Mesulam Center for Cognitive Neurology and Alzheimer's Disease, where he contributes to multidisciplinary investigations of cognitive neurology. This affiliation facilitates integration of basic neuroscience with clinical neurology, particularly in studying network-based mechanisms of neurodegenerative conditions and developing biomarkers for cognitive disorders.
Cyril Allignol is a Lecturer and Researcher in the OPTIM team at the National School of Civil Aviation (ENAC) research laboratory. His work focuses on two primary themes: (1) solving combinatorial optimization problems related to air traffic and airport operations using constraint programming , and (2) formalizing reactive languages to ensure guaranteed properties for air traffic control and piloting assistance tools. PhD in Computer Science and Telecommunications (2011) from the University of Toulouse ENAC Engineer (2006) Master's in Computer Science and Telecommunications (2006) from the University of Toulouse His research spans air traffic conflict resolution , detect-and-avoid algorithms for UAVs/UAS , and formal methods in reactive language compilation . He has contributed to constraint programming frameworks, robust gate allocation models, and 3D trajectory deconfliction systems. His work integrates metaheuristics , geometrical algorithms , and formal verification techniques. Publications reveal expertise in mathematical optimization , UAS integration , and bigraph-based modeling for avionics systems. He collaborates with institutions across France, Italy, Georgia, and the United States through conferences like ICRAT , ATM Seminar , and ROADEF . His team affiliation ( OPTIM ) and technical focus on conflict resolution , self-separation , and navigation accuracy highlight his contributions to air traffic safety and efficiency . Current projects include 4D-trajectory deconfliction and formal methods for avionics software .
Johann Mitlöhner serves as an Associate Professor at the Vienna University of Economics and Business (WU), specifically within the Department of Information Systems and Operations Management. He is affiliated with the Institute for Data, Process and Knowledge Management, located in Building D2 on the third floor at Welthandelsplatz 1, 1020 Vienna, Austria. Professor Mitlöhner's research spans several interconnected areas within information systems and knowledge management. His primary focus lies in Preference Aggregation in Decision Support and Project Effort Estimation . He has extensively explored the application of Social Choice Voting Rules for preference aggregation and investigated the properties of voting rules through simulations. His work in text analysis encompasses Text Mining , Semantic Web technologies, Sentiment Detection , Product Feature Extraction , and Ontology Learning in restricted domains, with particular emphasis on grammar pattern-based approaches. Within his department, Professor Mitlöhner contributes to academic instruction and supervision of thesis topics related to decision support systems for IT system selection, effort estimation of IT projects, and various aspects of text mining and semantic technologies. His research bridges theoretical computer science with practical business applications, particularly in the domain of knowledge management systems. Professor Mitlöhner maintains an active research profile with publications cataloged in the WU Research database and participates in various research projects also documented through the university's research database. He collaborates with colleagues including Kaiser, Alexander; Kragulj, Florian; Polleres, Axel; Sabou, Marta; and others within the institute.
Victor Finomore Jr. serves as Executive Director of Research Operations and Data Analytics at the West Virginia University (WVU) Rockefeller Neuroscience Institute. He holds dual academic appointments as Assistant Professor in the Department of Neuroscience within the School of Medicine and Adjunct Professor in the Department of Chemical, Biomedical Engineering at the Statler College of Engineering and Mineral Resources. Dr. Finomore earned his Ph.D. in Experimental Psychology from the University of Cincinnati in 2008. Prior to joining WVU, he served as Technical Advisor for the Warfighter Effectiveness Research Center at the United States Air Force Academy, where he led research on human performance enhancement through multimodal displays, neuroergonomics, and human-machine teaming for military operators. His research program centers on Human Performance, Physiological Measurement and Assessment, and Cognitive Neuroscience. As director of the Human Performance and Applied Neuroscience (HPAN) research center, he conducts cutting-edge research on real-time neurophysiological assessment across diverse populations (Athletes, Military, Patients, and general Population - AMP 2 ). His work integrates advanced sensors with machine learning analytics to develop individualized augmentation strategies for optimizing brain health, human performance, recovery, and resilience. Cognitive testing in his lab focuses on workload, stress, fatigue, attention, decision-making, and multisensory integration. Analysis of Dr. Finomore's 15 most recent publications (2017-2021) reveals a strong interdisciplinary focus bridging psychology, neuroscience, and engineering. Key research themes include human-robot interaction dynamics (particularly robot authority and compliance), neurophysiological monitoring using wearable technology, deep brain stimulation applications for addiction treatment, and vigilance in automated systems. His work demonstrates consistent collaboration with military and clinical research partners, with significant emphasis on translating laboratory findings to real-world applications in healthcare and defense contexts. Dr. Finomore directs the Human Performance and Applied Neuroscience (HPAN) research center, which conducts both basic and applied research in controlled laboratory settings and naturalistic "in the wild" environments. The center specializes in developing and validating neurophysiological assessment protocols for diverse operational contexts, with particular expertise in translating cognitive neuroscience findings into practical human performance solutions.
Juhee Bae is a Senior Lecturer in the Department of Information Technology at the University of Skövde, Sweden. She is affiliated with the AI research group, focusing on machine learning, visual analytics, and explainable AI. Her work bridges research and education, with courses in advanced data science and experience in teaching coordination. Ph.D. in Computer Science, North Carolina State University Merited Teaching Award (2023) Mobility Grant for Belgian University Collaboration (2020) Her research spans machine learning , visual analytics , and explainable AI , with applications in Steel industry process optimization Climate-adaptive water management Migration intention forecasting Smart production logistics Wearable biosensor analysis Recent publications highlight predictive models for weather-driven migration, causal discovery frameworks, and interactive data mining techniques. She contributes to editorial boards and conference organization. Juhee Bae serves as course coordinator for Explainable AI and has been active in international collaborations. Scientific awards include 2023 Merited Teaching Award 2020 Mobility Grant Her projects STRATUS (AI for climate adaptation), INSITE-X (steel industry AI), and Understanding Human Migration demonstrate practical AI applications. She works with interdisciplinary teams and has presented talks on predictive machine learning and explainable AI.
