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Ana Bumber is a Doctor of English Studies currently serving as an ATER (temporary teaching and research associate) at Université Paul Sabatier Toulouse 3, assigned to IUT A. She is affiliated with the Department of Chemical Engineering Process Engineering, though her academic work is firmly rooted in English literature and language pedagogy. Her dual research focus bridges the humanities and technology, particularly in AI applications for language education and the literary artistry of Vladimir Nabokov. Her research interests include Artificial Intelligence in Language Teaching , generative AI tools , automatically generated subtitles , and intermediality in Vladimir Nabokov’s work . She explores how AI influences second language acquisition and investigates Nabokov’s intricate use of visual and sensory elements in literature. Her work often intersects digital pedagogy with modernist literary analysis. The recent trends in her publications show a strong emphasis on AI in education , particularly tools like HeyGEN for influencing L2 motivation, and the evaluation of AI-generated captions in ESP contexts. Simultaneously, she continues to publish on Nabokov’s aesthetic empiricism, train imagery, nature writing, and portraiture, reflecting a sustained scholarly engagement with modernist intermediality. Scientific Affiliations and Service: Member, French Vladimir Nabokov Society (SFVN) – also on the board (CA) Member, International Vladimir Nabokov Society (IVNS) Member, European Association for Computer Assisted Language Learning (EUROCALL) Member, Modern Language Association (MLA) Member, Society for Modernist Studies (SEM) Organizing member, Pedagogical Exchange Days (Lairdil) Webmaster team member for academic website Ana Bumber is actively involved in academic mentoring and pedagogical innovation, though no formal advisees are listed. She has organized pedagogical events and contributed to curriculum development through active learning strategies such as 'Job Dating' and peer feedback workshops. She has not received any explicitly mentioned grants or scientific awards in the provided text. Her work is supported through institutional affiliations and collaborative research networks, particularly in Nabokov studies and AI-enhanced language teaching. She participates in interdisciplinary seminars such as the EFELIA-ANITI cycle on AI and Humanities, indicating a strong commitment to bridging technological and humanistic scholarship.

• Artificial Intelligence in Language Teaching
• Generative AI
• Automatically Generated Subtitles
• +7 more

Asta Halkjær From is a postdoctoral researcher in the Department of Computer Science at the University of Copenhagen, affiliated with the Software, Data, People & Society (SDPS) section under Dmitriy Traytel. She previously completed her PhD at DTU Compute from 2020 to 2023, focusing on formalized deduction methods in computational logic. She holds a Master’s and Bachelor’s degree from DTU in Computer Science and Software Technology, respectively, with a specialization in Artificial Intelligence and Algorithms. Her research lies at the intersection of formal logic and computer science, particularly in automated reasoning, proof assistants (Isabelle/HOL and Lean), and mechanized metatheory. She has contributed extensively to synthetic completeness proofs, tableau systems, and verified theorem provers. Her work emphasizes formal verification of logical systems, including epistemic logic, hybrid logic, and first-order logic, with a focus on soundness and completeness. The recent publications highlight a consistent trend: the mechanization of logical foundations in proof assistants. Her work bridges theoretical logic with practical verification tools, enabling reliable automation in theorem proving. She has developed and verified provers, explored axiomatic systems, and advanced the methodology of synthetic completeness, often leveraging Isabelle/HOL’s framework. Distinguished Paper Award, CPP 2023 DTU Young Researcher Award DTU Travel Grant (Executive Board Recognition) Otto Mønsted Fonden Travel Grant She has supervised BSc and MSc theses, special courses, and research projects at DTU, and currently teaches Software Development for Digital Health . She has served on program committees for CPP, ITP, and Dalí workshops and has reviewed for journals including Journal of Automated Reasoning and Journal of Logic and Computation . She has also participated in international research visits, including at VU Amsterdam. She is actively involved in building tools for formal reasoning and maintains a personal website with resources, including an Isabelle snippets generator and bibliography tools. Her work continues to advance the foundations of formal logic through mechanized proofs and practical automation.

• Formal Logic
• Automated Reasoning
• Theorem Proving
• +13 more

Noury Bouraqadi is a part-time university professor at IMT Nord Europe, located in the Lille region of northern France. He is affiliated with the College of Engineering, contributing to academic and research initiatives in software engineering and robotics. His work bridges academia and industry through his active involvement in both teaching and entrepreneurial ventures. Research Interests: His research spans two major domains: Software Engineering (SE) and Artificial Intelligence (AI) for mobile and autonomous robots. In SE, he investigates software architectures, dynamic reflective languages (especially Pharo and JavaScript via PharoJS), and tools enabling modular and agile development. In AI, his focus lies on coordination and cooperation mechanisms in robotic fleets, including communication models and emergent or predefined organizational structures in multi-agent robotic systems. His entrepreneurial endeavors include co-founding nootrix , a startup that develops PLC3000 , a SaaS platform dedicated to teaching PLC programming and factory automation, reflecting his commitment to practical technology education. He has supervised PhD students, post-doctoral fellows, and research engineers, demonstrating an active role in mentoring and collaborative research. His scholarly output can be explored via his Google Scholar profile. Professional Engagement: Noury shares insights through his blog (with RSS feed) and is active on LinkedIn , maintaining a public presence in technology and robotics communities. Advising & Collaboration: He has mentored several early-career researchers, including doctoral candidates (as co-advisor), post-doctoral fellows, and research engineers, contributing to a vibrant research team focused on robotics and software innovation. Labs & Teams: While specific lab names are not mentioned, his research activities suggest leadership or active participation in a robotics and software engineering research group at IMT Nord Europe, integrating both academic inquiry and practical tool development.

• Software Engineering
• Artificial Intelligence
• Mobile Robots
• +12 more

Simon Lacoste-Julien is an Associate Professor at Université de Montréal, affiliated with the Department of Computer Science and Operations Research (DIRO). He also serves as the Associate Scientific Director of Mila – Quebec Institute of Artificial Intelligence and holds the position of Vice President Lab Director at Samsung SAIT AI Lab Montreal (SAIL). His research focuses on machine learning, optimization, and their applications in areas like deep learning, generative models, causality, and computer vision. Lacoste-Julien has held academic positions at INRIA in Paris and has a PhD from UC Berkeley, with postdoctoral work at the University of Cambridge. He teaches advanced graduate courses on probabilistic graphical models and structured prediction. His work includes contributions to optimization algorithms (e.g., Frank-Wolfe methods), causal discovery, and generative models. Lacoste-Julien has supervised numerous students and postdocs, and his awards include being a CIFAR Fellow and Canada CIFAR AI Chair. His research spans theoretical foundations and practical applications, with a strong emphasis on scalable and efficient machine learning techniques.

• Machine Learning
• Optimization
• Deep Learning
• +5 more

Nicolas Ballier is a Professor at Université Paris Cité (formerly Université Paris Diderot), where he teaches and conducts research in linguistics and digital humanities. Previously, he taught at the Université de Rouen and Paris 13. His work focuses on the intersection of linguistics, computational methods, and language learning technologies, with particular expertise in neural machine translation and speech processing. His primary research interests include: Corpus prosody Neural machine translation Automatic analysis of learner corpora Digital humanities Epistemology of linguistics (third revolution of grammatisation) Interpretability of neural machine translation systems Representation of speech in Whisper audio models Dr. Ballier's recent work explores how computers transform linguistic data (the 'third revolution of grammatisation'), with a focus on neural machine translation interpretability and speech analysis using large language models like Whisper. His research bridges theoretical linguistics with practical applications in language learning and translation technologies, particularly focusing on how these technologies can be made transparent and useful for translators and language learners. His 15 most recent publications (2022-2024) demonstrate a strong focus on neural machine translation interpretability, Whisper applications for language assessment, and learner corpus analysis. The publications span multiple prestigious venues including EAMT, ACL, LREC-COLING, and specialized journals in speech technology and computational linguistics, showing consistent productivity and impact in his fields. Dr. Ballier has been PI or team member on numerous European-funded projects including DOKTORAND (2012-2016), KVARK project (2014-2026), PHC Ulysses (2019), multitraiNMT (2021), and LT-LIder project (2024-Nov 2026). He has developed platforms like PAPTAN for neural machine translation experiments and MAKE-NMT VIZ for investigating machine translation interpretability. He has supervised PhD students through collaborative projects and has been involved in research initiatives like DLLA (Deep Learning for Language Assessment) exploring CEFR levels with keylog data, Neuroviz (2021-2022), and SPECTRANS (2020-2022) focusing on specialized neural translation and probing information flow in neural networks.

• Corpus prosody
• Neural machine translation
• Automatic analysis of learner corpora
• +5 more

Olivia Chevalier is a researcher at Institut Mines-Télécom Business School , affiliated with the Bopa Chair (Augmented Operating Room) and the PEPR O2R (Origins of Organic Robotics) program. She collaborates with institutions like AP-HP, University Paris-Saclay, CEA, INRIA, and CNRS. PhD (2008) , DEA (2003), and Master of Philosophy (2000) from Université Paris-Nanterre Her research focuses on the economic, geopolitical, social, and ethical implications of AI and biotechnologies , with a specialization in philosophy of science and epistemology . She explores how AI integrates into surgical practices, digital twins, and organic robotics. Recent publications highlight trends in AI-driven surgical innovation , ethical AI frameworks , and robotics in healthcare , emphasizing interdisciplinary collaboration and technological challenges. Summa cum laude in all academic degrees from Université Paris-Nanterre Chevalier contributes to science dissemination through teaching roles at Sciences Po , Ecole des Ponts et Chaussés , and MYPREPA , covering AI, biotechnologies, and socio-political issues. She is involved in the GdR Robotics and Society network, bridging technical and societal perspectives.

• Artificial Intelligence Ethics
• Bioethics
• Philosophy of Science
• +4 more

Robert Voyer is an Associate Professor at Institut Mines-Télécom Business School (IMT-BS), specializing in Management Information Systems. His work bridges computer science and ethical considerations in technology. PhD in Computer Science & Artificial Intelligence (University of Paris 6, 1989) Active in teaching and research since 2000 Focus areas: object-oriented languages, knowledge representation, J2EE architecture, and ethical implications of AI His research explores intersections of: Autonomous agent ethics Big data platforms (Hadoop, Spark) Enterprise architecture patterns Philosophy of knowledge in technical systems Recent publications address ethical conflict resolution in multi-agent systems and responsible AI implementation. He contributes to interdisciplinary collaborations across French institutions including CNRS, EHESS, and ONERA. Teaching engagements include: Leading courses on AI fundamentals (INF 6701, INF 6704) Coordinating data science modules (MIS 5520) Designing curriculum on enterprise architecture (INF 6708) Addressing data ethics (MAN 6704, MAN 6707)

• Management Information Systems
• Object-Oriented Programming
• Knowledge Representation
• +4 more

Marie Puren is an Associate Professor in history and digital humanities at EPITECH Paris , where she leads the Digital Methods for Humanities and Social Sciences (MNSHS) team since 2022. She also holds research affiliations with the LRE laboratory , Jean-Mabillon Center , and LARHRA in Lyon. Her work bridges historical analysis with computational techniques, focusing on political ideologies, cultural heritage preservation, and digital methodologies. Her research interests include: Digital Humanities and Computational History Political Ideologies in the French Third Republic Cultural Heritage Modeling Textual Analysis with NLP and XML-TEI History of Publishing and Literary Collaboration Marie contributes to major projects like AGODA (parliamentary debates analysis) and SILKNOW (silk heritage preservation). She teaches courses on digital methodologies, historical research, and environmental impacts of technology. Her leadership in Humanistica and participation in international conferences highlight her academic influence.

• History
• Digital Humanities
• Political Ideologies
• +2 more

Fabrice Boissier is an Associate Professor at EPITA, specializing in digital methods for humanities and social sciences. He earned his PhD and MSc from Université Paris 1 Panthéon-Sorbonne, focusing on knowledge extraction and reuse in knowledge-intensive processes, enterprise modeling for decentralized organizations, and applications of formal concept analysis, natural language processing, and data visualization. Current Research: Formal Concept Analysis, Topic Modeling, Text Processing, Data Visualization, Knowledge Extraction, and Knowledge-Intensive Processes. Collaborations: Working with Nida Meddouri in the Security and Systems team and Marie Puren in the DMHSS (MNSHS) team. Teaching: Courses in algorithmics, computer architecture, programming languages, and operating systems at EPITA's Bachelor CyberSécurité program. Supervision: Mentoring research and industry interns from EPITA, Université de Sousse, and Université Paris 1 Panthéon-Sorbonne.

• Formal Concept Analysis
• Topic Modeling
• Text Processing
• +7 more

Sam Goree is an assistant professor of computer science at Stonehill College, specializing in the intersection of computer vision, human-computer interaction, and digital humanities. His work critically examines how AI systems evaluate aesthetic quality and human-centered aspects of AI, with particular focus on subjectivity in machine learning models and evaluation methodologies. PhD in Informatics from Indiana University's Luddy School of Informatics, Computing and Engineering Goree's research explores aesthetic phenomenon problems in computer vision, particularly image aesthetic quality assessment. He investigates how subjective human perceptions of beauty and aesthetics can be modeled and evaluated in AI systems, challenging traditional approaches that assume a universal standard of beauty. His work draws from feminist theory, philosophy of science, and media archaeology to develop more human-centered evaluation methods for AI systems. Goree is particularly interested in how subjectivity manifests in both data and models, and how this affects algorithmic bias. His recent publications reveal a strong trend toward human-centered evaluation of AI systems, with increasing focus on subjectivity, personalization, and philosophical foundations of aesthetics in computer vision. Goree's work bridges technical computer vision research with critical humanities perspectives, examining how historical contexts shape current AI practices. He has developed novel evaluation frameworks that incorporate qualitative human feedback, moving beyond traditional quantitative metrics to better understand how AI systems interact with human perception and values. Goree actively engages with teaching challenges in the era of generative AI, developing pedagogical approaches that help students develop critical thinking skills while navigating new AI tools. His teaching philosophy emphasizes maintaining human-centered learning experiences even as AI transforms educational landscapes. He advocates for assignments that require contextual understanding and creativity, which are difficult for AI to replicate. His research is organized around understanding the "aesthetic gap" - the difference between information extractable from image pixels and the feelings images evoke in humans. Goree's lab explores how to build more transparent and accountable AI systems that acknowledge their subjective nature rather than claiming false objectivity.

• computer vision
• human-computer interaction
• digital humanities
• +4 more

Christine Largouet is an Associate Professor in Computer Science at Institut Agro Rennes-Angers and a member of the IRISA Laboratory's LACODAM team. Her research focuses on Explainable AI, Complex Systems Modelling, and applications in Ecosystem Management and Agroecology. She leads the Computer Science Teaching Unit at Institut Agro Rennes-Angers since 2006 and has held academic roles at the University of New Caledonia (2003-2006) and IRD Nouméa. She holds a HDR (2019) and PhD (2000) from Université de Rennes 1. Education: Habilitation Defense (HDR): "Artificial Intelligence for Decision Support of Dynamical Complex Systems" (Université de Rennes 1, 2019) PhD: "Interpretation of a sequence of remote-sensing images" (Université de Rennes 1, 2000) Research Interests: Her work bridges AI and environmental/agricultural systems, with emphasis on: Explainable AI for decision-making Machine learning in precision agriculture Timed behavioral models & discrete event systems Data-driven approaches for ecosystem management Publications Trends: Recent work emphasizes: AI explainability in user-centric applications Livestock management using sensor-based machine learning Formal methods for system modeling (timed automata) Interdisciplinary applications in agroecology Teaching & Supervision: Supervised 17+ PhD/Master students since 2002 Teaches programming, AI, and data science at multiple academic levels Labs & Teams: Active contributor to: IRISA Laboratory's LACODAM team Collaborations with INRAE and INRIA

• Artificial Intelligence
• Explainable AI
• Complex Systems Modelling
• +6 more

Allel Hadjali is a Full Professor in Computer Science specializing in Data Engineering at ISAE-ENSMA (École Nationale Supérieure de Mécanique et d'Aérotechnique) in Poitiers, France. He is affiliated with the Laboratory LIAS (Laboratoire d'Ingénierie des Applications de la Connaissance et des Systèmes) at ISAE-ENSMA. His academic career includes progression from Associate Professor to his current Full Professor position, with extensive teaching experience across multiple computer science domains. Professor Hadjali's research falls within the data science domain, with particular focus on Exploitation, Extraction, and Recommendation (E2R). His work applies Computational Intelligence and Soft Computing techniques to massive data exploitation and analysis, including flexible querying approaches (Skyline, Gradual, and Bipolar queries), modeling and querying uncertain/incomplete data, cooperative answering techniques, and data reduction through linguistic summaries. He also conducts research in recommendation systems (learning-based and group recommendation) and extraction techniques (mining gradual patterns), along with related interests in data quality, intelligent systems, and crowdsourced data management. His publication record demonstrates consistent contributions to top-tier journals and conferences, with recent work focusing on skyline query processing, uncertain data management, RDF knowledge bases, and explainable AI. His research shows a clear trajectory from foundational work in fuzzy logic and uncertain databases toward more applied research in semantic web technologies and machine learning explainability. Professor Hadjali serves on the editorial boards of several prestigious journals including the Journal of Smart Environments and Green Computing, Sensors Journal, and the Universal Journal of Aeronautics and Aerospace Research. He has also organized special issues on topics such as uncertainty in cloud computing and managing uncertain data. At ISAE-ENSMA, Professor Hadjali teaches courses including Formal aspects of software engineering, Language interpretations and compilation, Programming languages, and Data management and exploitation. Previously as an Associate Professor, he taught courses on object modeling, distributed algorithms, operating systems, and advanced databases focusing on preferences and uncertainty. He leads the Data Engineering team within the Laboratory LIAS, which focuses on developing computational intelligence approaches for modern data challenges. His current projects include work on data quality (QDoSSI project funded by CNRS Mastodons 2016-2018) and research actions in GDR MADICS 2018 related to scientific data quality.

• Data Science
• Computational Intelligence
• Soft Computing
• +12 more

Professor Dominique Vaufreydaz holds a position in Computer Science at Grenoble Alpes University and leads the Multimodal Perception and Sociable Interaction (M-PSI) team at the LIG laboratory. His academic career includes roles as Associate Professor (2005–2023) and Head of the M-PSI team. He specializes in multimodal perception, affective computing, and sociable human-robot interaction, with projects like ARAS dataset and TokenCut object discovery. Education details are not explicitly provided, but his research spans robotics, autonomous systems, and healthcare technologies. He has published extensively in top-tier conferences (CVPR, ICMI) and journals (ACM Transactions, IEEE TPAMI). Over 30 PhD students have been supervised, with current advisees exploring activity recognition, head motion generation, and emotion analysis. Teaching roles include 300+ hours annually across computer science courses (C++, databases, mobile programming) and numerical literacy for non-specialists. He co-leads the MOSIG Master's GVR specialty and manages transversal courses at the Grenoble Faculty of Economics. His labs, notably M-PSI, focus on smart spaces, ambient assisted living, and ethical teaching analytics. Notable collaborations include EU projects (FAME, CHIL) and industry partnerships like Orange Labs. His work bridges cognitive science and machine learning, with applications in healthcare and urban autonomous systems.

• Multimodal Perception
• Affective Computing
• Robotics Interaction
• +5 more

Hugo BRUNELIERE is an Enseignant Chercheur (Teaching & Researcher) at IMT Atlantique, Nantes, affiliated with the NaoMod team within the LS2N laboratory. His primary roles involve leading innovative projects on software and systems engineering, focusing on model-based techniques and their applications in Cloud/Fog Computing, Cyber-Physical Systems, and Digital Twins. He holds a position in the Department of Automation, Production and Computer Sciences (DAPI). His research spans Model-Driven Engineering (MDE), tool interoperability, and industrial collaboration through projects like MOSAICO, MATISSE, and AIDOaRt. He has collaborated with companies such as Orange, Sopra Steria, and BNP Paribas on R&D contracts. Research Interests : - Model Management & Traceability - Model-Based Reverse Engineering - Cloud/Fog System Architecture - Cyber-Physical Systems Design - Tool/Language Interoperability - Open Source Contributions (Eclipse MoDisco, ATL). Awards : Best Paper at SAC 2024 (VeriFog) Best Paper in SoSyM Journal 2018 (Model View Survey) Best Paper at CLOSER 2018 (Cloud Systems Modeling) Grants & Projects : European Projects: MOSAICO (AI Communities), MATISSE (Digital Twins), AIDOaRt (DevOps Automation) National Projects: SeMaFoR (Fog Management), CoMe4ACloud (Autonomic Clouds) He also leads open-source initiatives in the Eclipse community and organizes Eclipse DemoCamps. Labs & Teams : Part of the NaoMod team (LS2N) and actively contributes to the MegaM@Rt2 and ARTIST projects.

• Model-Driven Engineering
• Cloud Computing
• Fog Computing
• +3 more

Giovanna Maria Dimitri is an Assistant Professor Tenure Track in Artificial Intelligence at Universitá degli Studi di Milano (Statale), with additional affiliations at the ICE, University of Cambridge, and the Dipartimento di Ingegneria dell'Informazione e Scienze Matematiche (DIISM) at the University of Siena. She earned her PhD in Artificial Intelligence from the University of Cambridge under Prof. Pietro Liò, focusing on multilayer network methodologies for brain data analysis. She holds an MPhil in Advanced Computer Science from Cambridge with distinction and completed her Master’s and Bachelor’s in Computer and Automation Engineering at the University of Siena, both with top honors. PhD in Artificial Intelligence – University of Cambridge, UK MPhil in Advanced Computer Science – University of Cambridge, UK (Distinction) Master’s & Bachelor’s in Computer and Automation Engineering – University of Siena, Italy (110/110 cum laude) Her research spans a broad spectrum of artificial intelligence, including foundational models, deep learning, brain data modeling, and applications in healthcare, environmental science, and sustainability. She is particularly known for her work on GAN detection, emotional image datasets, climate change impact modeling, and AI for Sustainable Development Goals. Her interdisciplinary approach integrates computer science with neuroscience, public health, and social impact. Her recent publications reflect a strong trend in applying AI to real-world problems such as healthcare diagnostics (e.g., Brugada Syndrome detection), environmental monitoring (air quality, climate change on agriculture), and ethical AI (CO2 emissions of ML models). She also contributes to digital humanities and science communication, indicating a commitment to societal engagement and interdisciplinary collaboration. She has received the competitive Ai-Net Fellows Scholarship from DAAD in 2023, enabling collaboration with Prof. Gemma Roig’s lab. She is an Associate Editor for Neurocomputing (Elsevier) and was elected Associate Editor of IEEE Transactions on Technology and Society in May 2024. Dimitri has extensive teaching experience, lecturing Business Intelligence at the University of Siena and serving as a Guest Lecturer in Data Science at the University of Cambridge’s Institute of Continuing Education. She has supervised numerous students and has a publication record of nearly 60 peer-reviewed papers. She is also active in science communication, having been interviewed by Italian media and appearing on Rai Radio 1. She is a life member of Clare Hall College, University of Cambridge, and continues to contribute to academic and public discourse on AI through seminars, workshops, and editorial leadership.

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +12 more