جستجوی استاد راهنمای دانشگاهی

• Computer Science
• Artificial Intelligence
• Data Mining
• +۳ مورد دیگر

Cyril De Runz is an Associate Professor at the University Institute of Technology of Blois (IUT Blois), University of Tours, France, affiliated with the Department of Multimedia and Internet Professions and the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT, UR 6300). He also contributes to the Polytechnic School of the University of Tours (EPU), focusing on interdisciplinary computer science applications. His research spans Artificial Intelligence, Data Mining, Wireless Sensor Networks, Geographic Information Systems, and Human-Computer Interaction. Key areas include cycling infrastructure analysis using OpenStreetMap data, automatic dashboard generation for business intelligence, employee attrition modeling through sequential pattern mining, and optimization of sensor network coverage. His work bridges theoretical computer science with practical urban planning and human resources applications. Analysis of his 2023-2025 publications reveals strong trends in edge-AI deployment (e.g., ternarized vision-language models), advanced wireless sensor network strategies, and integration of multi-source data for transportation and workforce analytics. His methodologies often combine probabilistic modeling, fuzzy logic for imperfect geographic data, and deep learning adaptations. De Runz actively participates in LIFAT laboratory research, emphasizing interdisciplinary collaboration across civil engineering, social sciences, and energy systems. His projects frequently address real-world challenges like cycling safety, urban mobility, and business intelligence automation. No verifiable information on student advising, research grants, or scientific awards was found in the provided sources.

Nathalie Friburgerمشاهده پروفایل

دانشیار

• Natural Language Processing
• Named Entity Recognition
• Finite State Automata and Transducers
• +۳ مورد دیگر

Nathalie Friburger is an Associate Professor in Computer Science at the University Institute of Technology of Blois, affiliated with the University of Tours. She is a member of the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT) and the BDTLN Research Team since 2003. Her academic work spans teaching in algorithms, programming, web development, and operating systems, while her research focuses on Natural Language Processing with particular expertise in named entity recognition and finite state transducers. Her research interests center on Natural Language Processing, specifically the automatic extraction of named entities using finite state automata and transducers. She has developed significant tools like CasSys for working with cascades of transducers within the Unitex text processing system. Her work extends to Arabic language processing, Renaissance text analysis, and cultural heritage applications, demonstrating both theoretical depth and practical implementation across multiple languages and domains. Analysis of her recent publications reveals a consistent focus on named entity recognition systems, with increasing specialization in Arabic language processing alongside continued work on French NER. Her research integrates rule-based approaches with data-driven methods, showing evolution from foundational work on transducer cascades toward more sophisticated hybrid systems and applications in digital humanities. The development of CasSys and related tools demonstrates her commitment to creating open, reusable infrastructure for the NLP community. She has served as co-supervisor for PhD students Damien Nouvel (2009-2012) and Fatma Ben Mesmia Chaabouni (2014-2019), contributing to the next generation of researchers in computational linguistics. Her research has been supported by multiple projects including ISTEX (2014-2017), Ortolang (2013-2014), Renom (2011-2013), and others focused on named entity recognition and text processing. Friburger has been actively involved in the academic community as Program Committee Member for TALN conferences (2013-2018), director of the TAL magazine on Named Entities (2013), and reviewer for numerous conferences including EMNLP and CIAA. She has also contributed to book editing, notably for TALN 2001 proceedings and the TAL Review on Named Entities (2013).

• Data Mining
• Pattern Mining
• Database Systems
• +۳ مورد دیگر

Arnaud Giacometti is a Professor at the University of Tours, affiliated with the Faculty of Science and Technology and the Computer Science Department. He is also the President of the University and leads research at the Tours Fundamental and Applied Computer Science Laboratory (LIFAT). Research Interests: His work focuses on data mining, pattern discovery, and knowledge graph analysis. He has developed innovative methods for frequent pattern mining, sequential pattern sampling, and constraint-based clustering, emphasizing efficiency and scalability in large datasets. Recent Publications: His research trends include trie-based sampling, reservoir pattern sampling in data streams, and semantic web-driven comparison tables. Key subfields span distributed databases, knowledge graph representativeness, and memory-aware parallelization for big data. Scientific Roles: As President of the University of Tours, he oversees institutional leadership while maintaining active research contributions. His laboratory (LIFAT) serves as a hub for fundamental and applied computer science research.

• Pattern Mining
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Dominique LI is a Professor in the Department of Computer Science at the University of Tours, affiliated with the Fundamental and Applied Computer Science Laboratory (LIFAT) and the Polytechnic School of the University of Tours (EPU). His research focuses on pattern mining, skyline computation, and data mining applications in domains like lithium-ion battery analysis and fluid mechanics. His recent publications highlight advancements in sequential pattern sampling, dynamic texture classification, and skyline maintenance for data streams. Keywords span Computer Science Machine Learning Database Systems Fluid Dynamics Text Classification Algorithm Design .

Veronika Peralta Costabelمشاهده پروفایل

دانشیار

• Data Quality
• Data Warehousing
• Exploratory Data Analysis
• +۷ مورد دیگر

Veronika Peralta Costabel is an Associate Professor at the University of Tours and a key member of the LIFAT Computer Science Laboratory . She serves as Head of the Master 2 SIAD program and has held academic positions at institutions in France, Uruguay, and Argentina since 1996. PhD in Computer Science (2006) from University of Versailles and University of the Republic of Uruguay Master in Computer Science (2001) and Engineer degree (1998) from University of the Republic of Uruguay Her research focuses on Data Quality , Exploratory Data Analysis , and Query Personalization in heterogeneous and distributed systems. She has led projects on data narratives in journalism, public health, and urban mobility. Recent article trends emphasize context-aware data quality frameworks , interestingness measures for OLAP queries , and automated data storytelling models , particularly in big data and cloud environments . Scientific Awards : Young Researcher Prize (2005), CLEI-UNESCO 3rd Prize (2003), LIFAT Council Member (2018-2019) She supervises PhD and Master students in topics like SQL workload analysis, data narrative generation, and semantic trajectory clustering, and contributes to journal editorial work and conference organization .

• Signal Processing
• Optimization
• Sensitivity Analysis
• +۳ مورد دیگر

Hugo Raguet serves as an Associate Professor at INSA Centre-Val de Loire in Blois, France, with research affiliations at the Fundamental and Applied Computer Science Laboratory (jointly associated with University of Tours). His work bridges theoretical computer science and applied mathematics, focusing on algorithmic solutions for real-world computational challenges. His research expertise spans: Signal Processing: Developing efficient algorithms for real-time applications and medical imaging Optimization: Advancing convex and non-smooth optimization techniques with graph-based regularization Sensitivity Analysis: Pioneering targeted methods using dependence measures for domain-specific phenomena Image Processing: Extending raster-to-vector conversion to multicolor systems Publication analysis reveals consistent innovation in splitting methods (Douglas-Rachford, forward-backward) and preconditioning techniques, with strong emphasis on computational efficiency for graph-structured problems. His work frequently bridges mathematical theory with practical implementations in signal processing and machine learning. No scientific awards are documented in available sources. Student advising and grant information remain unspecified. Raguet actively contributes to the Fundamental and Applied Computer Science Laboratory, where he develops open-source tools for academic collaboration including live conferencing software and LaTeX templates for scientific communication.

• Wood Science
• Materials Science
• Non-Destructive Testing
• +۳ مورد دیگر

Louis Denaud is a Full Professor and Director of LaBoMaP (Laboratory of Mechanical and Physical Properties of Materials) at Arts et Métiers ParisTech. His work focuses on the mechanical and physical characterization of wood and engineered wood products, with emphasis on non-destructive evaluation and sustainable applications in structural engineering. His research spans Wood Science , Materials Engineering , and Non-Destructive Testing . Key interests include X-ray and terahertz imaging for wood property analysis, deep learning applications for defect detection in veneers, mechanical grading of heterogeneous wood materials, and sustainable wood composites for structural applications. He investigates fiber orientation effects on mechanical behavior, optical characterization via the tracheid effect, and processing parameter optimization for veneer production. Analysis of his 2023-2025 publications reveals dominant trends in integrating advanced imaging techniques (terahertz, X-ray, hyperspectral) with machine learning for wood characterization, alongside significant focus on sustainable wood utilization in mobility applications and mechanical grading for stiffness-optimized structural beams. His work bridges fundamental wood physics with industrial applications in plywood, LVL, and agroforestry systems. No scientific awards are mentioned in the source material. Information regarding student advising and research grants is not provided in the available text. As Director of LaBoMaP (research unit EA3633), Denaud leads a team specializing in wood mechanics and non-destructive testing. The laboratory conducts research on wood drying processes, veneer production parameters, terahertz densitometry (BOOST project), optical wood characterization (WOOPS project), and sustainable wood composites for structural applications.

• Natural Language Processing
• Computational Linguistics
• Machine Learning for NLP
• +۷ مورد دیگر

Pierre Magistry is a Lecturer at the Institut National des Langues et Civilisations Orientales (INALCO) specializing in language sciences with a focus on computational linguistics and Asian languages. His academic work bridges computer science and linguistics with particular emphasis on multilingual systems. His primary research interests include: Diversity of Sinitic languages NLP for under-resourced languages and heritage languages Morphosyntax and segmentation Digital Humanities applications Lexical networks Magistry's teaching portfolio centers on Machine learning for NLP, General linguistics (morphosyntax), and supervised projects in NLP. He works within the CNU Discipline 07 - Language sciences: general linguistics and phonetics framework. As an active researcher, he leads the ANR JCJC DiLSi-HN project and is affiliated with the ERTIM research team. His linguistic expertise spans Chinese and Taiwanese languages, with a geographical focus on Asia.

Romain Grandchampمشاهده پروفایل

پژوهشگر

• Neuroscience
• Cognitive Science
• Brain-Computer Interface
• +۳ مورد دیگر

Romain Grandchamp is a Researcher at Université Grenoble Alpes , affiliated with the Laboratoire de Psychologie et Neurosciences Cognitives (LPNC) and the Consciousness, Memory and MetaCognition (CoMMet) team. His work bridges neuroscience , brain-computer interfaces (BCI) , and cognitive modeling to study mental states , mind wandering , and inner speech . PhD in EEG and BCI (2012, Université Paul Sabatier) Developed tools like NeuroTRIP and Brainarium for BCI training and immersive neuroeducation Focus on electromyographic correlates of rumination, ICA decomposition , and neurofeedback applications Prior research includes neuroplasticity in meditation practitioners and verbal rumination mechanisms. Collaborations span institutions in France, Australia, Netherlands, and Morocco.

• Multi-source Sensor Fusion
• Semantic Mapping
• Collaborative Robotics
• +۵ مورد دیگر

Hiba Al-Assaad serves as a Teacher-researcher at CESI Higher Education Group in Toulouse, France, specializing in Engineering and Digital Tools within the Computer Networks and Telecommunications domain. Her work bridges academic instruction in preparatory and engineering cycles with advanced research in robotics and navigation systems. Her research focuses on multi-source sensor fusion, semantic mapping for mobile robotics, and cyber-physical systems. Key areas include developing robust navigation algorithms using digital elevation models, creating decentralized decision systems for industrial applications, and advancing federated learning frameworks. Her work consistently addresses real-world industrial challenges through collaborative robotics and smart transportation solutions. Analysis of her recent publications reveals a strong trajectory toward industrial cyber-physical systems, with increasing emphasis on federated learning, meta-algorithms, and human-robot collaboration in manufacturing environments. Her research integrates geospatial engineering with machine learning to solve navigation and perception challenges. She actively supervises doctoral candidates including A. Achour (defense 2024) researching real-time semantic mapping with robot swarms, and B. Guendouzi (defense 2025) developing decision support systems for industrial cyber-physical environments. Her technical contributions span from fundamental navigation algorithms to cutting-edge machine learning applications, maintaining consistent output across journals like International Journal of Image and Data Fusion , IEEE CSR , and Journal of Network and Computer Applications .

• Artificial Intelligence
• Decision Support Systems
• Uncertain Data Analysis
• +۳ مورد دیگر

Zeineb El Khalfi is a Teacher-researcher at CESI Bordeaux Campus specializing in artificial intelligence and decision support systems, with research focused on mobility/smart transport and uncertain data analysis. Her academic credentials include: Doctorate from Paul Sabatier University of Toulouse and Higher Institute of Management of Tunis (2017) on "Lexicographic refinement in possibilistic sequential decision-making" Research Master's from École Polytechnique de Nantes (2014) in Data Mining and Knowledge Management Research Master's from Higher Institute of Management of Tunis (2014) in Computer Science and Knowledge Management Her research bridges theoretical AI frameworks with real-world applications, particularly in possibilistic reasoning for decision-making under uncertainty. Early work centered on lexicographic refinements in Markov decision processes and possibilistic decision trees, evolving toward smart mobility solutions where she applies spatio-temporal forecasting to micro-mobility redistribution challenges. This trajectory demonstrates a strategic shift from foundational AI theory to transportation-focused implementations while maintaining core expertise in uncertainty modeling. Analysis of her 2016-2023 publications reveals consistent contributions to possibilistic AI (6 conference papers, 3 journal articles), with recent work emphasizing practical transportation applications through micro-mobility systems and data-driven urban mobility solutions. No information is available regarding student supervision or research grants in the provided materials. She actively contributes to CESI's Engineering and Digital Tools Research Team, leveraging interdisciplinary collaboration to advance smart transportation technologies through computational methods.

• Computer Science
• Graph Theory
• Urban Mobility
• +۵ مورد دیگر

OBEROI Kamaldeep Singh is a teacher-researcher at CESI Toulouse, focusing on spatio-temporal modeling of urban mobility systems and graph-based pattern detection. His work bridges computer science, transportation engineering, and geomatics to address challenges in smart and sustainable mobility. Doctorate in Computer Science, University of Rouen Normandy (2019) MSc in Embedded Electronic Systems, ESIGELEC (2016) BEng in Electronics and Communication, Chitkara University (2014) Research interests span: Dynamic graph theory applications in urban traffic analysis Mobility-as-a-Service (MaaS) personalization frameworks Multi-agent systems for sustainable transportation Reinforcement learning in mobility behavior simulation Spatio-temporal data analytics for smart cities His publications from 2017–2025 demonstrate a sustained focus on graph-theoretic modeling of urban dynamics, with recent work expanding into AI-driven MaaS personalization. Key contributions include pattern detection algorithms and qualitative spatial models for traffic flow optimization. Current affiliations: Scientific Animator, CEE MobE program (ION Team) MIDOC Key Challenge COPIL (Intelligent Mobility) neOCampus Scientific Interest Group R3Mob Regional Network (New Aquitaine) Editorial roles include program committee membership at W2GIS (2023–2024) and ECAI (2024), with reviewing experience for journals on geomatics and sustainability.

• Cyber-Physical Systems
• Security
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Samir Ouchani is a Research Director at CESI Engineering School in Aix-en-Provence, France, with a focus on Cyber-Physical Systems (CPS) , Security , and Artificial Intelligence . He holds a PhD in Computer Science from Concordia University (2013) and an HDR (Habilitation à Diriger des Recherches) from CNAM, Paris (2022). His research emphasizes decentralized AI techniques for securing CPS and formal methods to validate AI-driven approaches. Key areas include Blockchain for smart cities and healthcare IoT Security via PUF protocols Federated Learning in industrial contexts Recent publications highlight 2025: Proceedings of the 6th Summer School on CPS and IoT 2025: Smart Cities Services Review 2024: Federated Learning Client Selection 2024: Blockchain for Healthcare Exchange He co-supervises PhD students in smart security and CPS vulnerability detection , integrating statistical model checking and machine learning frameworks.

Sarah Lemlerمشاهده پروفایل

پژوهشگر

• Non-parametric Estimation
• High-dimensional Statistics
• Survival Analysis
• +۴ مورد دیگر

Sarah Lemler is a Researcher at the Laboratory Mathematics and Computer Science for Complexity and Systems , specializing in high-dimensional statistics and non-parametric estimation methods. Her work bridges mathematical statistics with biomedical applications in oncology and neuroscience. Primary research areas: Survival Analysis, Hawkes Processes, Diffusion Models Key applications: Genomics, Childhood Cancer Survivor Studies, Cardiac Disease Prediction Her recent publications (2024-2025) focus on adaptive estimation in Cox models, variable selection in high-dimensional mixed-effects models, and neural network applications to multivariate Hawkes processes in neuroscience. She contributes to methodological developments for analyzing counting processes and stochastic differential equations with jumps. Collaborations span the French Childhood Cancer Survivor Study (FCCSS) and interdisciplinary projects combining dosiomics with machine learning to predict radiation-induced valvulopathy. Her 2025 thesis "Estimation for counting processes with high-dimensional covariates" represents a culmination of her methodological innovations.

Hugues Talbotمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Image Segmentation
• +۵ مورد دیگر

Hugues Talbot is a researcher affiliated with the Digital Vision Center, specializing in computer vision, machine learning, and optimization. His work spans medical imaging, digital pathology, and mathematical morphology, with recent publications focusing on deep learning applications for tumor segmentation, image restoration, and medical diagnostics. Research Interests: Computer Vision (e.g., image segmentation, mathematical morphology), Machine Learning (e.g., generative models, multiple instance learning), Medical Imaging (e.g., CT/MRI analysis, tumor detection), and Optimization (e.g., maximal flows, convex modeling). Key Projects: Development of algorithms for automated lymphoma prognosis using PET images, deep learning models for breast cancer diagnosis from cytology slides, and physics-informed neural networks for myocardial perfusion simulation. Technical Contributions: Hybrid heart segmentation algorithms, noise density analysis for image splicing detection, and discrete calculus methods for inverse problems in imaging. His work often integrates theoretical mathematical models (e.g., Ambrosio-Tortorelli functional) with practical applications in clinical settings, such as sacroiliitis detection, bone density estimation, and zebrafish brain volumetric analysis.