جست‌وجوی استاد و پژوهشگر

Gabriel Henrique de Oliveira Caetanoمشاهده پروفایل

پژوهشگر ارشد

• Conservation Science
• Macroecology
• Data Science
• +۵ مورد دیگر

Gabriel Henrique de Oliveira Caetano is a Post-doctoral Researcher at the Laboratoire Écologie, Société et Évolution (IDEEV) within the University of Paris-Saclay, specializing in data-driven conservation science and macroecology through the Biodiversity dynamics and macro-ecology team. His research spans Conservation Science, Macroecology, and Data Science with focused expertise in extinction risk assessments, biogeography, conservation culturomics, ecophysiology, and population dynamics—primarily using terrestrial vertebrates as model systems. Caetano develops computational tools to address biodiversity crises, integrating spatial, phylogenetic, and behavioral data to predict species responses to environmental change. Analysis of his recent publications reveals a cohesive trajectory in applying machine learning and large-scale data analysis to global conservation challenges, including reptile extinction risk modeling, climate-driven population declines, and digital footprint analysis for biodiversity monitoring. His work consistently bridges ecological theory with actionable conservation strategies. Scientific Awards: None mentioned in source material. Advising and Grants: No student supervision or grant information available in provided text. Caetano actively contributes to the Biodiversity dynamics and macro-ecology team at IDEEV, which pioneers interdisciplinary research on ecological patterns across spatial and temporal scales to inform planetary-scale conservation policies.

Jean-Michel Guillonمشاهده پروفایل

پژوهشگر

• Evolutionary Biology
• Ecology
• Sex Determination
• +۳ مورد دیگر

Jean-Michel Guillon is a CNRS Researcher (CR1) at the Laboratoire Écologie, Saclay et Évolution (IDEEV) affiliated with Université Paris-Saclay, where he has been active since 1998. His research integrates evolutionary theory, ecological fieldwork, and molecular biology to investigate environmental sex determination mechanisms across plant and reptile systems. As a qualified HDR supervisor, he directs research within the 'Ecological Processes and human Impact' team. His academic background includes: PhD in Biology from École Polytechnique (1989-1992) focusing on bacterial translation mechanisms DEA in Fundamental Bases of Oncogenesis (Université Paris 11, 1987-1988) Agrégation in Biochemistry-Biological Engineering (1986-1987) Studies at École Normale Supérieure de Cachan (Section Biochemistry, 1984-1988) Guillon's research centers on environmental sex determination (ESD), particularly in horsetails (Equisetum) and turtles. He pioneered experimental demonstrations of how light intensity and sugar availability manipulate sex ratios in Equisetum gametophytes, challenging classical Charnov-Bull models by revealing dispersal dynamics as critical evolutionary drivers. His theoretical frameworks incorporate spatially explicit modeling of sex-biased dispersal and habitat heterogeneity. In reptile studies, he established causal links between nest-site selection, temperature gradients, and skewed sex ratios in turtles, providing foundational models for climate change impact assessments. His phylogenetic work on Equisetum using chloroplast DNA redefined taxonomic classifications by questioning subgeneric monophyly. Analysis of his 15 most recent publications reveals consistent interdisciplinary integration across evolutionary biology, ecology, and statistics. Key trends include: (1) Progressive refinement of sex ratio models incorporating spatial/temporal environmental variation; (2) Methodological innovation in thermal biology (e.g., embryo growth modeling for sex determination); (3) Cross-taxon synthesis of ESD mechanisms from plants to reptiles; (4) Increasing focus on anthropogenic impacts, particularly climate-driven sex ratio distortions in vulnerable turtle populations. His work bridges theoretical predictions with conservation applications, exemplified by global change vulnerability assessments for TSD species. Guillon has supervised doctoral research including Vincent Hulin's thesis on turtle nest-site selection and TSD evolution. His collaborative network spans CNRS laboratories, Université Paris-Saclay, and international herpetology research groups, supported by institutional funding from CNRS and university frameworks. Current projects continue to explore dispersal-sex ratio interactions in heterogeneous landscapes. He operates within the 'Ecological Processes and human Impact' research team at IDEEV laboratory (Bâtiment 680, Gif-sur-Yvette), utilizing controlled environment facilities for Equisetum gametophyte cultures and computational resources for theoretical modeling. Fieldwork components involve turtle nesting site monitoring across European habitats, integrated with climate data modeling.

Nicolas Audebertمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Representation Learning
• +۵ مورد دیگر

Nicolas Audebert is a Computer Vision and Machine Learning researcher working as a junior research director at the French National Institute of Geographic and Forest Information (IGN) in the LASTIG laboratory, STRUDEL team. He is currently on leave from his position as Associate Professor of Computer Science at the Conservatoire national des arts et métiers (Cnam) where he was part of the Vertigo team. His research spans computer vision, machine learning, and Earth Observation with applications in remote sensing and video games. Dr. Audebert earned his PhD in Computer Science from ONERA and IRISA in 2018, followed by an MEng in Computer Science from Supélec and an MSc in Human-Computer Interaction from Université Paris-Sud in 2015. In May 2025, he successfully defended his habilitation à diriger des recherches (HDR) titled "Learning representations from observations". His research focuses on representation learning , where he develops methods to create abstract representations of raw data that allow computers to manipulate high-level concepts numerically. In Earth Observation , he processes and makes sense of large volumes of satellite data for land cover mapping, change detection, and image interpretation. His work in machine learning for games explores how to use reinforcement learning to generate diverse and challenging AI in video games. Additional research interests include generative models, multimodal learning, and domain adaptation techniques. His recent publications demonstrate expertise in cross-sensor learning, super-resolution of satellite imagery, diffusion models for Earth Observation, and robust image retrieval systems. His work bridges theoretical advances in deep learning with practical applications in geospatial analysis, with a particular focus on developing methods that work across different sensor types and environmental conditions. Outstanding Reviewer for ECCV 2024 Outstanding Reviewer for BMVC 2021 Outstanding Reviewer for ICCV 2021 Best Benchmarking Contribution Award at GEOBIA 2016 2nd best student paper award at JURSE 2017 Google Research Scholar Program gift Dr. Audebert currently advises four PhD students: Maxime Merizette (semantic segmentation of 3D point clouds), Georges Le Bellier (domain adaptation for Earth Observation), Léo Géré (generative models for music), and Aimi Okabayashi (super-resolution of satellite image time series). He has successfully supervised two PhD students to completion: Perla Doubinsky (controlling generative models) and Elias Ramzi (robust image retrieval), whose thesis won the AFRIF PhD award 2024. He has mentored numerous MSc students on diverse topics including flood detection, procedural generation of video game levels, and deep learning for communication systems. He leads the MAGE project (2022-2026), funded by the Agence Nationale de la Recherche, which investigates using procedural generation and modern rendering engines to create labeled synthetic data for Earth Observation models, particularly for disaster mapping applications. He also leads the SESURE project (2021-2023) focused on super-resolution of Sentinel-2 time series, and previously led the RL-Games project (2020-2022) exploring reinforcement learning applications for video games.

Catherine Dominguèsمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Cartography
• Data Mining
• +۳ مورد دیگر

Catherine Dominguès is a senior researcher at the French National Mapping Agency (IGN) specializing in Natural Language Processing for Cartography within the MEIG (Mediation and Enrichment of Geographic Information) research team. Her work bridges computational linguistics and geographic information science, focusing on the intersection of language analysis and spatial data representation. Education : Habilitation à diriger des recherches (HDR) in Natural Language Processing (2020), PhD in Computer Science (2001), DEA in Theoretical Foundations of Computer Systems (1989) Research Interests center on places and placenames , expressive variations in toponym spelling , location-based information retrieval , and sensitive cartography . Her methodology combines data mining , fuzzy spatial reasoning , and geovisualization techniques. Key publication trends show her work connects NLP for map analysis , mountain rescue support systems , and narrative-based mapping . She contributes to heterogeneous data integration and fuzzy logic applications in geographic contexts. Supervision : Co-supervised Dong Chuanming (PhD) and previously supervised Firas Bessadok (PhD) Projects : Core member of the CHOUCAS project (2017-2022) integrating heterogeneous data for mountain rescue, and involved in MATRICIEL (2016-2017) mapping migrant perceptions

Bruno Valletمشاهده پروفایل

پژوهشگر

• Geographic Information Science
• Computer Vision
• Remote Sensing
• +۷ مورد دیگر

Bruno Vallet is a Senior Researcher at IGN (French National Institute of Geographic and Forest Information) within the LASTIG lab and leads the ACTE research team since 2019. His work focuses on geospatial data processing, including LiDAR and image registration, 3D urban modeling, and computer vision applications. He contributes to projects like AI4GEO and Time Machine , specializing in large-scale point cloud analysis and structured city reconstruction. Education : Habilitation (HDR) in Geographic Information Science (Univ Paris-Est, 2016), PhD in Computer Science (Institut National Polytechnique de Lorraine, 2008), and Master's in Computer Vision (Telecom ParisTech, 2005). His research integrates surface reconstruction , semantic labelings , and uncertainty propagation , with methodologies applied to autonomous navigation and urban change detection. Recent publications emphasize data fusion, visibility computation, and deep learning for 3D scene analysis. He co-supervises PhD students and leads teaching activities at ENSG (National School of Geographic Sciences), covering image processing and 3D data structures. Bruno Vallet also chairs the ISPRS Working Group II/4 on 3D Scene Reconstruction, demonstrating leadership in photogrammetry and remote sensing communities.

Arnaud Labourelمشاهده پروفایل

مدرس

• Implicit representation of graphs
• Edge partitioning
• Universal graphs
• +۴ مورد دیگر

Arnaud Labourel is a Lecturer at Aix-Marseille University, affiliated with the LIS (Laboratory of Informatics and Systems) and the DALGO team. He previously worked at the University of Provence (2010-2012) and has held positions at institutions including LaBRI (University of Bordeaux 1), ENSEIRB, and the University of Quebec in Outaouais (UQO). Current Position: Lecturer at Aix-Marseille University (LIS, DALGO team) Past Positions: Lecturer at University of Provence (2010-2012), Research Engineer at LIF (2010), ATER at University of Bordeaux 1 (2009-2010), Post-doc at UQO (2008-2009), Half-ATER at ENSEIRB (2007-2008) His research focuses on theoretical computer science, particularly in graph theory and distributed algorithms. Key topics include implicit graph representation, edge partitioning, universal graphs, and robotic exploration of unknown environments. His work often intersects with graph theory, computational geometry, and algorithmic challenges in network design.

Jean-Loup Guillaumeمشاهده پروفایل

استاد

• Graph Theory
• Network Analysis
• Algorithms
• +۵ مورد دیگر

Jean-Loup Guillaume is a Professor at La Rochelle University since October 2014, affiliated with the L3i Laboratory (Computer Science, Image and Interaction Laboratory) within the 'Models and Knowledge' team. He is renowned for co-developing the Louvain method in 2007, a groundbreaking algorithm for community detection in networks with over 20,000 citations. His research focuses on network science, aiming to understand and model complex systems such as social networks, neural connections, and e-commerce interactions. He emphasizes interdisciplinary applications, spanning medicine, history, geography, sociology, and marketing. Current projects include network robustness, urban mobility analysis, social influence mechanisms, and energy-efficient distributed application deployment. The Louvain method revolutionized network analysis by enabling rapid processing of massive datasets (up to billions of connections) with high accuracy. The method’s simplicity, computational efficiency, and cross-disciplinary utility have driven its global adoption in academic and industrial contexts.

Shyamal Ghoshمشاهده پروفایل

استادیار

• Dependence Modelling using Copula
• Stochastic Degradation Modelling
• Statistical Inference
• +۳ مورد دیگر

Dr. Shyamal Ghosh is an Assistant Professor Grade I in Data Science at the Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM). Previously, he served as an Assistant Professor at the Indian Institute of Information Technology Guwahati (2021-2023), a Post-Doctoral Fellow at the University of the Free State, South Africa (2020-2021), and a Visiting Scientist at the Indian Statistical Institute, Kolkata (2019-2020). His educational background includes: M.Sc. in Applied Mathematics from Indian Institute of Engineering Science and Technology, Shibpur (2019), with thesis on "On Certain Probabilistic and Inferential Issues in Nonparametric Ageing Models". B.Sc. (Hons.) in Mathematics from Indian Institute of Engineering Science and Technology, Shibpur (2014). Studies at Vidyasagar University (2009-2012). Dr. Ghosh's research focuses on Dependence Modelling using Copula , Stochastic Degradation Modelling , Statistical Inference , Stochastic Ordering , Shock Model Theory , and Mathematical Theory of Reliability . His work bridges theoretical statistics with applications in reliability engineering, environmental science, and computer systems. He has developed novel statistical tests for ageing classes and methods for stochastic comparisons in heterogeneous systems. His recent publications (2020-2025) span journals in statistics, reliability engineering, and computer science, showing a trend towards interdisciplinary applications. Key areas include nonparametric testing for reliability classes, copula-based dependence modeling, and resource optimization in fog computing. His work often involves moment inequalities, stochastic orders, and lifetime distributions. Dr. Ghosh is actively seeking PhD students with fellowships (e.g., CSIR-JRF, INSPIRE) in Applied Probability and Statistics. He has taught courses such as Statistical Modeling, Survival Analysis, and Mathematical Statistics at IISER TVM and IIIT Guwahati.

Suresh Chavhanمشاهده پروفایل

استادیار

• AI-assisted B5G/6G wireless communication
• Network slicing
• AI/ML/Deep and federated learning
• +۷ مورد دیگر

Suresh Chavhan is an Assistant Professor Grade I in the School of Data Science at the Indian Institute of Science Education and Research (IISER), Thiruvananthapuram. Previously, he served as an Assistant Professor at IIIT Raichur, IIIT Kottayam, and VIT Vellore. His academic journey includes postdoctoral research at the Federal University of Piauí, Brazil, and IISc Bangalore. His educational background includes a Ph.D. from IISc Bangalore, M.Tech. from NIT Surathkal, and B.E. from VTU Belgaum. He holds professional memberships as a Senior Member of IEEE, Member of ACM, and serves on editorial boards including the Alexandria Engineering Journal. Dr. Chavhan's research spans AI-assisted B5G/6G wireless communication, network slicing, AI/ML/deep and federated learning, edge intelligence, V2X, drone communication systems, and intelligent transportation systems. His recent work shows a strong focus on EdgeAI-IoT integration for traffic management, intelligent transportation systems, and next-generation communication networks. His publications demonstrate expertise across multiple high-impact journals including IEEE IoT Journal, IEEE Transactions on Consumer Electronics, IEEE Systems Journal, and Alexandria Engineering Journal. The research shows increasing emphasis on practical implementations of AI in transportation systems, edge computing applications, and security in vehicular networks. 2021 IEEE Systems Journal Best Paper Award 2021 IEEE Madras Section's Publication Award Faculty Research Award from VIT Vellore Honorary Rosalind Membership from London Journals Press International Travel Grant from SERB Cited in UN Sustainable Development Goals for SDG 8 & 11 (2023) Dr. Chavhan leads the Sensing Perception Intelligence & Networking (SPIN) Lab, mentoring multiple PhD students including Ajish, Asir, and Mufeed. He has secured significant research funding including an ANRF grant for three years starting in 2025, ARTPARK-IISc Bangalore, and Bosch projects. His lab actively works on real-time adaptive QoS optimization in 6G-enabled V2X communication, safety frameworks for vulnerable road users using 5G-UWB technology, and real-time location systems using UWB. The SPIN Lab maintains strong industry and international collaborations, with current projects focusing on intelligent transportation systems, connected autonomous vehicles, next-generation communication networks, and edge AI applications for real-time decision-making in networked environments.

Jérémie Gaidamourمشاهده پروفایل

پژوهشگر

• Quantum Mechanics
• Numerical Methods
• High Performance Computing
• +۵ مورد دیگر

Jérémie Gaidamour is a CNRS Research Engineer at the Institut Élie Cartan de Lorraine (University of Lorraine), specializing in high-order numerical methods and high-performance computing (HPC) for quantum mechanics simulations. His work focuses on parallel solvers for large sparse linear systems, including hybrid direct-iterative methods and algebraic multigrid (AMG). PhD Thesis: "Conception d'un solveur linéaire creux parallèle hybride direct-itératif" (2009, Université de Bordeaux I) His research interests span Quantum Mechanics (Bose-Einstein condensates via Gross-Pitaevskii equations), Numerical Methods (pseudo-spectral techniques, domain decomposition), and High-Performance Computing (MPI, parallel algorithms). Recent publications highlight advancements in fractional gradient flows and energy-minimizing AMG preconditioners for nonlinear Schrödinger equations. Software development projects include BEC2HPC (parallel spectral methods for BECs), MueLu (AMG solver in Trilinos), and HIPS (hybrid direct-iterative sparse solver). He has contributed tutorials on HPC tools (MPI, OpenMP, GPU/Xeon Phi) and participated in Grid'5000/IDRIS support teams.

Arnaud Revelمشاهده پروفایل

استاد

• Machine Learning
• Human-Computer Interaction
• Robotics
• +۶ مورد دیگر

Arnaud Revel is a Professor at the University of La Rochelle, holding the position of Deputy Director of the Science and Technology Department since April 2015 and Deputy Director of the S2IM doctoral school (Sciences and Engineering for Information, Mathematics) since June 2012. His academic career spans multiple institutions including ENSEA, where he was initially recruited in 1998, and the University of La Rochelle where he was promoted to professor in 2009. He has served in various administrative roles including co-directing the IMAGIN team at L3I laboratory and as deputy director of the L3I laboratory for AERES evaluation. Revel's research interests focus on three interconnected themes: Learning (structuring complex information and data mining), Human/Machine Interaction (modeling interaction dynamics in robotics and multi-agent systems), and Art and Science (notably the LINK Human/Robot project collaborating with the Cambrai School of Art). His work bridges theoretical computer science with practical applications in robotics, digital art, and interactive systems. Recent publications demonstrate his continued focus on mobile agent security, human-robot interaction, and content-based image retrieval, with notable collaborations across disciplines and international borders. His work on the LINK Human/Robot project, despite not receiving ANR funding, has been presented at prestigious venues including Le Fresnoy (2012), Bains numériques (2012), Futur en Seine (2013), VIA (2014), University of La Rochelle (2014), and Le Cube (2015), culminating in a collaborative art and science book published by Van Dieren in 2015. Professor Revel has supervised multiple doctoral students including K.Prepin (co-supervision, 2008), M.Rojas-Castro (co-supervision since 2012), and H.Idrissi (co-supervision since 2013 in a cotutelle with Morocco). His teaching activities span various institutions including the University of La Rochelle where he teaches Logic Programming, Smartphone programming, and Video Game Programming at the undergraduate level, and Data Mining and Python Programming at the Master's level.

• Theoretical Computer Science
• Distributed Computing
• Descriptive Complexity Theory
• +۳ مورد دیگر

Fabian Reiter is an Associate Professor at Gustave Eiffel University , specializing in Theoretical Computer Science within the DataBases, Automata, algorithm Analysis and Models (BAAM) team at the Gaspard Monge Laboratory of Computer Science (LIGM) . His research bridges logic and distributed computing , focusing on extending descriptive complexity theory to distributed systems. His work establishes equivalences between distributed algorithm classes and logical formula classes, mirroring classical results like Fagin’s theorem . This includes groundbreaking connections between asynchronous automata and least fixpoint logic . Academic appointments : 2019–now : Associate Professor at Gustave Eiffel University 2019-03–2019-07 : Postdoc at Technical University of Munich (funded by ERC project PaVeS ) 2018–2019 : Postdoc at University of Paris-Saclay (funded by ERC project EQualIS ) Education : PhD (2014–2017) under Olivier Carton at IRIF, Paris Diderot University Master’s (2013–2014) under Fabian Kuhn and Andreas Podelski at University of Freiburg

• automata theory
• logic
• games
• +۳ مورد دیگر

Anca Muscholl is a Professor at the University of Bordeaux, affiliated with LaBRI (Laboratoire Bordelais de Recherche en Informatique), a prominent computer science research laboratory in France. Her research focuses on theoretical aspects of computer science, particularly in automata theory, logic, games, verification and synthesis, distributed systems, and foundations of databases. Her research interests span across several key areas in theoretical computer science: Automata theory, with particular focus on string transducers and weighted automata Formal verification and synthesis of distributed systems Logic in computer science, including two-variable logic on data words Communication models and message passing systems Web services composition and negotiation protocols Professor Muscholl has been actively involved in the theoretical computer science community through numerous editorial and leadership roles. She currently serves on the editorial boards of TheoretiCS, Logical Methods in Computer Science, and LIPIcs. She is also a member of the Council of the European Association for Theoretical Computer Science (EATCS) and the Comité national de la recherche scientifique (section 06). Her recent publications demonstrate continued productivity and influence in her field, with significant contributions to string transducers, distributed systems verification, and automata theory. Her work shows a consistent focus on theoretical foundations with practical applications in verification and distributed computing. Her professional service includes significant leadership roles such as: Steering committee chair of the EATCS International Colloquium on Automata, Languages and Programming (2018-2024) Editorial board member of Discrete Mathematics & Theoretical Computer Science (2001-2023) Editorial board member of Information Processing Letters (2006-2015) Steering committee member of the Symposium on Theoretical Aspects of Computer Science (STACS) (2008-2014) Executive committee member of GDR Informatique-Mathématique (2017-2023) Professor Muscholl has made substantial contributions to the theoretical computer science community through her research, service, and mentorship. Her work bridges theoretical foundations with practical applications in verification and distributed systems, contributing to the advancement of formal methods and their applications.

Emmanuel Adamمشاهده پروفایل

دانشیار

• Multi-Agent Systems
• Holonic Architectures
• Self-Adaptation in Complex Systems
• +۷ مورد دیگر

Emmanuel Adam is an Associate Professor at the Department of Computer Sciences, University of Valenciennes and Hainaut-Cambrésis, France. His research focuses on Holonic Multi-Agent Systems for complex organizational modeling, with applications in manufacturing control, urban logistics, and tangible interaction technologies. Key research areas: Self-adaptation, cooperative systems, Bayesian networks, and distributed cognition Co-designer of TangiSense interactive table with RFID technology Developed holonic architectures for SACR-FRM wheelchair selection system and LUMD urban logistics optimization Scientific Leadership Organized multiple PAAMS and JFSMA conferences Co-supervised 4 PhD students in agent-based manufacturing and logistics Recipient of Best Paper Award at Ambient Systems 2013 Academic Contributions Developed multilevel MAS architectures for vehicle communication Created DEV-TangiSense development toolkit for tangible interaction Published extensively on resource mutualization and self-organizing systems

• Semantic Web
• Web Services Composition
• Formal Concept Analysis
• +۶ مورد دیگر

Nizar Messai is a Researcher at the University of Tours , affiliated with the Faculty of Science and Technology and Polytechnic School of the University of Tours (EPU) . He works within the Laboratory of Fundamental and Applied Computer Science (LIFAT, UR 6300) , focusing on semantic technologies and distributed systems. Research Interests : Semantic Web Services Composition Formal Concept Analysis (FCA) for Data Mining Blockchain Applications in Trustworthy Systems Context-Aware IoT Mashups Linked Open Data Integration Scientific Contributions : Developed semantic similarity measures for service discovery Created the LDS Java library for LOD-based similarity Explored blockchain for decentralized choreography execution Integrated attention mechanisms in travel mashup systems Applied FCA to clinical decision support systems Collaborations & Labs : Active member of the LIFAT (UR 6300) laboratory, contributing to projects at the intersection of computer science, semantic technologies, and biomedical informatics.