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Maya RamanathView profile

Associate Professor

• Database Systems
• Information Retrieval
• Semantic Web Data Management
• +3 more

Maya Ramanath is an Associate Professor in the Department of Computer Science and Engineering at Indian Institute of Technology (IIT) Delhi. She joined IIT Delhi in 2011 after a postdoctoral research stint at the Max-Planck Institute for Informatics in Germany. Her research interests focus on database systems, information retrieval, semantic web technologies, and knowledge graph construction and applications. Education: PhD in Computer Science, Indian Institute of Science, Bangalore M.Sc.(Engg.) in Computer Science, Indian Institute of Science, Bangalore B.E. in Computer Science and Engineering, Bangalore University, Bangalore Her recent work emphasizes efficient query processing over large-scale graphs, knowledge graph applications, and natural language interfaces for semantic data. Notable contributions include algorithms for reachability approximation in web-scale graphs, speculative query planning for knowledge graphs, and exploratory querying techniques. She has collaborated extensively on projects like NAGA, ESTHETE, and KlusTree, advancing the state of the art in graph-based data management and semantic search. Publications span conferences such as ICDE, ECIR, EDBT, and VLDB, reflecting a strong focus on database systems, graph algorithms, and semantic web applications. Her work bridges theoretical foundations with practical implementations, addressing scalability and efficiency challenges in modern data management systems. Research and advising activities include supervision of projects on distributed graph processing, query optimization, and knowledge representation. She has contributed to open-source tools like LegoDB and StatiX, and her lab focuses on interdisciplinary approaches to data-centric AI.

Edouard OyallonView profile

Researcher

• Machine Learning
• Deep Learning
• Distributed Systems
• +5 more

Edouard Oyallon is a CNRS Researcher at Sorbonne University's MLIA team within the Institute of Intelligent Systems and Robotics (ISIR). His research focuses on machine learning foundations, particularly the symmetries of deep neural networks, and large-scale distributed/decentralized training algorithms. He has contributed to frameworks like Kymatio for wavelet scattering transforms and collaborates on projects such as SHARP (Frugal Learning) and ADONIS (ANR-funded). He advises multiple PhD and postdoctoral researchers and teaches advanced deep learning courses at Institut Polytechnique de Paris (IPP). Grants include the ADONIS project (ANR/Sorbonne) and participation in VHS and CoCa4AI initiatives. His work spans theoretical and applied aspects, with recent emphasis on optimizing LLM training at exascale. He maintains active roles in academic service, including organizing workshops on federated learning and graph machine learning.

Laurent DaudetView profile

Professor

• Machine Learning
• Optics
• Signal Processing
• +3 more

Laurent Daudet is a Professor of Physics at Université Paris Cité (on leave) and CTO & co-founder of LightOn, a startup developing optical computing technologies. His research spans signal processing, wave physics, and machine learning, with a focus on scalable AI solutions. He holds a PhD in Applied Mathematics from Marseille University and is a graduate of École Normale Supérieure in Paris. Research Interests: Laurent’s work bridges academia and industry, addressing challenges in massive-scale AI, optical computing, and hardware optimization. He leads cross-disciplinary R&D projects at LightOn, advancing technologies like the Optical Processing Unit (OPU) for low-power, parallel computing. Awards: Fellow of the Institut Universitaire de France Grants & Advising: Over 200 scientific publications and patents; collaborates globally with researchers and engineers. Former academic roles include Visiting Senior Lecturer at Queen Mary University of London and Visiting Professor at the National Institute for Informatics (Tokyo). Labs & Teams: Leads LightOn’s R&D initiatives, integrating optics, ML, electronics, and software engineering to tackle AI scalability challenges.

• Mesh generation
• Numerical simulation
• Finite element methods
• +5 more

Houman BOROUCHAKI is a Professor at the University of Technology of Troyes (UTT), France, with over 20 years of academic leadership. He has served as Head of the Automatic Mesh Generation and Advanced Methods (GAMMA3) project team since 2008 and previously led the Laboratory of Mechanical Systems and Concurrent Engineering (LASMIS) (2005-2007). His work bridges academic research and industrial applications through collaborations with INRIA , French Petroleum Institute (IFPEN) , Dassault Aviation , and others. Research Interests: A pioneer in adaptive meshing , he focuses on finite element methods , geometric modeling , and numerical simulations . His innovations underpin mesh generation algorithms , 3D triangulation software , and industrial applications in metal forming, composite simulation, and subterranean modeling. Scientific Trends: His recent work emphasizes metric-based meshing , high-order geometric validity , and parallel processing for mesh generation , with applications in petroleum reservoirs, aviation surfaces, and nanomaterials. His Google Scholar profile reflects 25+ years of contributions to meshing and simulation. Teaching: With 22 years of experience, he teaches courses on meshing , numerical analysis , geometric modeling , and computer graphics at UTT, covering undergraduate to PhD levels. Labs & Teams: He leads the interdisciplinary GAMMA3 team and has contributed to LASMIS (mechanical engineering), L2n (CNRS-UMR 7076) (nanomaterials), and LIST3N (computer science).

Aurèle MaheoView profile

Research Fellow

• Distributed Systems
• Parallel Computing
• Machine Learning Systems
• +3 more

Aurèle Maheo is a Research Fellow at the Computer Science Department of Telecom SudParis, affiliated with the Parallel and Distributed Systems (PDS) group. His research focuses on distributed systems, parallel computing, and hardware acceleration, with applications in machine learning and high-performance computing. He has contributed to projects such as SpeedyLoader for optimizing machine learning pipelines and FPGA memory bandwidth improvements. His work bridges theoretical distributed systems principles with practical implementation challenges in large-scale environments. Affiliations: PDS Group, Computer Science Department, Telecom SudParis Research interests include distributed consensus algorithms, system scalability, and hardware-software co-design for accelerating computational workflows. Recent work highlights include presentations on machine learning pipelining techniques and FPGA-based memory optimization strategies.

Petros EliaView profile

Professor

• Information Theory
• Coding Theory
• Caching
• +3 more

Professor Petros Elia is a faculty member at EURECOM, holding the position of Professor within the Department of Communications systems. He specializes in Information Theory , Coding Theory , Caching , Distributed Computing , and Wireless Networks , with additional research in Biometrics . His work focuses on advancing theoretical foundations and practical applications in distributed systems and wireless communication efficiency. He received a prestigious ERC Consolidator Grant for his DUALITY project (2016) and a four-year Fulbright Scholarship (1993-1997). He is also a recipient of the Newcom++ Network of Excellence Distinguished Achievement Award (2008-2011) and the Best Student Paper Award at SPAWC 2011, awarded to his advisee Arun Singh. His research explores cutting-edge topics such as tessellated distributed computing , hypergraph decomposition , and topology-aware caching , with recent contributions presented at venues like the IEEE International Symposium on Information Theory (ISIT 2025). His work bridges theoretical advancements with real-world applications in wireless networks and distributed systems. Education: Supported by his Fulbright Scholarship, he pursued studies in the U.S. during 1993-1997. Grants: ERC Consolidator Grant (2016), and others. Advising: Mentor to Arun Singh , whose work earned a student paper award. He actively contributes to teaching Mobile Communications at EURECOM and remains a key figure in advancing the field through interdisciplinary collaborations and leadership in the Communication Systems department.

• Virtualization
• Operating Systems
• Concurrency
• +4 more

Gaël Thomas is a Senior Researcher at Inria Saclay and a part-time Professor at École Polytechnique. He leads the Benagil team, a joint initiative between Inria and Telecom SudParis/IP Paris. His research focuses on virtualization, operating systems, concurrency, and runtime systems, with an emphasis on improving system performance and safety. He holds a PhD and Habilitation from Sorbonne Université and has extensive academic experience, including roles as a Professor at Telecom SudParis (2014–2023) and an Associate Professor at UPMC (2006–2014). Education: PhD in Computer Science, 2005, UPMC Sorbonne Université Habilitation à Diriger les Recherches (HDR), 2012, UPMC Sorbonne Université MSc in Computer Science, 2001, MIAIF Program Research Interests: Gaël’s work spans virtualization techniques, NUMA architecture optimization, garbage collection scalability, lock-free algorithms, and persistent memory systems. He has pioneered solutions like J-NVM for Java NVMM and NumaGiC for big data systems. Grants & Awards: Principal Investigator of DiVA (PEPR Cloud, 868k€) and Maplurinum (ANR PRC, 184k€) Contributions to VMKit and infra-web-cours development Labs/Teams: Benagil Team (Inria/Telecom SudParis) focuses on systems and runtime optimization.

Evripidis BAMPISView profile

Professor

• Approximation Algorithms
• Energy-Aware Algorithms
• Algorithmic Game Theory
• +5 more

Evripidis Bampis is a Professor of Computer Science at Sorbonne Université, affiliated with the LIP6 laboratory and the Operations Research (RO) group. He currently serves as the Head of the Master of Computer Science program and leads the AGAPE research group within the GDR Recherche Opérationnelle. His research focuses on approximation algorithms, energy-aware computing, algorithmic game theory, scheduling, and graph problems. He has been actively involved in organizing international conferences and workshops, including co-chairing the International Symposium on Fundamentals of Computation Theory (FCT) in 2021 and 2023, and chairing the Symposium on Experimental Algorithms (SEA) in 2015. His collaborative projects include ANR-funded initiatives such as ALGORIDAM (2020–2024) and ENERGUMEN (2018–2023), which address algorithmic theory and energy efficiency in distributed systems. Bampis has advised numerous PhD students and co-authored influential publications in scheduling and algorithm design. His international collaborations span institutions like the Technical University of Munich, the Chinese Academy of Sciences, and Imperial College London. He maintains a strong presence in academic committees and has contributed to EU-funded thematic networks like APPOL and APPOL II.

Xujia ZHUView profile

Associate Professor

• Uncertainty Quantification
• Surrogate Modeling
• Stochastic Simulators
• +4 more

Xujia ZHU is an Associate Professor at CentraleSupélec, Paris-Saclay University, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on uncertainty quantification, surrogate modeling, stochastic simulators, and reliability analysis. He holds an engineer’s degree in Mechanics from École Polytechnique (2015), a Master’s in Computational Mechanics from TU Munich (2017), and a Ph.D. from ETH Zurich (2022). He was a postdoctoral researcher at ETH Zurich until 2023. Education: Ph.D., Chair of Risk, Safety, and Uncertainty Quantification, ETH Zurich, 2022 Master’s (high distinction), Computational Mechanics, Technical University of Munich, 2017 Engineer’s Degree, Mechanics, École Polytechnique, 2015 Research Interests: Xujia’s work bridges numerical simulations and statistics, addressing topics like uncertainty propagation, sensitivity analysis, and surrogate modeling for stochastic systems. Key areas include polynomial chaos expansions, Bayesian active learning, and applications in seismic fragility analysis. Publications: His recent work emphasizes emulation techniques for stochastic simulators, multi-fidelity methodologies, and Bayesian active learning strategies in reliability analysis. Key themes include sparse polynomial chaos expansions and latent variable modeling. Labs/Teams: Affiliated with L2S, he collaborates on transversal projects in energy, industry, and health, leveraging interdisciplinary approaches in uncertainty quantification and computational modeling.

Anne-Cécile OrgerieView profile

Researcher

• Energy Efficiency
• Environmental Impacts
• Distributed Systems
• +3 more

Anne-Cécile Orgerie is a permanent CNRS Research Scientist (Directrice de recherche) at the Magellan Team within IRISA in Rennes, France. She holds a PhD from École Normale Supérieure de Lyon (2011) and previously worked as a postdoctoral researcher at the University of Melbourne. Current roles: Director of GDRS EcoInfo (CNRS service group on ICT environmental impact), member of multiple editorial boards (e.g., IEEE TPDS, IJDSN), and TPC member for conferences like SC, IPDPS, and CCGrid. Research Focus: Her work centers on energy efficiency and environmental impacts of distributed systems, including cloud infrastructures, telecommunications networks, and smart grids. She explores renewable energy integration, resource optimization, and co-simulation frameworks for smart grid management. Key Projects: Leads initiatives like CARECloud (PEPR Cloud project reducing cloud environmental impacts, 2023–2030) and DECORUS (CNRS 80Prime project on renewable-powered edge computing). Co-leads the RennesGrid ADEME project (2017–2021) for smart grid demonstrators. Supervision: Advises/co-advises over 20 PhD students and postdocs, focusing on topics like energy-efficient fog infrastructures, co-optimization of electrical/communication networks, and edge computing models.

Laurent CaraffaView profile

Researcher

• 3D Data Processing
• Surface Reconstruction
• Point Cloud Processing
• +7 more

Laurent Caraffa is a Researcher at Université Gustave Eiffel, working at the LaSTIG laboratory of IGN (National Institute of Geographic and Forest Information). His research focuses on large-scale 3D data processing, including surface reconstruction from point clouds and images, leveraging triangulated structures and implicit methods. His work also covers indexing and searching within point clouds for large-scale place recognition, with applications in urban environments and navigation systems. Caraffa's research interests span 3D Data Processing, Surface Reconstruction, Point Cloud Processing, Large-scale Place Recognition, Indexing and Retrieval, Big Data, Cloud Computing, Mathematical Optimization, 3D Mapping, and Photogrammetry in degraded conditions. His work bridges theoretical computational geometry with practical applications in geographic information systems and autonomous navigation. His publication record demonstrates significant contributions to distributed 3D processing, particularly through advancements in Delaunay triangulation, watertight surface reconstruction, and neural radiance fields. Recent work shows a clear trajectory toward more efficient and scalable methods for processing massive 3D datasets, with growing emphasis on implicit representations and learning-based approaches for 3D reconstruction. Caraffa actively participates in the scientific community through organizing events like the Big Data Day 2023 at IGN and contributing to major research projects. His work has resulted in publications in top-tier conferences including ICLR, CVPR, ISPRS, and IEEE Big Data, establishing him as a significant contributor to the field of large-scale 3D data processing. As a research supervisor, Caraffa currently co-supervises four PhD students working on projects funded by AID, Criteo, and Huawei, focusing on large-scale place recognition, implicit representations for 3D reconstruction, and 3D reconstruction in degraded conditions. He is also the co-founder of ExtraLabs, a company developing distributed computing solutions for cooperative digital twins, demonstrating the practical impact of his research.

Yelva RoustanView profile

Lecturer

• Air Quality Modeling
• Atmospheric Chemistry
• Pollutant Deposition
• +3 more

Yelva Roustan is a Lecturer at CEREA (joint laboratory between École Nationale des Ponts et Chaussées and EDF R&D) since 2005, specializing in air quality modeling . Her research focuses on multiscale modeling approaches, source-concentration relationships, and pollutant deposition processes. She teaches at the National School of Bridges and Roads, sharing knowledge from her research in atmospheric transport and environmental science. Her work addresses urban pollution challenges, including the impact of low-emission vehicles on air quality, urban tree effects, and pollutant dynamics in street networks. She has developed the MUNICH street-network model and contributed to projects like EURODELTA for multi-pollutant analysis across Europe. Her methods integrate data assimilation, inverse modeling, and Bayesian techniques to improve emission source reconstruction and model accuracy. Key contributions include studies on radioactive release scenarios (e.g., Fukushima, 106Ru event) and urban infrastructure impacts on pollution. Her research bridges atmospheric science with engineering solutions for sustainable urban environments. She holds a PhD in Environmental Engineering from École Nationale des Ponts et Chaussées (2005) and an HDR in Numerical Modeling of Atmospheric Pollutants (2020).

Amin MesmoudiView profile

Associate Professor

• Data Engineering
• Machine Learning
• Anomaly Detection
• +5 more

Amin Mesmoudi serves as Associate Professor in Data Engineering at the University of Poitiers' IUT (Institut Universitaire de Technologie), with dual laboratory affiliations at LIAS-ENSIP (Poitiers campus) and LIAS-ISAE-ENSMA (Chasseneuil campus). His research bridges theoretical database systems with practical large-scale data engineering challenges, particularly in semantic web technologies and machine learning applications. The laboratory maintains physical presences at both ENSIP's Bâtiment B25 in Poitiers and ISAE-ENSMA's Téléport 2 facility in Chasseneuil, facilitating cross-institutional collaboration. Mesmoudi's research program centers on scalable data management systems, with three interconnected pillars: (1) RDF and graph-based query optimization techniques for billion-triple datasets, (2) machine learning integration for spatial query performance and anomaly detection, and (3) explainability frameworks for complex black-box models. His work demonstrates consistent evolution from foundational database systems (2011-2016) toward contemporary AI-driven data engineering, particularly evident in his 2023-2025 publications on temporal dependency preservation and co-selection explainability. The Data Engineering team within LIAS laboratory provides the primary research context for these investigations. Publication analysis reveals strong methodological continuity in addressing scalability bottlenecks across database paradigms. Early work focused on SQL-on-MapReduce benchmarking for astronomy databases (2015-2016), transitioning to specialized RDF processing frameworks (2019-2021), and culminating in current hybrid approaches combining temporal modeling with machine learning (2023-2025). Key technical themes include fragmentation strategies for distributed data, optimizer feedback mechanisms, and graph-based query acceleration - all targeting real-world performance constraints in big data environments. As a core member of LIAS laboratory's Data Engineering team, Mesmoudi contributes to France's national research infrastructure in computer science and automation systems. The laboratory's dual-university structure enables unique cross-pollination between University of Poitiers' academic programs and ISAE-ENSMA's engineering specialization, with Mesmoudi's work exemplifying this synergy through applications spanning astronomy databases to wireless sensor networks.

Inbar FijalkowView profile

Professor

• Signal Processing
• Wireless Communications
• Optimization
• +3 more

Inbar Fijalkow is a Full Professor at the National School of Electronics and Computer Science (ENSEA) within CY Cergy Paris University. She is a member of the ETIS Research Unit (UMR 8051), focusing on signal processing for wireless communications, optimization, and machine learning applications. Her research bridges theoretical advancements with practical implementation in emerging communication systems. Education & Career: PhD in Signal Processing from TelecomParisTech (1993) Postdoctoral Fellow at Cornell University (1994–1995) Professor at ENSEA since 1999 Former Head of ETIS Research Unit (2004–2013) Research Interests: Signal processing for wireless communications Optimization techniques in massive MIMO and NOMA systems Machine learning applications in communication systems Nonlinear effects mitigation in high-power amplifiers Community & Awards: Member of CoNRS Section 7 (National Committee for Scientific Research) Chevalier de l’Ordre National du Mérite (2015) Founder of the CY Alliance Women in Science Prize (2017) Recent Projects: Active in ANR-funded initiatives (e.g., EcoBioH2, AI4code) and EU projects (e.g., PERSEUS). Her work emphasizes sustainability and AI-driven communication systems. Teaching: Teaches signal processing and wireless communications at ENSEA. Supervises PhD students and master’s theses in communication systems and signal processing.

• Turbomachinery Aerodynamics
• Aeronautical Propulsion
• Unsteady Flow Analysis
• +3 more

Nicolas Binder is a Professor and Head of the Turbomachinery and Propulsion Research Group at ISAE-SUPAERO . His research focuses on turbomachinery aerodynamics, unsteady flow analysis, and innovative propulsion systems for aerospace applications. Member of EuroTurbo executive committee ASME Member Associate Editor, Journal of Turbomachinery Research expertise in off-design operations and windmilling flows Research Interests : Aerodynamics of turbomachinery in severe off-design conditions Unsteady flow dynamics in turbines Innovative propulsion methods including magneto-hydrodynamics Flow analysis techniques for compressors and fans Recent publications (2024-2021) emphasize transient flow modeling in turbines, windmilling operation optimization, and variable geometry turbine performance. Articles span experimental validation of numerical models, shock wave interactions, and novel propulsion concepts like plasma-thrusters for drones.