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Professor
Corinne Lucet is a full-time Professor at the University of Picardie Jules Verne (UPJV), affiliated with the UR 4290 research unit. Her work focuses on Combinatorial Optimization , Artificial Intelligence , and Operations Research applications in healthcare and logistics.
Lecturer
Leo ROBERT serves as a Lecturer in the Networks and Data department at the University of Picardie Jules Verne (UPJV), affiliated with research unit UR 4290 (Domain - REDO). His office is located in room 313, and he is actively engaged in academic instruction within the university's technical domain. His research concentrates on Computer Networks and Data Science , with specialized focus on network architecture, data transmission protocols, and large-scale data system optimization. Current investigations include real-time network monitoring frameworks and distributed data processing methodologies within the REDO research domain. No scientific awards, student supervision records, or grant funding details are documented in available sources.
Research Professor
Olivier Cappé is a CNRS research director at the Department of Computer Science of École Normale Supérieure (ENS), which is part of PSL University. He also serves as associate professor at PSL University and director of the IASD (Artificial Intelligence, Systems, Data) Master's program, a collaborative initiative between University Paris Dauphine, ENS, and Mines Paris. His academic career spans over 25 years with significant contributions across machine learning, statistics, and signal processing. Dr. Cappé's research trajectory has evolved from foundational work in speech and audio processing during the 1990s, through Bayesian methods and Markov Chain Monte Carlo in the 2000s, to his current focus on online learning and multi-armed bandit models. His theoretical contributions to reinforcement learning, particularly regarding exploration-exploitation tradeoffs and non-stationary environments, have established him as a leading figure in the field. His work bridges rigorous statistical theory with practical applications in resource allocation, bidding strategies, and influence maximization. His recent publications demonstrate sustained innovation in machine learning theory with practical relevance. Key research themes include bandit algorithms that adapt to changing environments, theoretical foundations of online optimization, and privacy-preserving machine learning techniques. His work consistently appears in top-tier venues including NeurIPS, ICML, and JMLR, reflecting both theoretical depth and practical impact. French Academy of Sciences 2013 Grand prix de la fondation d'entreprise EADS IEEE Signal Processing Society 2005 Signal Processing Magazine Best Paper Award (with E. Moulines, J-C. Pesquet, A. Petropulu and Z. Yang) IEEE SP Society 1995 Young Author Best Paper Award Dr. Cappé has mentored over 20 PhD students and postdoctoral researchers throughout his career, with his advisees securing prestigious positions at institutions including University de Lille, Imperial College London, Twitter, and major research laboratories. His research has been consistently funded through competitive grants including multiple ANR projects (ALICIA, SIMINOLE, MGA, KERNSIG), demonstrating sustained recognition of his research program's significance and quality. He is affiliated with the Centre Sciences des Données (CSD) at ENS and has held significant leadership roles including Deputy Scientific Director of the INS2I Institute of CNRS (2017-2023), director of LTCI laboratory (2013-2016), and head of the STIC department at University of Paris-Saclay (2016-2017). He co-authored the forthcoming illustrated book 'Tout comprendre (ou presque) sur l'intelligence artificielle' published by CNRS éditions in April 2025.
Researcher
Umut Simsekli is a Researcher at INRIA - SIERRA team and École Normale Supérieure de Paris, Computer Science Department. He earned his PhD (2015) and MSc (2010) from Boğaziçi University, and a BSc from Sabancı University (2008). His career includes roles at Télécom Paris (2016-2020) and visiting positions at Oxford and Boğaziçi University. Research Focus: Mathematical Machine Learning, emphasizing Deep Learning theory, Heavy-Tailed Optimization, Bayesian Inference, and Stochastic Dynamics. Grants: Principal Investigator for ERC Starting Grant DYNASTY (€1.5M, 2022-2027) and co-PI for ANR/TUBITAK grant FBIMATRIX (2016-2022). Recent Article Trends: Explores PAC-Bayesian generalization, heavy-tailed SGD dynamics, and topological stability in optimization algorithms. Scientific Awards: ERC Starting Grant (2021) ICASSP Best Student Paper Award (2020) Multiple IEEE/SPS Student Travel Grants (2012-2020) Victor L. Wooten Bass/Nature Camp Scholarship (2005) Teaching & Advising: Teaches Deep Learning at École Polytechnique and previously led courses on Probabilistic Graphical Models at Télécom Paris. Supervises PhD students Dario Shariatian, Benjamin Dupuis, and others.
Lecturer
Hassan Maatouk is a Lecturer at the University of Perpignan, affiliated with the UFR SEE (Science, Economics, and Engineering) faculty, specifically within the MATH-INFO Department. He is a member of the LAMPS (Multidisciplinary Modeling and Simulation Laboratory) where he conducts research in applied mathematics and statistics. His primary research interests include: Data Science and Statistical Learning Nonparametric and Bayesian Statistics High-dimensional Statistical Modeling Computational Statistics and Gaussian Processes MCMC Methods and Uncertainty Quantification Dr. Maatouk's research focuses on non-parametric statistics and high-dimensional modeling with structured constraints such as monotonicity, bounds, and convexity. His work aims to improve prediction models based on Gaussian processes and quantify uncertainties in simulations, with applications spanning econometrics, microbiology, chemistry, and industrial contexts. His recent publications demonstrate a strong emphasis on constrained Gaussian processes, truncated multivariate normal distributions, and scalable Bayesian methods for large datasets, with increasing citation impact (65 citations in 2025 alone). His scholarly impact is evidenced by 362 total citations and an h-index of 8. His most influential works include 'Gaussian process emulators for computer experiments with inequality constraints' (123 citations) and 'Kriging of financial term-structures' (70 citations). Dr. Maatouk collaborates with researchers across France including Xavier Bay from École des Mines de Saint-Étienne, Areski Cousin from the University of Strasbourg, and Yann Richet from IRSN. His interdisciplinary approach extends to materials science as shown by his co-authored work on ZnO nanoparticles' antibacterial properties.
Associate Professor
Aurélien Bénel is an HDR Lecturer (Associate Professor) in Computer Science at the University of Technology of Troyes, affiliated with the LIST3N laboratory's 'Technologies and Practices' research axis. He teaches across multiple academic levels, including undergraduate courses in Software and Information Systems Engineering (e.g., Information System Analysis), graduate courses in software innovation (e.g., Service-Oriented Architectures, Agile Methods), and doctoral seminars on Socio-Technical Systems and research methodologies. His research focuses on software technologies for intellectual work instrumentation , with experimental applications in archaeology, sociology, translation, and engineering. He develops and maintains open-source tools through the Hypertopic suite, including participatory platforms (Porphyry, LaSuli, TraduXio) and infrastructure components for image corpus management and semantic categorization. His interdisciplinary work intersects with digital humanities, knowledge engineering, and human-computer interaction. Bénel actively contributes to scientific communities as a reviewer in Digital Humanities, Digital Document studies, Knowledge Engineering, Artificial Intelligence, and Computer-Supported Cooperative Work. His publications emphasize participatory systems, semantic technologies, and the sociotechnical aspects of digital tools.
Associate Professor
Achim Edelmann is an Associate Professor of Sociology at Sciences Po's médialab, specializing in the sociology of culture, social networks, and computational methods. He habilitated at the University of Bern, was a visiting scholar at UC Berkeley, and completed postdoctoral training at Duke University's Network Analysis Center. His research examines the interplay between cultural meanings and social networks, the co-evolution of scientific ideas and public debates, and the application of big data to sociological questions. Education: Habilitation in Sociology (University of Bern), Postdoctoral Trainee (Duke Network Analysis Center) Affiliations: Sciences Po (médialab), University of Bern, UC Berkeley, Duke University Key research projects include analyzing the interaction between cultural meanings and social networks, studying scientists' public support of risky science, semantic analysis in interviews, and misinformation spread through private messaging. He increasingly employs relational databases and cloud computing for large-scale data analysis. His Google Scholar publications (2014–2023) demonstrate expertise in computational social science, symbolic boundary theory, military-to-civilian transitions, and climate science networks. Notable works include analyzing gender differences in political education outcomes (2023), uncertainty in research methodology (2022), and formalizing cultural boundaries (2018). Methodologically, Edelmann combines traditional sociological theory with computational approaches, focusing on structural analysis of cultural systems, network ecology, and big data applications in social research. He supervises PhD theses and has contributed to MOOC-Ed network datasets.
Associate Professor
James R. Eagan is an Associate Professor at Télécom Paris , part of Institut Polytechnique de Paris , and holds a Visiting Professor position at the University of Colorado, Boulder in the College of Media, Communication & Information. He leads the DIVA Lab (Data, Intelligence, Visualization, and Applications) within the Computer Science & Networking Department. Currently on sabbatical (2024–25 academic year) at CU Boulder, his research bridges Human-Computer Interaction, Explainable AI (XAI), and computational media design.
Associate Professor
Gaël Obein is a Senior Lecturer (HDR) and Associate Professor at Conservatoire National des Arts et Métiers (CNAM) in Paris, France. He serves as Director of LNE-CNAM, the national metrology laboratory for radiometry, photometry, temperature, and length, and leads the 'Radiometrics/Photonics' team. His academic career spans roles at CNAM, Paris VI, NIST (USA), and MNHN, with a PhD in 'Physical Systems and Metrology' (2003) and HDR since 2019. President of French Lighting Association (AFE) Secretary of CIE Division 2 Coordinator of four European research projects (2013–2027) Expert in BRDF, BSSRDF, and BTDF metrology His research focuses on optical metrology for material appearance, including: High-angular-resolution goniospectrophotometry (0.015°) µBRDF measurements for 50µm surfaces Specular gloss perception studies Development of primary standards for BRDF/BSSRDF/BTDF Light polarization and speckle effects Scientific Leadership includes: Secretary of CIE Division 2 Director of CIE TC2-85 and JTC17 Coordination of EURAMET-PR-K6 key comparisons Participation in 4 CIE technical committees Publications (2023–2024) cover bidirectional reflectance distribution functions, gloss metrology, spectral irradiance traceability, and material appearance modeling. Projects include 'xDReflect', 'BiRD', 'BxDiff', and 'xDDiff', addressing multidimensional optical diffusion and appearance measurement standards.
Professor
Marc Sebban is a Professor in Computer Science at the Hubert Curien Laboratory (LabHC) and Deputy Director of this research unit. He leads the Inria project-team MALICE, focusing on machine learning, domain adaptation, and metric learning. Research Interests Metric learning with theoretical guarantees Domain adaptation via optimal transport Physics-informed neural networks Imbalanced data classification Tree-structured data similarity learning Recent Publications His 2025 work introduces provably accurate adaptive sampling for collocation points in PINNs and theoretically grounded quadrature methods using residual Hessians. 2024 publications explore physics-informed ML for laser-matter interaction, predictive modeling of body shape changes, and approximation error analysis in tanh neural networks. Earlier works address graph diffusion Wasserstein distances, metric learning for imbalanced data, and boosting algorithms with confidence oracles.
Assistant Professor
Valerio Incerti is an Assistant Professor at SKEMA Business School in France, specializing in Management & Organization research. His work focuses on virtual teams , knowledge management , and organizational behavior , with a particular emphasis on the effects of Multiple Team Membership (MTM) and communication rules on teamwork dynamics. 2017 PhD in Industrial Innovation Engineering from University of Modena and Reggio Emilia 2013 Master in Engineering Management (summa cum laude) from University of Modena and Reggio Emilia Research interests span organizational polychronicity , social identity , and team performance , with publications in journals like M@n@gement and Journal of Enterprising Communities , as well as conferences such as the Academy of Management Annual Meeting and INGRoup Conference . His methodological approach includes mixed-method studies, simulation modeling, and experimental research. Key article trends show interdisciplinary work bridging management theory with operations research and social identity perspectives , particularly in areas like LGBT entrepreneurship and environmental sustainability .
Lecturer
Christophe Schuwey is a Lecturer in digital humanities and book history at Université Bretagne Sud, affiliated with the Heritage and Creation in Text and Image research group (HCTI). His interdisciplinary work bridges traditional literary scholarship with digital methods, reflecting his dual background in French literature and computer science. His research focuses on three interconnected areas: digital humanities (theory and practice, interfaces, digital publishing), the history of the book, publishing and the book market in the 17th century, and information, media and controls during the same period. Schuwey's scholarship demonstrates how early modern media practices anticipated contemporary digital culture, particularly in how information was curated, distributed, and consumed. Schuwey's recent publications reveal a consistent focus on media history, particularly examining the Mercure galant, Molière, Donneau de Visé, and La Bruyère through both traditional literary analysis and digital humanities approaches. His work often explores how 17th century media practices relate to contemporary digital culture, with particular attention to information dissemination, marketing strategies, and media manipulation. Currently directing the "Caranum" project (2024-2027) for the first digital edition of La Bruyère's Caractères, Schuwey is actively shaping how early modern texts are presented and studied in the digital age. His publications span books, peer-reviewed articles, digital projects, and multimedia content, demonstrating his commitment to diverse scholarly communication formats.
Professor
Bertrand Ducourthial is a Professor at the University of Technology of Compiègne (UTC), teaching in the Department of Computer Engineering and conducting research at the Heudiasyc laboratory (UMR CNRS-UTC 6599). His work focuses on distributed systems, networks, and complex systems engineering. His institutional affiliations include: Primary appointment: Department of Computer Engineering, University of Technology of Compiègne Research affiliation: Heudiasyc Laboratory (UMR CNRS-UTC 6599), a joint CNRS-UTC research unit Professor Ducourthial's research examines distributed algorithms and complex system behaviors, particularly in transportation contexts. He maintains active teaching responsibilities in computer engineering while leading research initiatives at Heudiasyc. His contact email is Bertrand.Ducourthial@utc.fr for academic inquiries.
Catherine Labruère Chazal serves as a Lecturer at the Institute of Mathematics of Burgundy (IMB), UMR CNRS 5584, within the University of Burgundy's Faculty of Science and Technology. She is an active member of the Statistics, Probability, Optimization and Control (SPOC) research team and holds significant administrative responsibilities including President of the L1 jury, Chair of Parcoursup and Study in France application review committees, and Head of Mathematics for L1 AGIL programs. Her research demonstrates exceptional interdisciplinary breadth, anchored in statistical methodology development. Primary expertise includes topological data analysis, functional principal components analysis, and biostatistical modeling, with applications spanning archaeology (computer-assisted pottery reconstruction), microbiology (Candida albicans pathogenesis), evolutionary biology (tooth morphology and echinoderm architecture), perinatal health (birth-weight curve modeling), and social sciences (ageism in fashion media and adolescent sports psychology). This cross-domain approach highlights the versatility of statistical frameworks in solving complex real-world problems. Publication trends since 2007 reveal consistent methodological innovation in statistical theory, particularly in handling high-dimensional and topological data structures. Her work increasingly bridges theoretical advances with practical applications in health sciences and cultural studies, evidenced by collaborations with medical researchers on perinatal networks and microbiologists studying fungal pathogenesis. Recent publications indicate growing engagement with social science questions, notably age representation in media. Within the IMB structure, she contributes to the SPOC team's mission of advancing research in stochastic processes, optimization theory, and statistical learning. Her teaching portfolio directly supports this research ecosystem through courses in machine learning (Python), algorithms (C++), and statistical methodology for doctoral students, fostering the next generation of quantitative researchers.
Associate Professor
Patrick Tardivel is an Associate Professor at the University of Burgundy, specializing in statistical learning and biostatistics. His research focuses on the SLOPE estimator, sparsity, and geometric properties in penalized estimation. He has contributed to mathematical modeling in metabolomics and occupational health risk assessment. Tardivel holds a PhD in Statistics and completed his HDR thesis on SLOPE estimator properties. Education: PhD in Statistics, HDR thesis on SLOPE estimator Research Interests: Tardivel's work bridges statistical learning, biostatistics, and metabolomics. Key areas include multiple testing procedures, sparse representations, and geometric interpretations of penalized estimation. He applies these methods to biomedical and occupational health data analysis. Collaborations: He actively participates in the joint statistical learning seminar organized by the University of Lund, University of Burgundy, and University of Wroclaw, hosted via Zoom. Tardivel also contributes to software development for NMR spectra analysis through the ASICS R package. Contact: Email: patrick.tardivel@u-bourgogne.fr
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