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Claude DELPHAView profile

Professor

• Signal Processing
• Fault Diagnosis and Prognosis
• Electrical Engineering Systems
• +4 more

Claude DELPHA is a Full Professor at Université Paris Saclay, affiliated with CentraleSupélec’s Laboratoire des Signaux et Systèmes (L2S). He holds an IEEE Senior Member status and has been with L2S since 2001. His expertise spans signal processing, fault diagnosis, electrical engineering systems, and machine learning. He leads the Modelling and Estimation team (GME) at L2S and oversees engineering admissions at Polytech Paris Saclay. Education: PhD in Instrumentation & Measurements and Signal Processing from Université de Metz, with a focus on intelligent sensor systems. Graduate degree in Electrical and Signal Processing Engineering. Research Interests: Multidimensional/statistical signal processing, fault diagnosis/prognosis (modeling, detection, estimation), electrical systems (drives, converters, PV), data hiding (watermarking), and pattern recognition (machine/deep learning). Active in energy systems, industry 4.0, and health/biology applications. Professional Roles: Director of GME research team, Polytech admissions lead, member of Polytech’s executive and academic boards, and IUT department council member. Engaged in labs like SYCOMORE and ILOCOS. Publications: Over 200 works since 2015, focusing on fault diagnosis in electrical systems, photovoltaic modules, bearings, and tidal turbines. Key methods include Kullback-Leibler divergence, Jensen-Shannon divergence, Mahalanobis distance, and PCA-based approaches. Awards: Not explicitly listed in provided texts.

Thierry BadardView profile

Associate Professor

• Geospatial Big Data
• GeoAI
• GeoNLP
• +9 more

Thierry Badard is an Associate Professor at the Department of Geomatics Sciences , Université Laval, where he also serves as Director of the Center for Research in Geospatial Data and Intelligence (CRDIG) . With over 28 years of experience in geospatial science, he leads research initiatives at the intersection of GeoAI , LiDAR processing , and smart city technologies . Director, CRDIG (2016-2022) Steering Committee Member, Big Data Research Centre (CRDM) Researcher, Institute for Intelligence and Data (IID) Research Expertise spans geospatial big data, GeoNLP, and IoT applications for digital twins. His work addresses flood risk modeling , 3D urban analytics , and environmental monitoring through AI-driven solutions. Recent publications focus on contrastive learning for LiDAR segmentation and geospatial ontologies for early warning systems. Grant Leadership includes collaborative projects on smart insurance analytics (2018-2025), Arctic bioaerosol research (2019-2025), and Quebec-Morocco digital twin partnerships (2022-2023). He has advised 15+ graduate students in geomatics and related fields.

• Statistics
• Learning Theory
• High-dimensional statistics
• +4 more

Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE/CREST, École Nationale de la Statistique et de l'Administration Économique since September 2020. Previously, he was a postdoctoral researcher at the Laboratory for Computational and Statistical Learning at the University of Genoa (2019-2020). He completed his PhD in Statistics at École Polytechnique under the supervision of Stéphane Gaïffas and Erwan Scornet. His educational background includes a Master's degree in Mathematics with specialization in Probability and Random Models (2016), a Master's degree in Fundamental Mathematics (2015), and a Bachelor's degree in Mathematics from Pierre and Marie Curie University and École Normale Supérieure (2013). Dr. Mourtada's research focuses on the intersection of statistics and learning theory, with particular interest in high-dimensional statistics, online learning, and density estimation. His work explores the complexity of prediction and estimation problems through rigorous theoretical analysis. His research spans statistical learning theory, robust statistics, and the theoretical foundations of machine learning algorithms. His publication record since 2017 demonstrates consistent contributions to top-tier venues in statistics and machine learning, with recent work focusing on universal coding, aggregation methods, robust regression, and the theoretical analysis of kernel methods and random forests. His research shows a progression from online learning and expert aggregation to more complex statistical learning problems involving high-dimensional data and model misspecification. He teaches courses including Statistical Learning Theory for Master 2 Data Science students and Probability Theory at ENSAE. His teaching spans theoretical foundations of machine learning and core probability concepts for advanced statistics students.

Paolo PapottiView profile

Associate Professor

• Data Management
• Data Integration
• Data Cleaning
• +3 more

Paolo Papotti is an Associate Professor of Computer Science at EURECOM (France) since 2017, affiliated with the Data Science department. Previously, he was a senior scientist at QCRI (Qatar) and an assistant professor at Arizona State University (USA). He earned his PhD in Computer Science from the University of Roma Tre (Italy) in 2007, following an MEng in Computer Engineering from the same institution in 2003. His research focuses on scalable data management, data integration, data cleaning, and computational fact-checking. Notable contributions include work on knowledge graph rule discovery (Rudik), fact-checking frameworks (Scrutinizer), and data quality systems. His research has been supported by awards such as the 2020 Google Faculty Research Fellowship. Key publications include advancements in table representation learning, LLM-based data querying, and crowdsourced fact-checking validation. His work spans theoretical foundations and practical tools for improving data quality and information trustworthiness.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +4 more

Alain Durmus is a Professor at École Polytechnique, affiliated with the applied mathematics department (CMAP). His research focuses on computational statistics, machine learning, and stochastic methods, including Monte Carlo algorithms, Bayesian inference, and optimization. He explores topics such as Markov chain Monte Carlo (MCMC), stochastic approximation, and generative models. His work emphasizes theoretical guarantees for algorithms like Langevin Monte Carlo and Hamiltonian Monte Carlo, with applications to high-dimensional Bayesian inference and inverse problems. Key contributions include hypocoercivity analysis of piecewise deterministic MCMC processes, convergence guarantees for stochastic gradient methods, and the development of efficient sampling techniques. He has also contributed to Bayesian imaging and federated learning through works like the QLSD algorithm. Awarded the Best Student Paper Award at ICASSP 2020 for his work on the Sliced-Wasserstein distance. His teaching spans mathematical statistics, stochastic methods, and probability at École Polytechnique and ENS Paris-Saclay. He has also contributed to conferences and workshops on topics ranging from MCMC convergence to optimization in machine learning.

Hugues AschardView profile

Researcher

• Statistical Genetics
• Genome-Wide Association Studies (GWAS)
• Microbiome Research
• +7 more

Hugues Aschard is a Principal Investigator and Structure Manager at the Pasteur Institute in Paris, where he leads research in statistical genetics, microbiome analysis, and computational genomics. He is the principal investigator of the MicMat project, the EpiGenCOV Consortium, and several bioinformatics software initiatives including JASS, RAISS, and MGMM. Research Interests: Statistical and computational methods in genetics Genome-wide association studies (GWAS) Gene-environment interactions Microbiome and host genetics in inflammatory bowel disease Genetic epidemiology of infectious diseases like COVID-19 Development of open-source tools for multi-trait and summary-statistic analysis Recent Research Trends: His recent publications and projects emphasize integrative genetic modeling, multi-trait analysis across diverse populations, and the development of novel computational methods to handle missing data and improve SNP discovery. His work bridges statistical innovation with biological and clinical applications in complex diseases. Scientific Contributions: Development of JASS, RAISS, and MGMM software tools Leadership in large-scale consortia like EpiGenCOV Advancing methods for cross-ancestry genetic studies Advising and Collaboration: He supervises multiple PhD students and postdoctoral fellows, including Christophe Boetto, Antoine Auvergne, and Lucas Chataigner. He collaborates with major institutions such as APHP and CNRGH. His team includes research engineers and administrative staff, indicating an active and well-supported research group. Laboratories and Teams: He is a key member of the Biomaterials and Microfluidics team at the Pasteur Institute, where he contributes to interdisciplinary research involving Bayesian decision processes and genetic modeling.

• Asset Pricing
• Investment Strategies
• Portfolio Management
• +4 more

Adam Zaremba is an Associate Professor in the Finance & Accounting department at MBS. His expertise spans asset pricing, investment strategies, portfolio management, international finance, and capital allocation. Prior to joining MBS in 2020, he was affiliated with the University of Dubai (UAE) for three years. Education: PhD in Finance from Poznan University of Economics and Business (2012) His research integrates artificial intelligence with financial markets, focusing on factor investing, cryptocurrency dynamics, ESG strategies, and climate finance. Recent work includes machine learning applications in factor return prediction, cross-sectional cryptocurrency interactions, and non-standard errors in crypto analytics. Themes in his publications highlight advancements in momentum investing , capital allocation , and risk modeling across traditional and emerging markets. He has contributed to journals like the Journal of Finance , Journal of Financial Economics , and Review of Finance .

Marc PlantevitView profile

Professor

• Data Mining
• Machine Learning
• Artificial Intelligence
• +7 more

Marc Plantevit is a Full Professor at EPITA, member of the Laboratoire LRDE (LRDE). Previously, he served as an Associate Professor at University Claude Bernard Lyon 1 (2010–2021), leading the Data Mining & Machine Learning group at LIRIS lab. He holds a PhD in Computer Science from the University of Montpellier (2008), supervised by Maguelonne Teisseire and Anne Laurent at LIRMM Lab. His research focuses on foundational data mining, graph mining, subgroup discovery, and explainable AI. He is an editorial board member of Data Mining and Knowledge Discovery Journal and has held roles such as CAPES NSI jury member and former head of the Data Mining & Machine Learning group at LIRIS. Research Interests : Data Mining, Machine Learning, Explainable AI, Graph Mining, Subgroup Discovery, Exceptional Model Mining, Constraint-based Pattern Mining, and applications in neuroscience and energy systems. His work explores interpretable AI, GNN explainability, and interdisciplinary applications like odor perception modeling and electricity price forecasting. Key Contributions : Best Paper Award at EGC'22 for work on GNN representations. Active in program committees for ECMLPKDD, IJCAI, and IEEE ICDM . Supervised PhD students working on GNN explainability, electricity forecasting, and machine learning in exposome studies. Labs & Teams : LRDE (EPITA), previously involved with LIRIS (UMR CNRS 5205) and collaborative projects with institutions like INSA Lyon and ISGlobal (Barcelona).

Olivier FARGESView profile

Senior Lecturer

• Thermal Engineering
• Energy Systems
• Fluid Dynamics
• +3 more

Olivier FARGES is a Senior Lecturer and HDR (Habilitation à Diriger des Recherches) holder at the University of Lorraine, affiliated with ENSGSI (École Nationale Supérieure de Géologie et Sciences Industrielles) within the Groupe INP. He serves as Director of Industrial Partnerships at ENSGSI and is part of the LEMTA Laboratory (CNRS-University of Lorraine), focusing on multiphysics and multiscale modeling of heat transfer in complex environments. His academic roles include teaching courses such as Heat and Mass Transfer, Fluid Mechanics, Scientific Computing Modeling, and Renewable Energy. Dr. FARGES holds a Ph.D. in Energy and New R&D (2014) and an Engineering degree in Energy Engineering (2010), both from the École de Mines Albi. His research emphasizes coupled conductive-radiative heat transfer in porous media, thermal property characterization of heterogeneous materials, and Monte Carlo-based computational methods for energy systems. He has contributed to advancements in photovoltaic system modeling, solar thermal power optimization, and urban climate studies. His work bridges theoretical and applied thermal engineering, with applications in sustainable energy systems, material science, and industrial partnerships. Key research themes include radiative transfer modeling, multiphysics simulation frameworks, and the development of innovative tools for thermal property measurement and energy performance assessment.

Clément FERNANDESView profile

Lecturer

• Hidden Markov Models
• Data Segmentation
• Image Segmentation
• +3 more

Clément FERNANDES is a Lecturer at Telecom SudParis, part of the SAMOVAR laboratory under Institut Polytechnique de Paris. His research focuses on statistical modeling and unsupervised learning techniques, particularly applying hidden Markov chains to data and image segmentation. He holds a PhD in Discrete Mathematics from Institut Polytechnique de Paris (2022), where his thesis explored Triplet Markov Chains in image segmentation. His recent work includes advancing non-stationary data segmentation methods and developing forecasting models using Gaussian Markov frameworks. Though no explicit grants or awards are mentioned, his publications reflect contributions to approximate reasoning and applied statistics in engineering contexts. He is affiliated with the SAMOVAR laboratory and maintains an active publication record in top-tier journals like International Journal of Approximate Reasoning .

• Extreme Value Theory
• Applied Mathematics
• Gaussian Processes
• +5 more

Marie Kratz is a Full Professor at ESSEC Business School (Cergy, France), affiliated with the CREAR - Center of Research in Econo-finance and Actuarial Sciences on Risk . Her work bridges theoretical and applied domains in extreme value theory , heavy-tailed distributions , and risk management , with applications in finance, cybersecurity, and neuroscience. Research Focus : Extreme value theory, risk concentration, cyber risk modeling, Gaussian random fields, and pro-cyclicality in financial risk measures. Collaborations : Active collaborations with Michel Dacorogna, Marcel Bräutigam, and Sibsankar Singha on cyber risk and financial applications. Methodologies : Development of the Normex method for aggregated heavy-tailed risks, hybrid Gaussian-Pareto models, and near-explosive random coefficient autoregressive models. Awards and Recognition : No specific awards mentioned in the text.

• Conformal Field Theory
• Quantum Field Theory
• Renormalization Group
• +4 more

Slava Rychkov is a Permanent Professor of Theoretical Physics at the Institut des Hautes Études Scientifiques (IHES), a position he has held since 2017. He specializes in strongly coupled quantum and conformal field theories, with applications across high energy physics, statistical mechanics, and condensed matter physics. His current research focuses on the conformal bootstrap and renormalization group techniques, including both perturbative and nonperturbative methods like tensor network renormalization. Education: Ph.D. in Physics, Princeton University (2002) Master of Science, Moscow Institute of Physics and Technology (1996) Recent research highlights include a groundbreaking connection between Deligne categories and symmetries of probabilistic loop ensembles in statistical physics, and a novel method for analytic continuation of Euclidean CFTs to Lorentzian signature. His work on the 2+ϵ expansion challenges established assumptions about critical exponents in 3D systems. Publications span topics from tensor renormalization group methods to rigorous mathematical approaches in the conformal bootstrap program. Scientific Awards: Jacques Solvay International Chair in Physics (2025) Grand Prix Mergier-Bourdeix, French Academy of Sciences (2019) New Horizons in Physics Prize (2014) As Deputy Director of the Simons Collaboration on the Nonperturbative Bootstrap, Rychkov leads efforts to rigorously analyze conformal field theories. His former advisees include prominent researchers at institutions like EPFL, Princeton, and Università di Genova. Current projects focus on resolving fundamental questions about critical phenomena and phase transitions using advanced mathematical physics tools.

Jean-Baptiste AlayracView profile

Researcher

• Computer Vision
• Machine Learning
• Multimodal Learning
• +3 more

Jean-Baptiste Alayrac is a Researcher at DeepMind, focusing on structured learning from video and natural language. His academic background includes a PhD from the Sierra and Willow groups at Ecole Normale Supérieure and Telecom ParisTech, where he explored machine learning and computer vision. He has held teaching roles as a Teaching Assistant at Ecole Normale Supérieure and other universities, contributing to courses in statistical machine learning and mathematics. His research interests span multimodal learning, vision-language models, self-supervised learning, and efficient retrieval systems. Notable projects include the Flamingo model for few-shot learning and the Perceiver IO architecture for structured data processing. He has also contributed to foundational works like HowTo100M, leveraging large-scale video-text embeddings. Alayrac's publications emphasize cross-modal interactions, with key contributions in adversarial robustness, layered video representations, and weakly supervised learning. His work often bridges computer vision and natural language processing, with applications in instructional video analysis and cross-lingual translation.

Lionel TruquetView profile

Lecturer

• Statistics for dependent data
• Time series analysis
• Markov chains
• +2 more

Lionel Truquet is a Lecturer-Researcher in Statistics at ENSAI (École Nationale de la Statistique et de l'Administration Économique), where he focuses on Statistics for dependent data and Time series analysis . He serves as a Director of Research and has contributed significantly to fields like Markov chains and nonlinear dynamics . Research Interests: Time series models for ecological and economic data Statistical inference for categorical and discrete-valued processes Ergodic properties of Markov chains in random environments Mixing conditions for nonstationary processes Perturbation techniques in stochastic modeling Recent Publications: His work spans nearest neighbor sampling , multivariate autoregressive models , and mixing properties of count processes , with applications in ecology and econometrics. Key trends include nonparametric methods for high-dimensional data and stationarity analysis in time-varying systems. Scientific Awards: TJALLING C. KOOPMANS ECONOMETRIC THEORY PRIZE (2021–2023) for groundbreaking work on multivariate count autoregressions.

Geoffroy CouteauView profile

Researcher

• Cryptography
• Secure Multiparty Computation
• Zero-Knowledge Proofs
• +5 more

Geoffroy Couteau is a CNRS research scientist at IRIF (Institut de Recherche en Informatique Fondamentale), Université Paris Cité, where he conducts research in theoretical and applied cryptography. He obtained his PhD from École Normale Supérieure de Paris in 2017 under the supervision of David Pointcheval and Hoeteck Wee, followed by a postdoctoral position at Karlsruhe Institute of Technology (KIT) from 2017 to 2019. His primary research interests include secure multiparty computation, zero-knowledge proofs, and the theoretical foundations of cryptography, with a particular emphasis on pseudorandom correlation generators and efficiency improvements in cryptographic protocols. He has made significant contributions to fine-grained cryptography, non-interactive zero-knowledge proofs, and post-quantum secure computation. The recent publications reflect a strong trend toward foundational advances in secure computation, with increasing focus on efficiency, practicality, and connections to complexity theory and learning theory. His work often bridges theoretical hardness assumptions with practical protocol design. ERC Starting Grant (2023) for project OBELiSC (Overcoming Barriers and Efficiency Limitations in Secure Computation) Geoffroy Couteau has advised numerous PhD and master’s students, including Dung Bui, Clément Ducros, Eliana Carozza, and Ulysse Léchine. He has also hosted many visiting students and postdocs, fostering a vibrant research group. He has served on the program committees of major conferences such as EUROCRYPT, CRYPTO, TCC, and PKC. He is currently leading research in cryptography at IRIF and is involved in postdoctoral hiring for projects in advanced cryptographic primitives. He maintains a research blog and resource collection for students, including LaTeX templates, a probability cheat sheet, and curated answers to common cryptography questions.