جست‌وجوی استاد و پژوهشگر

• Stochastic Optimization
• Optimal Control
• Decomposition Methods
• +۵ مورد دیگر

Jean-Philippe Chancelier is a researcher at Cermics (École des Ponts ParisTech) , focusing on stochastic optimization , optimal control , and decomposition methods . His work bridges mathematical theory and computational tools, with applications in energy systems, supply chain management, and sparse optimization. Key Research Themes : Stochastic and multistage optimization Capra-convexity and sparsity-inducing norms Decomposition-coordination algorithms Tropical numerical methods for control Recent Article Trends : Unified frameworks for sparsity-inducing norms Coq-assisted formalization of conditional separation Two-time-scale models for storage and energy systems Complexity bounds for POMDP and deterministic systems Applications in hydrogen infrastructure and reservoir management Software Contributions : NSP (Numerical Scientific Programming) package Integration with ScicosLab and ScilabGtk His research often involves collaboration with colleagues like Michel De Lara, Pierre Carpentier, and Benoît Heymann, and is published in journals such as Transportation Science , JMLR , and Computers & Chemical Engineering .

Jérôme Lelongمشاهده پروفایل

استاد

• Computational Finance
• Stochastic Optimization
• Monte Carlo Methods
• +۳ مورد دیگر

Jérôme Lelong is a Professor at Grenoble Institute of Technology - ENSIMAG, Université Grenoble Alpes, and Director of AMIES. He is a member of the DAO team at Laboratoire Jean Kuntzmann, where he leads the development of the open-source numerical library PNL. He also maintains the LaTeX extension for Visual Studio Code (LaTeX Workshop) and vscode-latex-basics. His research focuses on: Computational finance and stochastic optimization Advanced Monte Carlo methods and parallel computing Stochastic modeling for financial and industrial applications Machine learning approaches for option pricing and risk management Lelong's publications demonstrate consistent focus on stochastic methods in finance, with recent emphasis on machine learning integration. His work bridges theoretical mathematics with practical applications in derivatives pricing, risk analysis, and high-performance computing. He actively advises doctoral students, including: Valentin Reis (co-directed, defended 2018) Zineb El Filali Ech-Chafiq (CIFRE Natixis, defended 2022) Tom Picard (CIFRE Nexialog Consulting, defended 2023) William Thévenot (CIFRE SCOR, ongoing) Ghada Ben Youssef (CIFRE Exiom Partners, ongoing) At ENSIMAG, he co-heads the Financial Engineering major and teaches specialized courses including FX derivatives, Monte Carlo methods for finance, and C++ implementation techniques for pricing models.

Julien Reygnerمشاهده پروفایل

استاد

• Stochastic Processes
• Particle Systems
• Numerical Methods
• +۴ مورد دیگر

Julien Reygner is a Professor at École des Ponts ParisTech (ENPC) and Deputy Director of CERMICS research laboratory. He holds a concurrent part-time position as Associate Professor at École Polytechnique. His academic background includes a PhD from Sorbonne Université (2011-2014) and a Habilitation thesis (2021). He has held positions as Assistant Professor at ENPC (2018-2023) and CNRS postdoctoral fellow at ENS Lyon (2014-2015). Reygner's research explores stochastic processes, particle systems, and numerical methods with applications in uncertainty quantification. His work bridges probability theory with mathematical analysis and statistical mechanics. Primary domains include Langevin dynamics, mean-field systems, Fokker-Planck equations, and computational statistics. His publications demonstrate consistent focus on stochastic modeling, particle methods, and probabilistic approaches to partial differential equations. Recent work emphasizes convergence analysis, structural reliability, and applications in statistical learning. He actively advises doctoral candidates, including projects on particle systems, Langevin processes, and structural fatigue. Research grants include ANR projects: Conviviality (2023-2028), QuAMProcs (2019-2024), and EFI (2018-2022). He leads CERMICS' Applied Probability team and co-organizes seminars on data transitions and uncertainty quantification.

• Web Data Management
• Probabilistic Databases
• Provenance in Databases
• +۴ مورد دیگر

Pierre Senellart is a Professor in the Computer Science Department at École normale supérieure (ENS, Université PSL) and deputy director of the DI ENS laboratory, a joint CNRS/Inria/ENS unit. He leads the Valda team at Inria Paris and holds a chair in the PRAIRIE Paris School of AI. University: École normale supérieure (Université PSL) School: Department of Computer Science (DI ENS) Academic Rank: Professor Research Interests: His work bridges theoretical and practical aspects of web data management, including web crawling, archiving, information extraction, uncertainty management, and intensional data systems. He contributes to probabilistic XML data models, provenance tracking, and knowledge base construction. Recent Article Trends: His Google Scholar publications (2025–2023) focus on provability in probabilistic databases, theorem extraction from PDFs, and AI applications in uncertain data analysis. Topics span game theory, knowledge compilation, and multi-modal learning for scientific documents. Scientific Awards: Junior member of Institut Universitaire de France (2020–2025) Distinguished SIGMOD 2017 Program Committee Member ACM HyperText 2014 Douglas Engelbart Best Paper Award Adviser of Clément Genzmer (SIGMOD Programming Contest 2009 winner) Grants & Projects: He secures major funding (e.g., PRAIRIE Paris School of AI: €575k, DesCartes CNRS@CREATE: SGD50M), with roles in research projects like Dissemin (open science platforms) and ARCOMEM (social web archiving).

Kevin GODINEAUمشاهده پروفایل

مدرس

• Additive Manufacturing
• Laser Powder Bed Fusion (LPBF)
• Digital Twin Technology
• +۳ مورد دیگر

Kevin GODINEAU is a Lecturer at ENS Paris-Saclay (Université Paris-Saclay) since 2020, specializing in mechanical engineering and additive manufacturing. He coordinates the ORACLE project focused on trajectory interpolation optimization in LPBF processes and contributes to the RA(SI) educational project involving augmented reality for engineering sciences. Education: Doctorate in Mechanics (2019), Master 2 in Digital Product Process Engineering (2016) Research Focus: Laser powder bed fusion (LPBF), opto-mechanical calibration, digital twin development, and augmented reality applications in production system calibration His recent publications analyze spatter formation in Inconel 718 manufacturing, laser trajectory control, and calibration techniques for LPBF machines. He supervises Master's internships and teaches modules on poly-articulated systems, production machine control, and proof-of-concept methodologies. Scientific contributions include: Industrial Engineering Science Aggregation Competition Winner (2015) Key developments in CNC-laser interaction modeling for additive manufacturing

Robert McCraithمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Autonomous Driving
• +۵ مورد دیگر

Robert McCraith is a Researcher at the Oxford Internet Institute, University of Oxford , and a Computer Vision Research Scientist at AiFi since January 2022. He completed his DPhil (PhD) in Computer Vision and Machine Learning at the University of Oxford in 2022, with prior academic training in Computer Science, Mathematics, and Statistics at Maynooth University (2017). His research focuses on Computer Vision, Machine Learning, and Generative AI , with applications to autonomous systems, medical question answering, and retail AI. Recent work includes Lifting 2D Object Locations to 3D (ICRA 2022), Monocular Depth Estimation (arXiv 2020), and evaluations of Large Language Models in healthcare contexts (2023-2024). Scientific Contributions: Developed LiDAR outlier discounting frameworks for 3D object localization Created synthetic data pipelines for real-time velocity estimation Explored human-inspired learning strategies for LLM fine-tuning Investigated domain-specific limitations of language models Teaching & Mentorship: Teaching Assistant in Computer Vision, AI, and Algorithms at Oxford (2019-2021) Mentored junior researchers at AiFi Volunteer mentor at CoderDojo (2011-2017)

Jean-Christophe Pesquetمشاهده پروفایل

استاد

• Optimization
• Signal Processing
• Image Processing
• +۴ مورد دیگر

Jean-Christophe Pesquet is a Professor affiliated with the Laboratory Digital Vision Center. His work spans optimization, inverse problems, artificial intelligence, signal and image processing . He actively collaborates on advanced algorithm design and computational methods for imaging and signal recovery. Key research areas: Optimization, Wavelet Analysis, Blind Source Separation, Deep Learning Recent publications focus on nonconvex optimization, primal-dual splitting, and deep unfolding techniques. His methodological contributions include proximal algorithms, stochastic subspace approaches, and Bregman divergences. Applications range from biomedical imaging (e.g., PET, CT scans) to seismic data analysis and aerospace defect detection. Notable collaborations include researchers like Emilie Chouzenoux , Audrey Repetti , and Caroline Chaux . Current projects integrate neural networks with classical signal processing frameworks (e.g., lifting schemes, MCMC algorithms).

Stephane Rossignolمشاهده پروفایل

پژوهشگر

• Economics
• Speech Signal Processing
• Political Economy
• +۳ مورد دیگر

Stephane Rossignol is an active researcher with interdisciplinary contributions to economics, political science, and speech signal processing. His work spans theoretical modeling and empirical analysis. Affiliation: Unspecified (likely affiliated with French academic institutions given the context) Research Themes: Spatial competition, pandemic dynamics, speech processing algorithms Research Interests : Economics : Political economy, inequality analysis, social insurance mechanisms Political Science : Valence politics, international agreements, trust in governance Signal Processing : Speech voicing extraction, co-channel classification, analytic signal methods Recent Publications highlight his focus on: Econometric modeling of political and health policies Statistical approaches to speech signal analysis Interdisciplinary studies combining Bayesian inference and social dynamics

Stanislav Aranovskiyمشاهده پروفایل

مدرس

• adaptive control
• nonlinear systems
• parameter estimation
• +۳ مورد دیگر

Stanislav Aranovskiy is a Lecturer with the AUT team at CentraleSupélec in Rennes, France, specializing in Control Science and Engineering. He is affiliated with the Rennes Institute of Electronics and Telecommunications and maintains an active role in the international control community as a member of the IFAC Technical Committee 1.2 since 2016 and the IEEE Control Systems Society's Technical Committee on System Identification and Adaptive Control since 2018. He achieved IEEE Senior Member status in 2018, reflecting his significant contributions to the field. Dr. Aranovskiy's research spans several critical areas in modern control theory: Adaptive control and estimation methodologies Nonlinear systems analysis and control design Advanced parameter estimation techniques Observers design for complex dynamic systems Disturbance attenuation methods Dynamic Regressor Extension and Mixing (DREM) framework development His publication trajectory reveals a strategic evolution from fundamental adaptive control theory toward increasingly sophisticated data-driven approaches. Recent work demonstrates substantial contributions to Model Predictive Control (MPC) frameworks, particularly in maintaining relevant datasets using Willems' fundamental lemma. A defining characteristic of his research is the innovative application of DREM methodology across diverse estimation problems, consistently demonstrating superior convergence properties compared to conventional approaches. This work has found applications in power systems, motor control, and signal processing domains. Professional Recognition: Member of IFAC Technical Committee 1.2 (Adaptive and Learning Systems) since 2016 Member of IEEE Control Systems Society Technical Committee on System Identification and Adaptive Control since 2018 IEEE Senior Member since 2018 As a Lecturer at CentraleSupélec, Dr. Aranovskiy contributes to both teaching and research supervision. His HDR (Habilitation à Diriger des Recherches) completed in December 2023 qualifies him to supervise doctoral candidates in the French academic system. His collaborative research network includes prominent scholars such as Alexey Bobtsov, Roméo Ortega, and Anton Pyrkin, indicating a robust research environment focused on theoretical and applied control systems. Dr. Aranovskiy works within the AUT (Automatic Control) team at CentraleSupélec, which forms part of the Rennes Institute of Electronics and Telecommunications. His research group maintains strong interdisciplinary connections, with publications spanning theoretical control advances to practical implementations in renewable energy systems and electromechanical devices.

• Audio Signal Processing
• Explainable AI
• Speaker Localization
• +۳ مورد دیگر

Théo Mariotte is an Assistant Professor at Le Mans University, affiliated with the LST team within the LIUM laboratory. His research focuses on lightweight and explainable audio processing systems, particularly for speaker localization and segmentation in multi-microphone environments. His primary research interests include Audio Signal Processing , Explainable AI , Speaker Localization , and Machine Learning for audio applications. Mariotte develops efficient algorithms for speaker segmentation, speech separation, and overlapped speech detection using multimicrophone arrays, with emphasis on interpretability and real-world applicability in meeting scenarios. Mariotte's publication record shows a strong focus on multimicrophone audio processing, with recent work spanning speaker segmentation (2025), multiple choice learning for speech separation (2025), explainable audio segmentation (2024), and beamformer selection for speaker diarization (2024). His research demonstrates consistent innovation in handling overlapped speech and developing lightweight, interpretable models for real-world audio applications. No scientific awards were mentioned in the source material. Mariotte actively contributes to open-source audio processing tools, including repositories for spherical acoustic holography, speaker localization algorithms, and MATLAB audio processing tools. His work bridges theoretical machine learning with practical audio engineering applications. He operates within the LIUM laboratory's LST (likely Speech and Text) team, focusing on developing robust audio processing solutions for meeting environments using distributed microphone systems.

• Combinatorial optimization
• Explainable AI
• Medical informatics
• +۲ مورد دیگر

Julie Jacques is a Senior Lecturer at the University of Lille, affiliated with the Faculty of Science and Technology and the Department of Computer Science . She is a member of the CRISTAL laboratory's ORKAD team since 2022. Her career includes a research professor role at the Catholic Institute of Lille (2017–2022), where she established a digital sciences degree, and a research engineer position at Alicante (2008–2017). Her research focuses on the intersection of combinatorial optimization and white-box machine learning , with applications in medical informatics , explainable AI , neural architecture search , and asymmetric data analysis . Combinatorial optimization for classification rule search Biclustering Automatic recommendation systems Explainable AI Medical informatics She received the Force Awards 2015 for her industry-collaborative thesis. Contact: julie.jacques@univ-lille.fr .

Silvio Montresorمشاهده پروفایل

استادیار

• Digital Holography
• Speckle Noise Reduction
• Acoustic Emission Monitoring
• +۳ مورد دیگر

Silvio Montresor is a Teacher-researcher at the Institute of Acoustics within Le Mans Université, actively contributing to acoustics and optical metrology research since at least 2018. His work bridges theoretical acoustics with practical engineering applications, particularly in structural health monitoring and digital holography. His research focuses on speckle noise reduction in digital holography , acoustic emission monitoring of structural damage , and ultrasonic characterization of composite materials . Key contributions include developing deep learning algorithms for phase image denoising and novel methods for damage localization in reinforced concrete using acoustic emissions. His work spans civil engineering, materials science, and biomedical applications. Analysis of his publication trends reveals consistent focus on computational imaging techniques (2018-2024), with increasing integration of machine learning since 2021. His research demonstrates strong interdisciplinary connections between acoustics , optics , and structural engineering , particularly in non-destructive testing methodologies. As a core member of the Institute of Acoustics, Montresor contributes to transversal research axes including Metamaterials , Non-linear Acoustics , and NDET (Non-Destructive Evaluation Technologies). His work supports the lab's mission in Materials Acoustics , Elastic Waves in Complex Media , and Physics of Musical Instruments , utilizing advanced facilities for laser ultrasonics and electroacoustic sensor development.

Serrestou Youssefمشاهده پروفایل

مدرس

• Acoustics
• Wireless Sensor Networks
• Speech Emotion Recognition
• +۳ مورد دیگر

Serrestou Youssef is a Lecturer at ENSIM and associated researcher at LAUM (Laboratoire d'Acoustique de l'Université du Mans) within Le Mans Université. His work bridges acoustics, signal processing, and machine learning. Research Interests : Acoustic signal processing Wireless sensor network optimization Speech emotion recognition using machine learning Application of reinforcement learning and genetic algorithms Scientific Production : Focuses on digital correction techniques, acoustics, and smart network deployment. Collaborates with researchers like Kosai Raoof and Mohamed Mbarki.

Soizic Terrienمشاهده پروفایل

پژوهشگر

• Nonlinear Dynamics
• Acoustics
• Musical Instruments
• +۳ مورد دیگر

Soizic Terrien is a CNRS Researcher at the Laboratoire d'Acoustique de l'Université du Mans (LAUM), Le Mans Université, affiliated with the 'Guides & Structures' team. Her research focuses on nonlinear dynamics of self-oscillating systems, spanning musical acoustics (strings, wind instruments) and optical systems (pulsating lasers) using bifurcation theory and dynamical systems analysis. Research interests include: Nonlinear dynamics of musical instruments (violins, trombones, recorders) Bifurcation analysis of sound production mechanisms Opto-acoustic systems with delayed feedback Computational modeling of transient dynamics Basins of attraction in high-dimensional systems Recent publications demonstrate strong focus on attack transients in wind instruments, quasiperiodic regimes in flutes, machine learning applications for sound classification, and laser pulse dynamics. Work consistently integrates theoretical modeling with computational validation.

François HUمشاهده پروفایل

مدرس

• Machine Learning
• Insurance
• Finance
• +۷ مورد دیگر

François HU is a Lead AI Researcher and Lecturer in Machine Learning and Computational Statistics at institutions including Conservatoire national des arts et métiers (Cnam), Institut Polytechnique de Paris (ENSAE), EPITA, and Institut des Actuaires. Since 2024, he has led the R&D AI Lab at Milliman France, focusing on Generative AI and Trustworthy AI for insurance and finance applications. PhD in Machine Learning and Insurance (2019-2022), Institut Polytechnique de Paris (CREST-ENSAE) Postdoctoral Researcher (2022-2024), Université de Montréal (Department of Mathematics and Statistics), affiliated with MILA and Algora Lab His research spans algorithmic fairness, semi-supervised learning, NLP, and GenAI, with applications in insurance, biostatistics, and finance. He developed an Early Warning System for Infectious Diseases under the Mathematics for Public Health initiative and contributed to ESG concept identification in Canadian companies via Algora Lab. Recent publications focus on fairness in multi-class classification, Wasserstein barycenters for bias mitigation, and trustworthy AI frameworks. Scientific awards include the 2022 French best actuarial science thesis award. He has taught Python programming, numerical algorithms, Bayesian machine learning, and optimization at EPITA and Institut Polytechnique de Paris, and developed educational materials including notebooks and datasets for practical workshops.