Find Academic Supervisors

• Formal Verification
• Software Testing
• Programming Languages
• +12 more

Alastair F. Donaldson is a Professor and Director of Research in the Department of Computing at Imperial College London, where he leads the FastPL research group. His work bridges formal methods, software testing, and programming languages, with a focus on enhancing the reliability of high-performance and parallel software systems. He has held key roles including Director of Research (since 2023) and previously served as Lecturer (2011–2014), Senior Lecturer (2014–2017), and Reader (2017–2020) before being promoted to Professor in 2020. His research interests include formal verification, compiler testing, GPU programming, concurrency, and fuzzing. He has made significant contributions to the verification of GPU kernels, metamorphic testing of graphics drivers, and the development of tools like GPUVerify and GraphicsFuzz. His work combines theoretical rigor with practical impact, demonstrated by the acquisition of his startup GraphicsFuzz by Google in 2018 and his subsequent roles as Senior Software Engineer and Visiting Researcher at Google. His recent publications reflect a sustained focus on compiler and system reliability, with trends in fuzzing, formal specification, and automated testing of complex systems such as WebGPU, CXL cache coherence, and large language models for code generation. His work increasingly integrates empirical validation with formal techniques to uncover subtle bugs in real-world systems. Scientific awards and recognitions include: 2017 BCS Roger Needham Award EPSRC Early Career Fellowship Fellow of the British Computer Society Best Paper awards at EuroSys 2024, MET 2021, IISWC 2019, IWOCL 2019, and ICST 2016 Best Industry Paper at ICST 2024 ACM SIGSOFT Distinguished Paper at ISSTA 2023 ACM SIGPLAN Most Influential OOPSLA Paper Award (2012 paper), awarded in 2022 Best Student Paper at PPoPP 2014 He has advised numerous PhD students and leads a vibrant research group. He has secured significant research funding and collaborates extensively with industry and academia. His service includes leadership roles such as General Chair of PLDI 2020, PC Chair of ECOOP 2019, and Steering Committee Chair of PLDI (2022–2025). He also serves on the advisory board of PACM-PL and on program committees for top venues including POPL, OOPSLA, PLDI, ICSE, and ISSTA. He leads the FastPL research group, which focuses on the design and implementation of programming tools and techniques for reliable software. The group conducts cutting-edge research in compiler testing, formal methods, and high-performance systems, fostering collaboration across academia and industry.

Pierre MarionView profile

Researcher

• Deep Learning Theory
• Optimization of Neural Networks
• Generative Models
• +2 more

Pierre Marion is a Researcher at INRIA Paris, working within the Sierra research team since September 2025. His work focuses on the theoretical foundations of deep learning and he is beginning to explore applications of AI in mathematics. Marion has established collaborations across multiple institutions including EPFL, Sorbonne Université, and Google DeepMind. His educational background includes: Engineering degree from École polytechnique (2015-2018) with specialization in Applied Mathematics Master's degree from Sorbonne Université (2019-2020) PhD from Sorbonne Université (2020-2023) under the supervision of Gérard Biau and Jean-Philippe Vert Postdoctoral research at EPFL (2024) supervised by Lénaïc Chizat Marion's research interests primarily focus on the theory of deep learning, where he investigates the optimization and statistical properties of various neural network architectures. His work spans from shallow networks to complex generative models, with a particular emphasis on understanding the mathematical foundations that govern deep learning performance. Recently, he has begun exploring applications of AI in mathematical research, aiming to bridge the gap between theoretical machine learning and mathematical discovery. His research often combines rigorous theoretical analysis with practical implications for training deep neural networks. Analysis of Marion's recent publications reveals several key trends in his research. He has made significant contributions to understanding the role of large learning rates in optimization dynamics, demonstrating how they can accelerate convergence in logistic regression and prevent memorization in score-based generative models. His work on attention mechanisms has provided theoretical guarantees for their effectiveness in specific tasks like single-location regression and clustering. Additionally, Marion has extensively studied the connections between residual networks and neural ordinary differential equations , establishing generalization bounds and exploring scaling properties in the large-depth regime. His earlier work included contributions to natural language processing and quasi-Monte Carlo methods, reflecting a broad mathematical foundation that informs his current deep learning research. Marion has received several notable scientific awards: Runner-up PhD Award of AFIA (French Association for Artificial Intelligence) in 2024 Google PhD Fellowship in 2022 Ecole polytechnique Grand Prize of Research Internships in 2018 As an advisor, Marion currently supervises PhD student Yu-Han Wu (since 2024), with whom he has co-authored multiple publications on large learning rates and denoising score matching. Previously, he co-supervised several Master's students including Seorim Park, Yerkin Yesbay, and Nathan Doumèche. Marion has been actively involved in the machine learning community through conference organization (NeurIPS@Paris meetups), session chairing (ICSDS 2022), and extensive reviewing activities. He has served as a reviewer for top journals including JASA and Annals of Statistics, and conferences including NeurIPS and ICLR, where he was recognized as a top reviewer at NeurIPS 2023. Marion is a member of the Sierra research team at INRIA Paris, which focuses on machine learning theory and applications. He has also collaborated with researchers at CREST (Center for Research in Economics and Statistics), as evidenced by his participation in seminars organized by Anna Korba and Karim Lounici. His work often bridges theoretical computer science, statistics, and applied mathematics, reflecting the interdisciplinary nature of modern machine learning research.

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.

Anna KorbaView profile

Assistant Professor

• Machine Learning
• Nonparametric Statistics
• Kernel Methods
• +5 more

Anna Korba is an Assistant Professor at École Polytechnique, specifically affiliated with ENSAE/CREST in the Statistics Department since September 2020. She is also a co-administrator of the Master Data Science program at École Polytechnique. Her academic journey has positioned her as a leading researcher in machine learning, with particular expertise in kernel methods, optimal transport, and statistical optimization. Dr. Korba received her PhD from Telecom ParisTech in 2018 under the supervision of Prof. Stephan Clémençon. Prior to her current position, she was a postdoctoral researcher at University College London's Gatsby Computational Neuroscience Unit working with Arthur Gretton from December 2018 to August 2020. Her academic foundation includes a Master's degree in Machine Learning and Computer Vision (MVA) from ENS Cachan and ENSAE in 2015. Anna Korba's research primarily focuses on machine learning with emphasis on kernel methods, optimal transport, optimization, particle systems, and preference learning. Her work bridges theoretical statistics with practical machine learning applications, particularly in developing novel sampling and optimization methods. She has made significant contributions to understanding Wasserstein gradient flows, density ratio estimation, and variational inference techniques. Her publication record demonstrates a strong trajectory in top-tier machine learning conferences including ICML, NeurIPS, AISTATS, and ICLR. Her research shows a clear evolution from foundational work on ranking and preference learning during her PhD to more recent contributions in Wasserstein-based optimization, sampling methods, and deep probabilistic modeling. The interdisciplinary nature of her work connects statistics, optimization theory, and practical machine learning applications. Top 10% Oral Presentation at AISTATS 2022 Top 15% Long Oral Presentation at ICML 2021 She actively mentors PhD students and postdoctoral researchers, currently advising seven PhD candidates and having successfully guided several alumni to prestigious positions. Dr. Korba also contributes to the academic community through her role in administering the Master Data Science program and collaborating with researchers across institutions worldwide. As part of the CREST research center, Dr. Korba works within a vibrant team of researchers focused on statistics, machine learning, and their applications to economic and social sciences. Her research group includes current PhD students and postdocs working on various aspects of her research interests, creating a dynamic environment for advancing the field of statistical machine learning.

Maxime Ferreira Da CostaView profile

Associate Professor

• Signal Processing
• Data Science
• Inverse Problems
• +6 more

Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.

Ewelina RupnikView profile

Researcher

• Photogrammetry
• Pose Estimation
• Image-based 3D Reconstruction
• +7 more

Ewelina Rupnik is a prominent researcher in photogrammetry and remote sensing, currently holding a researcher position at the Laboratory on Geographic Information Science (LaSTIG) at the French National Institute of Geographic and Forest Information (IGN) since 2017. She also serves as an associate researcher at the Paris Institute of Earth Physics (IPGP). Rupnik has established herself as a leading expert in historical imagery processing, neural radiance fields, and bundle adjustment techniques. Her educational background includes a PhD in photogrammetry from the Vienna University of Technology (2015), an MSc in Engineering in Photogrammetry from AGH University of Science and Technology, Poland (2005-2010), and an Erasmus exchange at the Technische Universitaet Muenchen, Germany (2009/2010). She recently completed her Habilitation from Université Gustave Eiffel in June 2025. Rupnik's research focuses on advancing photogrammetric techniques for processing historical imagery, developing novel neural radiance field approaches for satellite imagery, and improving bundle adjustment methodologies. Her work bridges traditional photogrammetry with modern deep learning techniques, particularly in the context of sparse satellite views and historical multi-epoch imagery. She has made significant contributions to the open-source MicMac photogrammetry software project. Her recent publications demonstrate a strong trend toward integrating deep learning with traditional photogrammetric methods, with a particular emphasis on satellite and historical imagery analysis. The research spans from fundamental algorithm development (like SparseSat-NeRF and Pointless Global Bundle Adjustment) to practical applications in earth sciences (landslide, earthquake, and glacier volume mapping). 2022 EuroSDR PhD Award for Corona satellite imagery processing Best Paper Award for BRDF-NeRF research Outstanding Reviewer at CVPR 2025 Rupnik actively contributes to the academic community through editorial and leadership roles, including serving as Editor-in-Chief of the French Journal for Photogrammetry and Remote Sensing (2021-2025), Advisory Board Member of the International Journal of Photogrammetry and Remote Sensing (2024-present), and Co-chair of the ISPRS Working Group on Image Orientation and Sensor Fusion (2022-2026). She regularly teaches at Université Paris Cité & ENSG (~30 hours/year since 2015) and has conducted numerous workshops worldwide on photogrammetry with historical images and MicMac software.

Yannick BlandinView profile

Professor

• Motor Learning
• Sensorimotor Integration
• Observational Learning
• +3 more

Professor Yannick Blandin is affiliated with the University of Poitiers (Faculty of Sports Sciences) and the Center for Research on Cognition and Learning (CeRCA) . His work focuses on motor learning mechanisms, observational learning, mental imagery, and feedback systems in sensorimotor integration. Ph.D. in Physical Activity Sciences (University of Montreal, 1994, directed by Dr. Luc Proteau) Current research themes: specificity of practice, eye movement analysis, point-light display applications His publications reveal trends in sensorimotor coding , motor sequence acquisition , and visual feedback optimization , often intersecting neuroscience , psychology , and sports rehabilitation . He has developed the PLAViMoP platform for visualizing and modifying motor sequences. International collaborations include Dr. Stefan Panzer (Saarland University) and Dr. Charles Shea (Texas A&M). His research spans applications in stroke rehabilitation , athletic training , and cognitive-motor interfaces .

Tony LELIEVREView profile

Professor

• Applied Mathematics
• Multiscale Modeling
• Molecular Simulation
• +7 more

Tony Lelièvre is a Professor of Applied Mathematics at the Ecole Nationale des Ponts et Chaussées, part of the Institut Polytechnique de Paris. He holds a PhD (2004) and Habilitation (2009), specializing in multiscale modeling, molecular simulation, and stochastic processes. His research focuses on free energy computations, numerical analysis of complex fluids, and computational statistical physics. He co-authored two books and over 100 papers, receiving significant awards like the ERC Consolidator Grant (2013-2019) and the Grand prix Alcan. Lelièvre has organized numerous international workshops and serves on editorial boards of journals like ESAIM:M2AN and SIAM/ASA Journal of Uncertainty Quantification. His work bridges applied mathematics, numerical analysis, and computational science with applications in materials science and industrial fluid dynamics. Education: PhD in Applied Mathematics, 2004 Habilitation à Diriger des Recherches, 2009 Research Interests: Multiscale modeling of complex fluids Molecular dynamics and free energy calculations Stochastic methods for metastable systems Numerical analysis of PDEs and SDEs Computational statistical physics Recent Contributions: His work on adaptive biasing force methods and hybrid Monte Carlo techniques has advanced the simulation of rare events and free energy landscapes. He contributed to variance reduction techniques in molecular simulations and mathematical analysis of parallel replica algorithms. Awards: ERC Consolidator Grant (2013-2019), Prix CS 2002, Grand prix Alcan (2010), Ordway Visiting Professorship (2012-2013), and several teaching awards. Professional Activities: Organized major conferences like CEMRACS 2013 and IPAM Long Program on Energy Landscapes (2017). Co-edits journals and authored influential textbooks on magnetohydrodynamics and free energy computations.

Mathieu PicardeauView profile

Professor

• Bacteriology
• Leptospirosis
• Genomics
• +7 more

Mathieu Picardeau is Head of the Biology of Spirochetes Unit at the Institut Pasteur in Paris, France. The unit serves as the French National Reference Center (NRC) for Leptospirosis and a WHO Collaborating Center for Research and Reference on Leptospirosis. He leads research on the genomics, epidemiology, and diagnostics of Leptospira , a significant zoonotic pathogen affecting tropical and underserved populations globally. His research focuses on: Whole-genome sequencing and cgMLST genotyping of Leptospira strains Development of improved diagnostic tools, including ELISA and point-of-care tests Characterization of Leptospira biodiversity and discovery of novel species Epidemiological surveillance in France and overseas territories Application of targeted genome capture to overcome cultivation challenges His recent publications reveal strong trends in molecular epidemiology, diagnostic validation, and global pathogen surveillance. He contributes to international taxonomic efforts and One Health approaches, with studies spanning human, animal, and environmental health across multiple continents. His work is central to improving healthcare outcomes for neglected tropical diseases. Scientific contributions and recognitions include: Leadership of the French NRC and WHO Collaborating Center for Leptospirosis Development of the publicly accessible cgMLST database at https://bigsdb.pasteur.fr/leptospira/ Principal investigator on multiple national and international research projects Active participation in the International Committee on Systematics of Prokaryotes As a principal investigator, Dr. Picardeau mentors junior scientists and collaborates extensively with teams in endemic regions such as Laos, Costa Rica, Algeria, and French Guiana. His research is supported by institutional funding and international collaborations. The Biology of Spirochetes Unit maintains a large, curated collection of Leptospira strains and plays a key role in public health response and outbreak investigation. Future work is expected to focus on real-time genomic surveillance, vaccine development, and expanding diagnostic accessibility in low-resource settings.

Jean-Claude ManuguerraView profile

Researcher

• Virology
• Bacteriology
• Pathogen Genomics
• +7 more

Jean-Claude Manuguerra is a senior researcher and the Head of Structure at the Laboratory for Urgent Response to Biological Threats (CIBU), part of the Environment and Infectious Risks Unit at the Institut Pasteur in Paris, France. The CIBU is a 24/7 operational unit dedicated to responding to biological emergencies, including epidemics, accidents, and potential bioterrorism events. Manuguerra leads critical activities in the rapid detection and identification of high-consequence pathogens such as Ebola, Marburg, Lassa, mpox, and SARS-CoV-2. His primary research interests lie in the fields of Virology , Bacteriology , and Pathogen Genomics . His work focuses on developing and applying cutting-edge diagnostic techniques, including rapid molecular assays (like LAMP), digital PCR, and next-generation sequencing (particularly Oxford Nanopore metagenomics), for the detection of known and unknown pathogens. A significant aspect of his research involves pathogen inactivation methods and the genomic characterization of emerging infectious agents to understand their evolution and transmission. The recent publications attributed to him reflect a strong trend in emerging viral threats. His work spans from fundamental research on the attenuation of Yersinia pestis virulence across pandemics to applied public health research on mpox outbreaks, diagnostics, and vaccine-induced immunity. His studies on SARS-CoV-2 detection in low-viral-load patients also highlight his commitment to improving diagnostic sensitivity. Manuguerra is the Principal Investigator for the DEFERM project, which focuses on decontamination measures following the release of pathogenic microorganisms. His leadership role in CIBU places him at the forefront of France's national and international response to biological threats, advising on microbiological issues and managing high-security laboratory operations.

Priyanka MajumderView profile

Assistant Professor

• Applied Statistics
• Probability
• Statistical Inference
• +8 more

Dr. Priyanka Majumder serves as Assistant Professor Grade I in Data Science at the Indian Institute of Science Education and Research (IISER) Thiruvananthapuram since August 2022, following a Post-Doctoral Fellowship at IIT Bombay (2020-2022). She is affiliated with the School of Data Science and teaches core courses including Survival Analysis and Mathematical Statistics. Her academic credentials include: PhD in Mathematics (2020) from Indian Institute of Engineering Science and Technology, Shibpur, with thesis "Some Contributions to Probabilistic and Inferential Aspects of Reliability Theory" M.Sc. in Applied Mathematics (2012-2014) from same institution B.Sc. (Hons.) in Mathematics (2009-2012) from Scottish Church College, Kolkata Majumder's research integrates Applied Statistics and Probability Theory , specializing in Statistical Inference for Cluster Randomized Trials , Longitudinal Study Design , and Reliability Theory . Her work develops novel methodologies for Survival Analysis, Maintenance Policies, and Stochastic Orderings with applications in clinical trial optimization and healthcare analytics. Current projects focus on sample size determination in multilevel clustered studies. Her 14 publications (2018-2025) demonstrate evolving expertise from foundational stochastic orders to cutting-edge cluster trial methodology. Recent work emphasizes longitudinal cluster randomized trials (2022-2025), while earlier research established contributions to reliability theory and mixture distributions. Key journals include Statistics in Medicine , Naval Research Logistics , and Statistical Papers . Major recognitions: SERB Start-up Research Grant (2023) for cluster trial methodology Consecutive teaching awards at IISER TVM (2022-2024) DST-INSPIRE Fellowship (2015-2020) and Scholarship (2009-2014) CSIR-JRF qualification She mentors 3 PhD candidates and 5 Master's students in statistical methodology, with current projects on clinical trial design and reliability systems. Her SERB-funded project supports a JRF position for sample size determination research. She actively recruits graduate students through IISER TVM's PhD program and direct fellowship routes. Majumder leads a research group within IISER TVM's School of Data Science focused on statistical methods for healthcare applications. Her team collaborates with biostatisticians on clinical trial design and participates in international networks including the International Indian Statistical Association.

Jose PicheralView profile

Researcher

• Signal Processing
• Source Localization
• Spectral Analysis
• +3 more

Jose Picheral is an active researcher at the Signals and Systems Laboratory specializing in signal processing with expertise in source localization, spectral analysis, and vibration analysis. His work bridges theoretical methodologies with practical applications in aerospace engineering, acoustics, and mechanical systems monitoring. His primary research interests include: Signal Processing Source Localization Spectral Analysis Vibration Analysis Acoustic Imaging Array Signal Processing Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on sparse signal processing techniques applied to aircraft engine vibration monitoring through tip-timing analysis, high-resolution acoustic imaging methods, and non-uniform antenna array processing. Key trends include the development of OMP-based spectral analysis for blade vibration, manifold learning approaches for impulse response reconstruction, and robust super-resolution techniques for correlated source localization in noisy environments. No scientific awards were documented in the provided source material. The text contains no information regarding student advising activities or research grant funding. He operates within the Signals and Systems Laboratory framework, which concentrates on advanced signal processing solutions for engineering challenges, particularly in aerospace vibration analysis and acoustic source mapping through innovative sparse recovery and manifold-based methodologies.

Talel AbdessalemView profile

Professor

• Database Systems
• Data Mining
• Machine Learning
• +4 more

Talel Abdessalem is a Professor at Télécom Paris , where he has held leadership roles including Director of LTCI Research Laboratory (since 2017) and Dean of Research (since 2018). He currently serves as Deputy Vice-President for Research at Institut Polytechnique de Paris . PhD in Computer Science from Paris-Dauphine University Habilitation (HDR) from UPMC-Sorbonne University His research spans large-scale data management , recommender systems , social network analysis , and uncertain data modeling . He has participated in numerous national (ANR, FEDER) and European (FP7) research projects, with recent work focusing on stream-based learning , graph analytics , and privacy-preserving systems . His publications include diverse contributions to directed graph centrality algorithms (2021), stream recommender frameworks (River, Scikit-Multiflow), and geospatial recommendation models (ALGeoSPF). He has supervised 14 PhD students and collaborates with researchers in France, Brazil, and Indonesia. Co-leads DIG (Data, Intelligence and Graphs) research team Director of LTCI (Information Processing and Communication) laboratory

• Microfluidics
• Magnetic Particle Manipulation
• Cellular Engineering
• +3 more

Simon Dumas is a Postdoctoral Researcher affiliated with the Institut Curie in Paris, France. He is associated with the research team Macromolecules and Microsystems in Biology and Medicine (MMBM) under the Cell Physics and Cancer (UMR168) unit. His work focuses on the intersection of microfluidics, magnetic manipulation, and biomedical applications. His research interests include: Microfluidic device design for biomedical diagnostics Magnetic particle modeling and manipulation Droplet-based assays for single-cell and multiomic analysis 3D tissue engineering and cellular alignment Nanoparticle coatings for genomic targeting Tumor-on-a-chip modeling for precision therapy Simon has published extensively on magnetic microtools, including innovations in Magnetic Microtweezers for bioseparation and Multiomic Droplet-Based Assays for ultra-low input samples. His work spans theoretical modeling, device fabrication, and biological applications, particularly in cancer research and cellular engineering. Recent publications highlight advancements in: Remote magnetic actuation of 3D cellular structures Continuum modeling of particle flows in microfluidic suspensions Hybrid in silico and tumor-on-a-chip protein behavior analysis VEGF-functionalized beads for angiogenic disease treatment

• Image Analysis
• Statistical Methods
• Determinantal Point Processes
• +6 more

Agnes Desolneux holds dual roles as a Research Professor (CNRS Directrice de recherche) at the Centre Borelli and a Professor attached to the Department of Mathematics at ENS Paris-Saclay. Her affiliations include the Centre Borelli (ex-CMLA) and CNRS laboratories, focusing on advanced interdisciplinary research in mathematical imaging and stochastic methods. Education and Background: PhD in Applied Mathematics (2000), ENS Cachan Habilitation à Diriger des Recherches (HDR) in Applied Mathematics (2010), Université Paris Descartes Research Interests: Her work combines statistical analysis, stochastic processes, and mathematical modeling to address challenges in image analysis. Key areas include: Statistical methods ( a contrario framework, maximum entropy models) Image restoration and texture synthesis Determinantal point processes (DPP) and optimal transport Geometric analysis of random fields (curvature, Euler characteristics) Perceptual modeling of Gestalt principles Recent Contributions: Her research explores cutting-edge topics such as Gromov-Wasserstein distances in Gaussian mixture models, anisotropy analysis of random fields, and federated learning techniques. Her methodologies bridge theoretical probability and applied image processing. Awards and Recognition: No specific awards listed, but her extensive publication record in top journals like SIAM Journal on Imaging Sciences and Annals of Applied Probability highlights her scholarly impact. Grants and Collaborations: Active in projects like StoRI (Stochastic Recipes in Imaging) and the Master MVA program, emphasizing interdisciplinary training in mathematical data science. Her work often involves collaborations with institutions like the EDMH doctoral school and the Université Paris-Saclay. Labs and Teams: Leads research within the Centre Borelli, a CNRS-ENS Paris-Saclay hub for mathematics and computer science, fostering innovation in imaging and stochastic modeling.