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Maxime Fairon is an Assistant Professor (Maître de Conférences) at the Institut de Mathématiques de Bourgogne, Université Bourgogne Europe, affiliated with CNRS. He joined the institute in December 2023 and is a member of the Mathematics-Physics research team. His office is located in the Mirande Building (Office 407). Research interests include Poisson algebras, integrable systems, and non-commutative geometry, with applications to mathematical physics and representation theory. Key focus areas are: Double Poisson brackets and their algebraic/geometric generalizations Connections between quiver varieties and integrable particle systems (e.g., Ruijsenaars-Schneider models) Vertex algebras and non-commutative Hamiltonian structures His publications (2017–2025) demonstrate consistent work on unifying Poisson geometry with dynamical systems, featuring innovations in double multiplicative structures, quiver-based integrability, and morphism theories. Recent articles emphasize classification problems and geometric reductions. Current student supervision includes Master's projects on Poisson vertex algebras (A. Fotiadis, 2024) and degenerate integrable systems (J. Deshan, 2025). He coordinates graduate courses in geometry and algebra, and co-organizes workshops like the 2025 Workshop on Non-Associative Algebras. No awards or grants are mentioned in available materials.

• Poisson algebras
• Integrable systems
• Non-commutative geometry
• +3 more

Virginie Ehrlacher Galland is a Professor at CERMICS (Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique) within École des Ponts ParisTech. Her expertise lies in applied mathematics, numerical analysis, and computational physics, with a focus on multiscale problems, quantum chemistry, and uncertainty quantification. She holds a PhD from CERMICS (2012) and a Habilitation (2020) from Université Paris-Dauphine. Her research interests include cross-diffusion systems, reduced basis methods, and optimal transport applications. Key contributions involve numerical methods for electronic structure calculations, homogenization techniques, and adaptive algorithms. She leads the ERC Starting Grant HighLEAP (2023-2028) and contributes to major projects like the ERC Synergy project EMC². Awards: Irène Joliot-Curie Prize (2023), Chevalier de l’Ordre National du Mérite (2025). Grants/Projects: ERC Starting Grant HighLEAP (PI), ERC Synergy EMC² (Member), ANR JCJC COMODO (PI). Her work bridges theoretical analysis and computational methods, addressing challenges in materials science, fluid dynamics, and machine learning applications.

• Quantum Chemistry
• Uncertainty Quantification
• Numerical Analysis
• +5 more

Giacomo Nardi is an Associate Professor (Maître de Conférences) at INSA Rouen starting September 2025, holding a PhD in Mathematics from Sorbonne University. His research bridges mathematical analysis and biomedical image processing through extensive collaborations with the Institut Pasteur in Paris, focusing on developing advanced mathematical frameworks for biological imaging applications. His educational background includes: PhD in Mathematics, Sorbonne University (Pierre and Marie Curie), thesis: "On a characterization of the relaxation of a generalized Willmore functional" under supervisors S. Masnou and E. Mascolo. Nardi's work spans Biomedical Image Processing and Mathematical Analysis , evolving from foundational contributions in calculus of variations (Willmore functionals, BV functions) to cutting-edge biomedical applications. His current research employs topological data analysis and geometric methods to solve complex biological imaging problems including 3D segmentation of hematopoietic stem cells, receptor dynamics analysis, and retinal degeneration quantification. This interdisciplinary approach integrates deep mathematical theory with practical biological challenges through close partnerships with biologists at the Institut Pasteur. Analysis of his publication trajectory reveals a decisive shift toward biomedical applications since 2016, with recent work (2022-2025) dominating in high-impact imaging journals and conferences. His research consistently applies geometric measure theory and functional analysis to innovate in cellular dynamics modeling and 3D image segmentation, demonstrating exceptional versatility across pure mathematics and translational biomedical research. Nardi actively mentors the next generation of researchers: Postdoctoral supervision: Somoballi Ghoshal (2024-present, Institut Pasteur) on persistent homology-based 3D cell segmentation in cardiac tissue. PhD supervision: Benjamin Dorra (defense March 5, 2025, Medetia/Institut Pasteur) on AI-driven histopathology for ciliopathy models, resulting in ISBI 2024 award-winning publications. He co-develops the open-source Fshapestk (Functional shapes toolkit) and collaborates with J.-C. Olivo-Marin's Quantitative Image Analysis Unit at the Institut Pasteur, maintaining strong ties between theoretical mathematics and experimental biology through ongoing joint projects in cellular dynamics and tissue imaging.

• Biomedical Image Processing
• Mathematical Analysis
• Computational Geometry
• +3 more

Giuseppe Dito is a Lecturer with HDR (Habilitation à Diriger des Recherches) at the Institut de Mathématiques de Bourgogne (IMB), a joint research unit (UMR 5584 CNRS) affiliated with the University of Burgundy in Dijon, France. His office is located in the Mirande Building, Wing A, Office 327. Institut de Mathématiques de Bourgogne (IMB), UMR 5584 CNRS University of Burgundy, France Mathematics-Physics research team Giuseppe Dito specializes in Mathematical Physics, with research interests spanning Deformation Quantization, Noncommutative Geometry, Poisson Geometry, and Quantum Mechanics. His work bridges theoretical physics and advanced mathematical structures, focusing on the mathematical foundations of quantum theories and their geometric interpretations. His recent publications and editorial work demonstrate a strong focus on quantization procedures, geometric structures in physics, and the mathematical analysis of quantum systems. Giuseppe Dito has served as Editor-in-Chief of "Progress in Mathematical Physics" and is a series editor for "Mathematical Physics Studies." He has also been a dedicated editor for "Letters in Mathematical Physics" since 2000. Editor-in-Chief: Progress in Mathematical Physics Series Editor: Mathematical Physics Studies Editor: Letters in Mathematical Physics (2000-present) He has organized numerous international conferences and schools that have significantly contributed to the field of mathematical physics: Thematic year on "Perspectives in Deformation Quantization and Noncommutative Geometry" at RIMS, Kyoto University (2010-2011) Multiple "Estate Quantistica" schools in Scalea, Italy (2014-2022) CIMPA School on Groups and Lie Algebras (2021) Several Afro-Franco-Brazilian Meetings on Mathematics and Physics (2013-2023)

• Mathematical Physics
• Deformation Quantization
• Noncommutative Geometry
• +3 more

Arnaud ROUSSELLE is a Lecturer at the University of Burgundy Europe, affiliated with the Institute of Mathematics of Burgundy (UMR 5584). He teaches at the IUT Dijon Auxerre, Polytech Dijon (formerly ESIREM), and UFR Sciences et Techniques. Current roles: Lecturer, IMB Scientific Mediation Correspondent, Head of PopMath Group Research focuses on Probability Theory , Stochastic Geometry , and Random Graphs , particularly in percolation models and extreme value analysis. His publications address topics like IDLA forests, random walks in random sceneries, and geometric percolation theory. He holds a doctoral degree from the University of Rouen (2014), with research on random walks on random geometric graphs generated by point processes. Professional activities include membership in the IMB Council, coordination of internships in BUT GEA, and leadership in mathematical outreach initiatives like the Science Festival and PopMath group at IREM Dijon.

• Probability Theory
• Stochastic Geometry
• Random Graphs
• +4 more

Jérôme Casse is an Assistant Professor of Mathematics at the University Paris-Saclay, affiliated with the Probability and Statistics team at the Mathematics Department (IMO). He holds teaching roles at the IUT of Sceaux (School of Marketing and Management), NYU Shanghai, and Mines Nancy. His research focuses on probability theory, stochastic dynamical systems, combinatorics, and integrable systems, with particular emphasis on probabilistic cellular automata (PCA), nearest neighbour trees, and models like the 8-vertex system. Research interests span reversible stochastic systems, ergodic theory, spatial stochastic processes, and their applications in statistical mechanics. His work often explores the interplay between discrete and continuous probabilistic structures, with a focus on invariant laws, integrable models, and processes iterated ad libitum. Key contributions include studies on Poisson-Kirchhoff systems, edge correlations in lattice models, and the dynamics of random trees with catastrophes. Teaching responsibilities include calculus, probability, graph theory, and partial differential equations across institutions like IUT GEA (192h/year) and NYU Shanghai. His publications (15+ articles since 2012) reflect expertise in stochastic processes, cellular automata, and their applications in both pure and applied mathematics. Laboratory affiliations include the Laboratoire de Mathématiques d'Orsay (LMO), where he occupies office 3E3, and the IUT of Sceaux (office 401). No specific grants or awards are highlighted in the provided materials.

• Probability
• Stochastic Processes
• Combinatorics
• +3 more

Alexander Terenin is an Assistant Research Professor at Cornell University , specializing in machine learning and artificial intelligence. His work focuses on decision-making under uncertainty, Bayesian optimization, and Gaussian processes, particularly in non-Euclidean spaces. He has contributed to geometric learning, scalable Gaussian process methods, and applications in robotics, plasma science, and legal AI. His research integrates theoretical foundations with practical algorithms, emphasizing principles like the Gittins Index for optimal decision-making. Notable projects include the GeometricKernels software package for manifold learning and the Cambridge Law Corpus for legal AI. His work bridges statistics, geometry, and computer science to address challenges in autonomous systems, energy optimization, and data-driven decision-making. Recent Talks and Contributions: An Adversarial Analysis of Thompson Sampling (INFORMS Applied Probability Society 2025) Cost-aware Bayesian Optimization (NeurIPS 2024) Stochastic Poisson Surface Reconstruction (ICML 2025) Key Research Themes: Bayesian Optimization for multi-objective problems (e.g., plasma-driven energy systems) Geometric Gaussian Processes for robotics and 3D modeling Statistical guarantees for Gaussian processes on manifolds Grants and Collaborations: His work involves interdisciplinary projects with institutions like Carnegie Mellon University, ETH Zürich, and the University of Cambridge, reflecting a global network in AI and statistical learning.

• Machine Learning
• Artificial Intelligence
• Decision Theory
• +7 more

Camille Laurent-Gengoux is a Professor at the University of Lorraine, affiliated with the Faculty of Mathematics, Computer Science, and Mechanics. She is part of the Analysis and Number Theory research team within the Institut Élie Cartan de Lorraine (IECL). Her work focuses on geometric structures such as Poisson geometry, symplectic geometry, and Lie algebroids, with applications to mathematical physics and algebraic topology. Research interests include singular foliations, Dirac structures, and the interplay between geometric analysis and algebraic methods. Recent publications explore topics like holonomy groupoids, symplectic resolutions, and numerical methods in Poisson geometry. She contributes to INRIA teams SPHINX and PASTA, advancing computational and theoretical aspects of geometric mechanics. No scientific awards are explicitly listed in the provided materials. She collaborates with institutions like the Mathematical Sciences Research Institute (MSRI) and has an active research group at the IECL. Contact details include a dedicated office (UM-ARC-021) in Metz.

• Differential Geometry
• Symplectic Geometry
• Poisson Geometry
• +3 more

Charles-Edouard Bréhier is a Professor at the University of Pau and the Pays de l'Adour, specializing in numerical methods for stochastic systems. His research spans stochastic PDEs, multiscale modeling, metastable processes, and geometric numerical integration. He leads investigations into error analysis and computational efficiency for complex stochastic systems. Bréhier's work develops advanced numerical schemes for high-dimensional problems, including splitting integrators, Galerkin approximations, and structure-preserving algorithms. His recent publications focus on error bounds for SPDE discretizations, turbulence modeling, and stochastic geometric mechanics. Articles consistently demonstrate rigorous mathematical foundations for computational methods, with applications spanning fluid dynamics, plasma physics, and optimization. The research provides tools for simulating complex systems with inherent randomness across multiple temporal and spatial scales.

• Numerical Probability
• Stochastic PDEs
• Multiscale Modeling
• +2 more

Prof. Marc CASTELLA is a Lecturer at Telecom SudParis, part of the SOP (Signal and Optimization Processing) unit. His research focuses on signal processing, particularly in blind source separation, nonlinear reconstruction, and optimization techniques. He has extensively contributed to areas such as sparse signal recovery, neural networks, and tensor decomposition. His work often addresses challenges in noisy environments and nonlinear systems. Recent publications include advancements in clipped signal recovery, motor torque estimation, and global optimization methods. Though no specific awards are listed, his prolific publication record reflects his impactful contributions to signal processing and applied mathematics. He collaborates with researchers like Jean-Christophe Pesquet and Arthur Marmin on projects involving polynomial systems and neural network models. His work is applied in diverse fields from industrial electronics to data analysis.

• Signal Processing
• Blind Source Separation
• Neural Networks
• +3 more

Saikat Chatterjee is an Associate Professor in the School of Mathematics at the Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM), India. He joined IISER TVM on May 5, 2015, after holding postdoctoral fellowships at the Institut des Hautes Études Scientifiques (IHES) in France, the Institute of Mathematical Sciences in Chennai, the Harish-Chandra Research Institute in Allahabad, and the Tata Institute of Fundamental Research in Mumbai. Dr. Chatterjee received his B.Sc. in 2002 from Presidency College, Calcutta University, followed by an M.Sc. in 2004 from West Bengal University of Technology. He conducted his doctoral research at the S.N. Bose National Centre for Basic Sciences, completing his PhD in 2011 with a thesis titled "Study of path spaces, gauge theory and holonomy" under Jadavpur University. His research focuses on the intersection of differential geometry, higher category theory, and mathematical physics. He investigates how categorical structures can be used to understand geometric phenomena such as holonomy, parallel transport, and quantization. His ongoing work explores connections on categorical bundles, gerbes, Poisson geometry, and symplectic geometry, establishing deep relationships between higher categorical frameworks and geometric structures in theoretical physics. Dr. Chatterjee has presented his research at numerous international conferences including Category Theory 2013 in Sydney, Poisson 2012 in Utrecht, and workshops at ETH Zurich and IMPA Rio de Janeiro. His publication record demonstrates consistent contributions to journals like the International Journal of Geometric Methods in Modern Physics and Theory and Applications of Categories, with recent work focusing on geometric prequantization and categorical approaches to path space geometry.

• Differential geometry
• Higher category theory
• Mathematical physics
• +5 more

Aurélie Chapron is an Associate Professor at the University of Rouen Normandy, affiliated with the Faculty of Science and Technology. Her research focuses on Probability, Random Geometry, and Point Processes, particularly on Voronoi Mosaics in Riemannian Manifolds. Doctorate in Mathematics (2018) from University of Rouen and Paris Nanterre Specialized in Probability during her Master's (2012–2014) and earned Mathematics Aggregation (2016) Her work explores geometric properties of Poisson processes, with publications in International Mathematics Research Notices and Lecture Notes in Mathematics . She has presented at conferences including the Stochastic Geometry Conference (Nantes) and Saint-Flour Probability Summer School. Chapron teaches Mathematics and Statistics courses across multiple disciplines, including Linear Algebra, Calculus, and Inferential Statistics with R, at both the University of Rouen and Paris Nanterre (2015–2018).

• Probability
• Random Geometry
• Point Processes
• +3 more

Guido Carlet is a Professor at the Institut de Mathématiques de Bourgogne (Mathematics-Physics team), Université Bourgogne Europe, where he holds multiple leadership roles including Coordinator of the International Master in Mathematical Physics (Math4Phys), President of the Bureau de la Commission de Proposition des Sections 25 et 26, and member of the Conseil de l'UFR Sciences et Techniques. His office is located in the Mirande Building (Office 415) in Dijon, France. He completed his PhD in 2003 at the International School for Advanced Studies (SISSA) under Boris Dubrovin, focusing on the Extended Toda hierarchy, and a Master's degree at the University of Padova in 1998. Carlet's research explores mathematical physics with emphasis on integrable systems, Poisson geometry, and Frobenius manifolds. His work bridges differential geometry, algebraic combinatorics, and topological field theories, frequently investigating Hamiltonian structures, deformation theory, and connections to Gromov-Witten theory. Recent publications analyze homogeneous Poisson brackets, infinite-dimensional Dubrovin-Frobenius manifolds, and constrained KP hierarchies. He actively advises graduate students, with current projects including deformations of bihamiltonian hierarchies and Coxeter-Frobenius connections. As Coordinator of the EIPHI project METTHHOD (2023-2026), he leads research initiatives. Carlet co-leads the Mathematics-Physics group at IMB and organizes international workshops like the 2024 Poisson Brackets and Integrability event at Amsterdam's KdVI institute.

• Mathematical Physics
• Integrable Systems
• Poisson Geometry
• +3 more

Djalil CHAFAÏ is a University Professor of Mathematics at Université Paris-Dauphine - PSL, with dual affiliation at CEREMADE (Centre de Recherche en Mathématiques de la Décision) and DMA (Département de Mathématiques et Applications) at École normale supérieure (Paris) - PSL. He currently serves as Directeur des études du DMA (2021-2026) and Directeur scientifique du RNBM (2021-2025). His extensive research spans multiple areas of probability theory, mathematical physics, and applied mathematics. CHAFAÏ's research interests center around geometric and probabilistic functional analysis, random matrices, random graphs, free probability, and high-dimensional phenomena. His work connects mathematical theory with applications in biology, physics, and data science. He has made significant contributions to understanding cutoff phenomena in high-dimensional diffusions, Riesz energy problems, and Coulomb gases. His research often combines theoretical analysis with visual illustrations created using computational tools like Octave, Python, and Julia. Analysis of his recent publications reveals a strong focus on cutoff phenomena in various stochastic processes, equilibrium measures in potential theory, and the mathematical properties of random matrix ensembles. His work demonstrates a consistent pattern of bridging abstract mathematical concepts with concrete physical phenomena, particularly in statistical physics. The interdisciplinary nature of his research is evident in the diverse applications ranging from mathematical biology to data science. CHAFAÏ has successfully advised numerous doctoral students whose work continues to influence the field. His current doctoral students include Samuel Chan-ashing, Rémi Bonnin, and Kewei Pan, while his former students have gone on to positions at prestigious institutions worldwide. He is actively involved in the mathematical community through organizing conferences, seminars, and workshops, including the Matrices Et Graphes Aléatoires (MEGA) project and the Conviviality project funded by ANR.

• Geometric and probabilistic functional analysis
• Random matrices, random graphs, and random tensors
• Free probability and high dimensional phenomena
• +10 more

Laurent Decreusefond is a Professor of Mathematics at Télécom Paris , affiliated with the Information Processing and Communication Laboratory (LTCI) and the Network, Mobility and Services (RMS) research team. His work bridges probability theory and telecommunications, focusing on stochastic modeling, Malliavin calculus, and applications to wireless network performance. Research Interests: Probability, stochastic processes, determinantal point processes, simplicial homology for network coverage, and Malliavin calculus in telecommunications. Publications: Recent articles address UAV swarm deployment, Stein's method for stable processes, and optimal transport in wireless simulation. His work connects abstract mathematics to practical network optimization. Labs & Teams: Active in LTCI and RMS, he contributes to stochastic geometry and network modeling. No explicit awards or student advisement details provided in the data.

• Probability and Stochastic Modeling
• Malliavin Calculus
• Telecommunications Network Analysis
• +3 more