Université Gustave Eiffel
Probabilités et statistiques
I am an Assistant Professor (Maître de Conférences) in Statistics at Université Gustave-Eiffel, and a member of LAMA (Laboratoire d’analyse et de mathématiques appliquées).
We study the inference of a planted dense component in a sparse random graph from a single spreading process. The graph has an Erdős-Rényi background with edge probability pn and contains a planted dense component of size nα, with α>1/2, whose internal edge density ξ>0 is constant. The edge set is unobserved; the…
Despite its ubiquity, clustering lacks a universally accepted definition of what is a cluster. Kleinberg's Impossibility Theorem formalizes this difficulty by showing that no flat clustering method can simultaneously satisfy three natural axioms: scale invariance, richness, and consistency. In this paper, we ask whether this impossibility persists when the output is a hierarchy…
Markov chains are fundamental models for stochastic dynamics, with applications in a wide range of areas such as population dynamics, queueing systems, reinforcement learning, and Monte Carlo methods. Estimating the transition matrix and stationary distribution from observed sample paths is a core statistical challenge, particularly when multiple independent trajectories are available. While classical…
Hierarchical clustering seeks to uncover nested structures in data by constructing a tree of clusters, where deeper levels reveal finer-grained relationships. Traditional methods, including linkage approaches, face three major limitations: (i) they always return a hierarchy, even if none exists, (ii) they are restricted to binary trees, even if the true hierarchy is…
Hierarchical clustering of networks consists in finding a tree of communities, such that lower levels of the hierarchy reveal finer-grained community structures. There are two main classes of algorithms tackling this problem. Divisive (top-down) algorithms recursively partition the nodes into two communities, until a stopping rule indicates that no further split is needed.
Centrality indices, such as closeness and eccentricity, are key to identifying influential nodes within a network, with applications ranging from social and biological networks to communication and transportation systems. However, computing these indices for every node in large graphs is computationally prohibitive due to the need for solving the All-Pairs Shortest Path (APSP)…