My research is about developing methods to discover hidden structure (e.g. clusters, trees, manifolds) in complex data (e.g. large dynamic networks, point processes, high-dimensional data).
Interests include: clustering, anomaly detection, manifold estimation, topological data analysis, dimension reduction, high-dimensional statistics, (graph) embedding, dynamic networks, spectral methods, bias/variance trade-offs, statistical/computational trade-offs, model selection, nonparametric statistics, exploratory data analysis, representation learning and machine learning.
Applications in biosciences, healthcare, (cyber-)security, societal resilience, environmental protection and more. For example, unfolded spectral embedding is used for anti-corruption.
Current and former post-docs and PhD students (group photo):