Olga Klopp

Olga Klopp is a Professor of Statistics at ESSEC Business School and a member of the CREST Statistics Department. Her research focuses on nonparametric estimation, high-dimensional inference and sparsity, network models, and matrix completion. She has made significant contributions to statistical theory in low-rank modeling and missing data problems, with applications to networks, epidemiology, and machine learning.

• Nonparametric Estimation and High-Dimensional Inference: Key areas in her work, including theoretical guarantees and adaptive methods.
• Network Models and Graphons: Addressing dynamic networks, change-point detection, and graphon games with missing links.
• Matrix Completion: Pioneering work on robust and collective matrix completion with low-rank constraints.

Her recent publications highlight advancements in tensor decomposition, topic modeling via projections, and sparse network estimation. She advises PhD students in statistics and network analysis.