Nicolas Chopin

Nicolas Chopin is a Professor of Data Sciences at ENSAE, Institut Polytechnique de Paris. He joined ENSAE in 2006 after serving as a lecturer at the University of Bristol (2003-2006). He holds a PhD from Université Paris VI (2003) and an HDR (habilitation) earned in 2010.

His research centers on Bayesian computation methodologies, including:

• Sequential Monte Carlo (particle filters)
• Markov chain Monte Carlo
• Variational inference
• Probabilistic Machine Learning

He develops computational frameworks for complex statistical inference problems.

Analysis of his recent publications (2022-2025) reveals strong emphasis on: Monte Carlo innovations (e.g., waste-free SMC, quasi-Monte Carlo), scalable Bayesian modeling, debiasing techniques for sequential inference, and applications in optimization/bandit problems. Theoretical rigor combined with computational efficiency is a consistent theme.

Awards:

• Savage Award for Best Doctoral Dissertation in Bayesian Statistics (2002)