Pierre ALQUIER

Pierre ALQUIER is a Professor at ESSEC Business School (Singapore) since 2023, specializing in statistical learning and machine learning. Previously, he held professorships at ENSAE Paris (2014–2019) and the University of Dublin (2012–2014). He earned his PhD in Mathematical Statistics from Pierre and Marie Curie University in 2006, with a focus on advanced statistical methodologies. His research centers on Bayesian methods, PAC-Bayes bounds, high-dimensional data analysis, and robust estimation, with applications in quantum computing and time series. He has authored over 60 peer-reviewed articles, including influential works on kernel mean embeddings and meta-learning. Alquier has received the 2019 Best Paper Award at the Asian Conference on Machine Learning. He actively contributes to academic leadership, serving as an associate editor for leading journals like the Journal of Machine Learning Research and organizing international workshops. His educational contributions include co-supervising multiple doctoral theses on topics like robust Bayesian inference and non-negative matrix factorization.

• Education:
  • PhD in Mathematical Statistics (2006), Pierre and Marie Curie University
  • MSc in Probability Theory and Statistics (2003), Pierre and Marie Curie University
  • Diploma in Statistician-Economist (2003), ENSAE
• Research Focus: Machine learning theory, PAC-Bayes bounds, Bayesian computation, high-dimensional statistics, quantum tomography, and time series forecasting.
• Grants & Activities: Member of key academic societies (IMS, SFdS), reviewer for top conferences (NeurIPS, ICML), and organizer of workshops on approximate Bayesian inference and high-dimensional data analysis.

His recent work emphasizes robust regression, meta-learning, and the theoretical foundations of deep learning, often addressing challenges in dependent data and model misspecification. He has developed R packages like regMMD for robust statistical estimation.