Adjunct Professor, Department of Machine Learning, MBZUAI
Dr Eric Moulines is an Adjunct Professor in the Machine Learning Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
Biography
In 1990, Moulines joined the Signal and Image Processing Department at Télécom ParisTech, where he was appointed full professor in 1996. In 2015, he moved to the Center for Applied Mathematics at Ecole Polytechnique, where he is currently professor of statistics. His areas of expertise include computational statistics (Monte Carlo simulations, stochastic optimization), probabilistic machine learning, statistical signal processing, and time series analysis (sequential Monte Carlo methods, nonlinear filtering). He is a EURASIP and IMS Fellow.
His current research themes aim to solve the challenges related to the need for rapid analysis of computational statistics created by ever-larger datasets. The four themes include: (1) Understanding and optimizing principled approximate inference in complex statistical models; (2) Develop principled statistical approaches for massive data sets and high-dimensional models; (3) Federated and distributed computational statistics; and (4) Theory and methodology for optimizing high-dimensional algorithms
×
About Eric Moulines
Dr Eric Moulines is an Adjunct Professor in the Machine Learning Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
Biography
In 1990, Moulines joined the Signal and Image Processing Department at Télécom ParisTech, where he was appointed full professor in 1996. In 2015, he moved to the Center for Applied Mathematics at Ecole Polytechnique, where he is currently professor of statistics. His areas of expertise include computational statistics (Monte Carlo simulations, stochastic optimization), probabilistic machine learning, statistical signal processing, and time series analysis (sequential Monte Carlo methods, nonlinear filtering). He is a EURASIP and IMS Fellow.
His current research themes aim to solve the challenges related to the need for rapid analysis of computational statistics created by ever-larger datasets. The four themes include: (1) Understanding and optimizing principled approximate inference in complex statistical models; (2) Develop principled statistical approaches for massive data sets and high-dimensional models; (3) Federated and distributed computational statistics; and (4) Theory and methodology for optimizing high-dimensional algorithms
| Present | Adjunct Professor, Mohamed bin Zayed University of Artificial Intelligence ‐ Department of Machine Learning | |
|
|
||
Disciplines
Research Interests
Honors and Awards
- Best paper award from the IEEE Signal Processing Society (for publications in IEEE Trans. On Signal Processing) in 1997 and 2006
- Silver Medal of the Centre National de Recherche Scientifique in 2010
- Orange Prize of the French Academy of Sciences in 2011
- Fellow of the IMS 2016
- EURASIP technical achievement award 2020
- Elected to the French Academy of Sciences in 2017
This author has not uploaded works yet.