Profiles

Principal Investigators

Biography

Professor Maurizio Filippone received his Master’s in Physics and a Ph.D. in Computer Science from the University of Genova, Italy, in 2004 and 2008, respectively. During his Ph.D. studies in 2007, Filippone spent a year as a research scholar at George Mason University, U.S.

From 2008 to 2011, he was a research associate at the University of Sheffield, U.K. (2008 to 2009), the University of Glasgow, U.K. (2010), and University College London, U.K. (2011). In 2011, Filippone took up a lecturer position at the University of Glasgow, which he left in 2015 to join EURECOM, France, as an associate professor.

In 2024, Filippone joined the Statistics program at KAUST as an associate professor.

Research Interests

Professor Filippone’s primary focus is Bayesian statistics, which enables sound decision-making through uncertainty quantification in model parameters and predictions; his main interests are in models based on deep learning and Gaussian processes.

Filippone is interested in the foundations of Bayesian statistics and computational aspects related to its use in practice. More specifically, he is developing approximations that enable recover tractability while being principled, practical and scalable.

He is also interested in applications in life and environmental sciences where uncertainty matters.

Education
Doctor of Philosophy (Ph.D.)
Computer Science, University of Genoa, Italy, 2008
Master of Science (M.S.)
Physics, University of Genoa, Italy, 2004

Postdoctoral Fellows

Biography

Dr. Emmanuel Ambriz is a Ph.D. in Statistics whose research has focused on frontier challenges in copula theory, particularly in multivariate vine copula models, and their relevance to other branches of modern statistics.

Dr. Ambriz has recently joined the CEMSE Division as a postdoctoral fellow. He obtained his M.S. and Ph.D. degrees from the Centro de Investigación en Matemáticas (CIMAT), Mexico, in 2016 and 2024, respectively. From 2017 to 2022, he has worked as a Professor and Researcher at the Universidad Regional Amazónica Ikiam in Ecuador, where he has been involved in several research projects related to conservation and water resource challenges in the Ecuadorian Amazon.

In addition to his research career, Dr. Ambriz has actively collaborated as a statistical consultant with various industries, public institutions, and NGOs in Mexico and Ecuador, applying statistical methods to support data-driven decision-making across diverse sectors.

Research Interests

Emmanuel's research focuses on developing novel interpretable ordering methods for multivariate functional data, enabling improved distributional analysis and flexible nonlinear functional quantile regression.