Profiles

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.

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.

Professor 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 the recovery of tractability while being principled, practical, and scalable.

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

Dr. Baptiste Py received his PhD degree from the Hong Kong University of Science and Technology, China, where he used theoretical and applied Mathematics to analyse electrochemical impedance spectroscopy data measured on a variety of electrochemical devices, including batteries, fuel cells, and supercapacitors. Baptiste focused on the theoretical understanding and practical deconvolution of distributions of timescales to augment the impedance measurements and advance the analysis of the data.

During his PhD, Baptiste published 17 peer-reviewed articles, including six as the first author and eight as the second author, and he attended six international conferences to give four talks and present two posters. Additionally, he actively collaborated with research groups around the world, including China, Singapore, Russia, India, Canada, Australia, Germany, and Italy.

Baptiste is naturally interested in Mathematics, both theoretical and applied, to tackle rea-life problems. For instance, he focused in his PhD research on an ill-posed inverse problem in electrochemistry whose solutions can provide direct insights into the underlying physico-chemical processes occurring in the electrochemical device under scrutiny. On the one hand, he analysed the theoretical underpinnings of these distributions using tempered distributions. On the other hand, he explained how to improve the deconvolution of these distributions via various methods, including Gaussian processes, maximum entropy, and deep neural networks. Furthermore, he analysed impedance data collected on batteries, fuel cells, supercapacitors, solar cells, MXenes-based electrodes, glucose electrooxidation, and DNA sensors.

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.

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

Mattia Rosso got his Bachelor's degree at Politecnico di Torino in Computer Engineering in 2021. He went on to pursue a double-degree Master's program in Data Science, split between EURECOM in Sophia-Antipolis, France, and Politecnico di Torino's AI & Data Analytics track.

During his master's studies, he gained early research experience at EURECOM in the summer of 2023, investigating Gaussian Processes and Bayesian methods for uncovering conditional independence structure in input features. He then completed his master's thesis internship at ALTEN Labs in Sèvres, France, where he applied Bayesian particle filters theory to sensor fusion problems in autonomous driving systems, merging occupancy grid data from multiple vehicles and roadside sources.

Since September 2024, he has been a PhD student in the Statistics department at KAUST, where he works within the Bayesian Deep Learning group on the theoretical foundations of generalization in large-scale neural networks.

Hans Montcho is a Postdoctoral Fellow researcher in the  Bayesian Deep Learning group.  He holds a PhD in Statistics from KAUST and an MSc in Statistics from  the Federal University of São Carlos and the University of São Paulo. Before that,  he also worked as a data scientist for a global credit bureau.

His current research focuses on developing scalable probabilistic methods for complex statistical and machine-learning problems.

His research focuses on  Bayesian computation, spatial statistics, latent Gaussian models, model assessment,  uncertainty quantification and more recently on Bayesian deep learning.