Intellectual curiosity and human creativity are some of the most important qualities a scientist should have in order to understand the world mysteries and solve the toughest scientific challenges.

Anass is a Ph.D. candidate in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in the Biostatistics group.

Education and Early Career

Anass is a Data Scientist by training. He holds a M.Eng. from the National Institute of Applied Sciences of Rouen Normandie (France) in Information Systems Architecture as well as a M.Sc. from Normandie University (France) in Data Science and Engineering. 

During the Spring of 2019, Anass joined professor Ombao's BIOSTATS research group as a visiting student. He is currently a Ph.D. candidate in the statistics program.

In recent years, Anass has dedicated his expertise to a range of complex inference problems. His work primarily encompasses the development of online estimation techniques for time-varying Vector Autoregressive (tvVAR) models, enhancing the accuracy and efficiency of real-time data analysis. Additionally, Anass has made significant contributions to the field of neuroscience, specifically in the topological analysis of brain networks. This involves applying advanced statistical methods to unravel the intricate connections within the brain. Beyond research, Anass is also actively involved in academia, teaching Artificial Intelligence and Machine Learning at various levels, where he imparts his knowledge and insights to aspiring students and professionals in the field.

Research Interest

His research spans a diverse array of areas within neuroscience and data analysis. He is deeply involved in exploring brain signals and employing Generative Adversarial Networks (GANs) for simulating brain activities. His work in Topological Data Analysis focuses on revealing hidden structures in complex data sets. Additionally, Anass delves into the realms of causality and topological ranking, examining causal relationships in brain networks. He also has a keen interest in time series analysis, particularly as it pertains to the dynamic nature of neural signals.