As a researcher and data scientist, I was conducting cutting-edge research on stellar physics using data-based model fitting, inference techniques and machine learning for over 14 years. Now I am designing an end to end pipeline for detection and characterization of anomalies within a Kubernetes orchestrator. This in order to improve self-healing properties of programs running within distributed systems. I have a strong background in numerical methods, programming, and physics, as well as a PhD in physics and a Master in Plasma Physics from Université Paris Sud (Paris XI).My core competencies include applying statistical techniques such as MLE, MAP, MCMC, and ML. I also have extensive experience in inverse-problem solving, using time-dependent stellar seismic data to unravel the physics within stars and to assess their habitability conditions.I use Python, C++ and IDL for developing reliable codes for scientific simulations, data analysis, visualization, and machine learning, and I leverage high performance computing to tackle computationally demanding problems in physics. Additionally, I have supervised several PhD and Master students around the world and taught statistics, physics, and data analysis. I have successfully obtained several research grants. I am fluent in English and French, and conversational in Arabic and Japanese.My mission is to advance the scientific knowledge, to contribute to the discovery and to construct future applications and infrastructures for solving real-world problems. I am passionate about exploring the mysteries of the universe and applying data science to solve challenging problems in physics. I am also actively involved in the crypto sphere, where I have acquired certifications in Cardano Blockchain and Haskell Programming from EMURGO Academy.