Researcher and Experienced Data Scientist currently working on stellar physics, and with high interest in technology: computer science, blockchain, space industry
As a researcher and data scientist, I conduct cutting-edge research on stellar physics using data-based model fitting, inference techniques and machine learning. With over 14 years of experience, 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 to better understand physical processes inside stars and to test hypotheses of stellar physics. 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 competitive research grants and observation time in astronomical facilities.
My mission is to advance the scientific knowledge, to always discover new things and topics, and to construct future applications and infrastructures for solving real-world problems. I am passionate about applying data science to solve challenging problems. I am actively involved in the blockchain world, where I have acquired certifications in Cardano Blockchain and Haskell Programming from EMURGO Academy and the Cardano Fundation.