Safwan ALJBAAE

PhD in Astronomy from the Paris Observatory (2013) and a Bachelor's degree in Mathematics from the University of Damascus, Syria (2004). I have extensive experience in astronomy and space dynamics, particularly in asteroid families and spacecraft control near irregularly shaped celestial bodies. I am proficient in various scientific languages in an Ubuntu environment, including FORTRAN, C, MATLAB, LATEX, and Python, as well as advanced Machine Learning and Deep Learning techniques applied in several areas such as automation and dynamic modeling. My goal is to expand my expertise to include different applications of Machine Learning, exploring new technological frontiers.Between February 2014 and 2024, I worked on 53 different publications in Qualis A1 and A2 indexed journals, with an average of 5.3 articles per year. My recent research is divided into three main areas:1. **Asteroid Dynamics**: I investigated the origin and evolution of asteroid families, measuring the age of various families and simulating the evolution of each member under the effects of Yarkovsky and YORP forces. I used Machine Learning algorithms to identify patterns in orbital distributions, contributing to a more accurate classification of these families.2. **Modeling the Gravitational Field of Small Bodies**: I modeled the external gravitational field of celestial bodies, using techniques such as the Mascon structure with a shaped polyhedral source. These studies were applied to the dynamics of spacecraft around complex systems like Lutetia, Bennu, Sylvia, Antiope, and Apophis, optimizing navigation and orbital control through Machine Learning techniques for automation.3. **Machine Learning Applications in Astronomy and Other Areas**: I developed new Machine Learning methodologies applied to the dynamics of small bodies, including the use of Deep Learning to identify asteroid families and study resonant dynamics in the Solar System. Time-series analyses were used to predict orbital behaviors and detect chaos indicators, techniques that were also applied in trajectory automation and optimization of complex systems.My interdisciplinary approach aims to expand the application of Machine Learning, not only in astronomy but also in other scientific and engineering fields, exploring new technological possibilities and contributing to advances in automation, dynamic prediction, and space exploration. (Source: Lattes Curriculum)