I am a computer scientist, a data scientist, and an interdisciplinary researcher. My research primarily focuses on the effective use of Artificial Intelligence (Machine Learning and Deep Neural Networks) to provide physicists with a better understanding of the Sun and the physical processes driving its activities. One of the main concerns in this area is to achieve a reliable forecast of the extreme space-weather events of which the Sun is the main creating source, and can have devastating economic and collateral impacts on mankind. The magnitude of such economic impacts have been estimated by the National Research Council in 2008 to be $1-2 trillion for the United States, during the first year of the occurrence alone. The severity of the consequences was recognized in 2016, and reiterated in 2019, by the White House, with executive orders that emphasized preparation for and minimization of the potential impacts.

As a data scientist, my expertise is to use cutting-edge cyberinfrastructure innovations in Data Mining and Machine Learning to extract human-understandable knowledge from a deluge of complex sensory data, and make the decision making a data-informed process. As a computer scientist, I design and deploy computer vision algorithms to automate the detection, identification, and tracking of solar events. Due to the existence of extremely large collections of images— true Big Data — manual analysis of such data is simply infeasible. I also investigate the effectiveness of the traditional evaluation methodologies, and engineer appropriate measures to address the new challenges that come with processing big data for space-weather analytics.