Georgios Georgalis, Ph.D.

Data Scientist II

Georgios’ work includes different aspects of data science, with a focus on aerospace sciences and uncertainty quantification. He is working with deep learning and surrogate models of large scale hybrid rocket motor simulations. He is also working on machine learning approaches to identify flight phases using data from the aircraft on board computers. Georgios’ doctoral research included the development of a crowd-based prototype to predict future project failures using machine learning and human analytics, and he also investigated whether targeted feedback helps in preventing such failures. Georgios also has work experience in AI applications for nuclear reactors, small satellites, rocket propulsion systems, and on zero-gravity experiments.

Research Interests

Uncertainty quantification, deep learning, surrogate models, aerospace engineering